Internet DRAFT - draft-pentikousis-icn-scenarios
draft-pentikousis-icn-scenarios
ICNRG K. Pentikousis, Ed.
Internet-Draft Huawei Technologies
Intended Status: Informational B. Ohlman
Expires: January 16, 2014 Ericsson
D. Corujo
Universidade de Aveiro
G. Boggia
Politecnico di Bari
G. Tyson
Queen Mary, University of London
E. Davies
Trinity College Dublin
P. Mahadevan
PARC
S. Spirou
Intracom Telecom
A. Molinaro
UNIRC
D. Gellert
InterDigital
S. Eum
NICT
July 15, 2013
ICN Baseline Scenarios and Evaluation Methodology
draft-pentikousis-icn-scenarios-04
Abstract
This document aims at establishing a common understanding about the
evaluation of different information-centric networking (ICN)
approaches so that they can be tested and compared against each other
while showcasing their own advantages. Towards this end, we review
the ICN literature and document scenarios which have been considered
in previous performance evaluation studies. We discuss a variety of
aspects that an ICN solution can address. This includes general
aspects, such as, network efficiency, reduced complexity, increased
scalability and reliability, mobility support, multicast and caching
performance, real-time communication efficacy, energy consumption
frugality, and disruption and delay tolerance. We detail ICN-
specific aspects as well, such as information security and trust,
persistence, availability, provenance, and location independence. We
then survey the evaluation tools currently available to researchers
in this area and provide suggestions regarding methodology and
metrics. Finally, this document sheds some light on the impact of
ICN on network security.
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Status of this Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 4
2. Toward ICN Baseline Scenarios . . . . . . . . . . . . . . . . 5
2.1. Social Networking . . . . . . . . . . . . . . . . . . . . 5
2.2. Real-time Communication . . . . . . . . . . . . . . . . . 7
2.3. Mobile Networking . . . . . . . . . . . . . . . . . . . . 8
2.4. Infrastructure Sharing . . . . . . . . . . . . . . . . . . 10
2.5. Content Dissemination . . . . . . . . . . . . . . . . . . 11
2.6. Vehicular Networking . . . . . . . . . . . . . . . . . . . 13
2.7. Multiply Connected Nodes and Economics . . . . . . . . . . 15
2.8. Energy Efficiency . . . . . . . . . . . . . . . . . . . . 20
2.9. Delay- and Disruption-Tolerance . . . . . . . . . . . . . 22
2.10. Internet of Things . . . . . . . . . . . . . . . . . . . 27
2.11. Smart City . . . . . . . . . . . . . . . . . . . . . . . 30
2.12. Operation across Multiple Network Paradigms . . . . . . . 31
2.13. Summary . . . . . . . . . . . . . . . . . . . . . . . . . 32
3. Evaluation Methodology . . . . . . . . . . . . . . . . . . . . 33
3.1. ICN Simulators and Testbeds . . . . . . . . . . . . . . . 34
3.1.1. CCN and NDN . . . . . . . . . . . . . . . . . . . . . 34
3.1.2. PSI . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.1.3. NetInf . . . . . . . . . . . . . . . . . . . . . . . . 36
3.1.4. COMET . . . . . . . . . . . . . . . . . . . . . . . . 37
3.1.5. Large-scale Testing . . . . . . . . . . . . . . . . . 37
3.2. Topology Selection . . . . . . . . . . . . . . . . . . . . 38
3.3. Traffic Load . . . . . . . . . . . . . . . . . . . . . . . 39
3.4. Choosing Relevant Metrics . . . . . . . . . . . . . . . . 40
3.4.1. Traffic Metrics . . . . . . . . . . . . . . . . . . . 43
3.4.2. System Metrics . . . . . . . . . . . . . . . . . . . . 44
3.5. Resource Equivalence and Tradeoffs . . . . . . . . . . . . 46
3.6. Technology Evolution Assumptions . . . . . . . . . . . . . 46
4. Security Considerations . . . . . . . . . . . . . . . . . . . 46
4.1. Authentication . . . . . . . . . . . . . . . . . . . . . . 47
4.2. Authorization, Access Control and Statistics . . . . . . . 48
4.3. Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4. Changes to the Network Security Threat Model . . . . . . . 49
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 50
6. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 50
7. Informative References . . . . . . . . . . . . . . . . . . . . 51
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 62
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1. Introduction
Information-centric networking (ICN) marks a fundamental shift in
communications and networking. In contrast with the omnipresent and
very successful host-centric paradigm, which is based on perpetual
connectivity and the end-to-end principle, ICN changes the focal
point of the network architecture from the end host to "named
information" (or content, or data). In this paradigm, connectivity
may well be intermittent. End-host and in-network storage can be
capitalized upon transparently, as bits in the network and on storage
devices have exactly the same value. Mobility and multiaccess are
the norm. Anycast, multicast, and broadcast are natively supported,
and energy efficiency is a design consideration from the very
beginning.
Although interest in ICN is growing rapidly, ongoing work on
different architectures, such as, for example, NetInf [NetInf], CCN
and NDN [CCN], the publish-subscribe Internet (PSI) architecture
[PSI], and the data-oriented architecture [DONA] is far from being
completed. The development phase that ICN is going through and the
plethora of approaches to tackle the hardest problems make this a
very active and growing research area but, on the downside, it also
makes it more difficult to compare different proposals on an equal
footing. This document aims to address this by establishing a common
understanding about potential experimental setups where different ICN
approaches can be tested and compared against each other while
showcasing their advantages.
Ahlgren et al. [SoA] note that describing ICN architectures is akin
to shooting a moving target. We find that comparing these different
approaches is often even more tricky. in particular, we observe that
a variety of performance evaluation scenarios has been devised,
typically with good reason, in order to highlight the advantages of
each ICN architecture. That is, there is no single scenario, use
case, or reference topology which is employed as a benchmark
consistently across the ICN literature. This should be expected to
some degree at this early stage of ICN development. Nevertheless,
this document shows that certain baseline scenarios seem to emerge in
which ICN architectures could showcase their superiority over current
systems, in general, and against each other, in particular.
The remainder of this document is organized as follows. In Section 2
we review the peer-reviewed ICN literature and select prominent
evaluation study cases as a foundation for the baseline scenarios to
be considered by the IRTF Information-Centric Networking Research
Group (ICNRG) in its future work. The list of scenarios has evolved
since the first draft version of this document based on the input
from the research group and the corresponding text contributions.
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Section 3 presents currently available simulation tools and
experimental testbeds that can be used in evaluating ICN, and
outlines the key elements that should be considered in an ICN
evaluation. Finally, Section 4 discusses the impact of ICN on
network security.
2. Toward ICN Baseline Scenarios
This section presents a number of scenarios grouped into several
categories. Note that certain evaluation scenarios span across these
categories, so the boundaries between them should not be considered
rigid and inflexible. There are two goals for this section. First,
to provide a set of use cases and applications that highlight
opportunities for testing different ICN proposals. Second, to
identify key attributes of a common set of techniques that can be
instrumental in evaluating ICN.
The overall aim is that each scenario is described at a sufficient
level of detail so that it can serve as the base for comparative
evaluations of different approaches. This will need to include
reference configurations, topologies, specifications of traffic mixes
and traffic loads. These specifications (or configurations) should
preferably come as sets that describe extremes as well as "typical"
usage scenarios.
2.1. Social Networking
Social networking applications have proliferated over the past decade
based on overlay content dissemination systems that require large
infrastructure investments to rollout and maintain. Content
dissemination is at the heart of the ICN paradigm and, therefore, we
would expect that they are a "natural fit" for showcasing the
superiority of ICN over traditional client-server TCP/IP-based
systems.
Mathieu et al. [ICN-SN], for instance, illustrate how an Internet
Service Provider (ISP) can capitalize on CCN to deploy a short-
message service akin to Twitter at a fraction of the complexity of
today's systems. Their key observation is that such a service can be
seen as a combination of multicast delivery and caching. That is, a
single user addresses a large number of recipients, some of which
receive the new message immediately as they are online at that
instant, while others receive the message whenever they connect to
the network.
Along similar lines, Kim et al. [VPC] present an ICN-based social
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networking platform in which a user shares content with her/his
family and friends without the need for a centralized content server;
see also subsection 2.4, below, and [JBDMM+12]. Based on the CCN
naming scheme, [VPC] takes a user name to represent a set of devices
that belong to the person. Other users in this in-network,
serverless social sharing scenario can access the user's content not
via a device name/address but with the user's name. In [VPC],
signature verification does not require any centralized
authentication server. Kim and Lee [VPC2] present a proof-of-concept
evaluation in which users with ordinary smartphones can browse a list
of members or content using a name, and download the content selected
from the list.
In short, in both ICN-based social networking application scenarios
there is no need for a classic client-server architecture (let alone
a cloud-based infrastructure) to intermediate between content
providers and consumers in a hub-and-spoke fashion.
Earlier work by Arianfar et al. [ANO10] considers a similar pull-
based content retrieval scenario using a different architecture,
pointing to significant performance advantages. Although the authors
consider a network topology (redrawn in Fig. 1 for convenience) that
has certain interesting characteristics, they do not explicitly
address social networking in their evaluation scenario. Nonetheless,
similarities are easy to spot: "followers" (such as C0, C1, ..., and
Cz in Fig. 1) obtain content put "on the network" (I1, ..., Im, and
B1, B2) by a single user (e.g. Px) relying solely on network
primitives.
\--/
|C0|
/--\ +--+ +--+ +--+ +--+
*=== |I0| === |I1| ... |In| |P0|
\--/ +--+ +--+ +--+ +--+
|C1| \ / o
/--\ +--+ +--+ o
o |B1| === |B2| o
o o o o o +--+ +--+ o
o / \ o
o +--+ +--+ +--+ +--+
o *=== |Ik| === |Il| ... |Im| |Px|
\--/ +--+ +--+ +--+ +--+
|Cz|
/--\
Figure 1. Dumbbell with linear daisy chains.
In summary, the social networking scenario aims to exercise each ICN
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architecture in terms of network efficiency, multicast support,
caching performance and its reliance on centralized mechanisms (or
lack thereof).
2.2. Real-time Communication
Real-time audio and video (A/V) communications include an array of
services ranging from one-to-one voice calls to multi-party multi-
media conferences with support ranging from whiteboards to augmented
reality. Real-time communications have been studied and deployed in
the context of packet- and circuit-switched networks for decades.
The stringent quality of service requirements that this type of
communication imposes on network infrastructure are well known.
Since one could argue that network primitives which are excellent for
information dissemination are not well-suited for conversational
services, ICN evaluation studies should consider real-time
communication scenarios in detail.
Notably, Jacobson et al. [VoCCN] presented an early evaluation where
the performance of a VoIP (voice over IP) call using an information-
centric approach was compared with that of an off-the-shelf VoIP
implementation using RTP/UTP. The results indicated that despite the
extra cost of adding security support in the ICN approach,
performance was virtually identical in the two cases evaluated in
their testbed. However, the experimental setup presented is quite
rudimentary, while the evaluation considered a single voice call
only. Xuan and Yan [NDNpb] revisit the same scenario but are
primarily interested in reducing the overhead that may arise in one-
to-one communication employing an ICN architecture. Both studies
illustrate that quality telephony services are feasible with at least
one ICN approach. That said, future ICN evaluations should employ
standardized call arrival patterns, for example, following well-
established methodologies from the quality of service/experience
(QoS/QoE) evaluation toolbox and would need to consider more
comprehensive metrics.
Given the wide-spread deployment of real-time A/V communications, an
evaluation of an ICN system should demonstrate capabilities beyond
feasibility. For example, with respect to multimedia conferencing,
Zhu et al. [ACT] describe the design of a distributed audio
conference tool based on NDN. Their system includes ICN-based
conference discovery, discovery of speakers and voice data
distribution. The reported evaluation results point to gains in
scalability and security. Moreover, Chen et al. [G-COPSS] explore
the feasibility of implementing a Massively Multiplayer Online Role
Playing Game (MMORPG) based on CCNx and show that stringent temporal
requirements can be met, while scalability is significantly improved
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when compared to a host-centric (IP-based) client-server system.
This type of work points to benefits for both the data and control
path of a modern network infrastructure.
Real-time communication also brings up the issue of named data
granularity for dynamically generated content. For instance, today
in many cases A/V data is generated in real-time and is distributed
immediately. One possibility is to apply a single name to the entire
content, but this could result in significant distribution delays.
Alternatively, distributing the content in smaller "chunks" which are
named individually may be a better option with respect to real-time
distribution but raises naming scalability concerns.
We observe that, all in all, the ICN research community has hitherto
only scratched the surface of this area with respect to illustrating
the benefits of adopting an information-centric approach as opposed
to a host-centric one, and more work is recommended in this
direction.
In short, scenarios in this category should illustrate not only
feasibility but reduced complexity, increased scalability,
reliability, and capacity to meet stringent QoS/QoE requirements when
compared to established host-centric solutions. Accordingly, the
primary aim of this scenario is to exercise each ICN architecture in
terms of its ability to satisfy real-time QoS requirements and
improved user experience.
2.3. Mobile Networking
IP mobility management relies on anchors to provide ubiquitous
connectivity to end-hosts as well as moving networks. This is a
natural choice for a host-centric paradigm that requires end-to-end
connectivity and a continuous network presence for hosts [SCES]. An
implicit assumption in host-centric mobility management is therefore
that the mobile node aims to connect to a particular peer, as well as
to maintain global reachability and service continuity [EEMN].
However, with ICN new ideas about mobility management should come to
the fore capitalizing on the different nature of the paradigm. For
example, one could exploit the ability of nodes to better express
their intended use of the network, i.e., the retrieval of a small
subset of the global data corpus as discussed in [MOBSURV].
Dannewitz et al. [N-Scen], illustrate a scenario where a multiaccess
end-host can retrieve email securely using a combination of cellular
and wireless local area network (WLAN) connectivity. This scenario
borrows elements from previous work, e.g., [DTI], and develops them
further with respect to multiaccess. Unfortunately, Dannewitz et al.
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[N-Scen] do not present any results demonstrating that an ICN
approach is, indeed, better. That said, the scenario is interesting
as it considers content specific to a single user (i.e., her mailbox)
and does point to reduced complexity. It is also compatible with
recent work in the Distributed Mobility Management (DMM) Working
Group within the IETF. Finally, Xylomenos et al. [PSIMob] as well as
[EEMN] argue that an information-centric architecture can avoid the
complexity of having to manage tunnels to maintain end-to-end
connectivity as is the case with mobile anchor-based protocols such
as Mobile IP (and its variants). Similar considerations hold for a
vehicular (networking) environment, as we discuss in subsection 2.6
below.
Overall, mobile networking scenarios have not been developed in
detail, let alone evaluated at a large scale. Further, the majority
of scenarios discussed so far have related to information consumer,
rather than source, mobility. We expect that in the coming period
more papers will address this topic. Earlier work [mNetInf] argues
that for mobile and multiaccess networking scenarios we need to go
beyond the current mobility management mechanisms in order to
capitalize on the core ICN features. They present a testbed setup
(redrawn in Fig. 2) which can serve as the basis for other ICN
evaluations. In this scenario, node "C0" has multiple network
interfaces that can access local domains N0 and N1 simultaneously
allowing C0 to retrieve objects from which ever server (I2 or I3) can
supply them without necessarily needing to access the servers in the
core network "C" (P1 and P2). Lindgren [Lin11] explores this
scenario further for an urban setting. He uses simulation and
reports sizable gains in terms of reduction of object retrieval times
and core network capacity use.
+------------+ +-----------+
| Network N0 | | Network C |
| | | |
| +--+ | ==== | +--+ |
| |I2| | | |P1| |
| +--+ | | +--+ |
| \--/ | | |
+-----|C0|---+ | |
| /--\ | | |
| +--+ | | |
| |I3| | | +--+ |
| +--+ | ==== | |P2| |
| | | +--+ |
| Network N1 | | |
+------------+ +-----------+
Figure 2. Overlapping wireless multiaccess.
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The benefits from capitalizing on the broadcast nature of wireless
access technologies has yet to be explored to its full potential in
the ICN literature, including quantifying possible gains in terms of
energy efficiency [AMR13]. Obviously, ICN architectures must avoid
broadcast storms. Early work in this area considers distributed
packet suppression techniques which exploit delayed transmissions and
overhearing; examples can be found in [MPZ10] and [OLG10] for ICN-
based mobile ad-hoc networks (MANETs), and in [WAKVWZ12] and [ACM12]
for vehicular scenarios.
One would expect that mobile networking scenarios will be naturally
coupled with those discussed in the previous sections, as more users
access social networking and multimedia applications through mobile
devices. Further, the constraints of real-time A/V applications
create interesting challenges in handling mobility, particularly in
terms of maintaining service continuity. This scenario therefore
spans across most of the others considered in this document with the
likely need for some level of integration, particularly considering
the well-documented increases in mobile traffic. Mobility is further
considered in subsection 2.9 and the economic consequences of nodes
having multiple network interfaces is explored in subsection 2.7.
To summarize, mobile networking scenarios should aim to provide
service continuity for those applications that require it, decrease
complexity and control signaling for the network infrastructure, as
well as increase wireless capacity utilization by taking advantage of
the broadcast nature of the medium. Beyond this, mobile networking
scenarios should form a cross-scenario platform that can highlight
how other scenarios can still maintain their respective performance
metrics during periods of high mobility.
2.4. Infrastructure Sharing
A key idea in ICN is that the network should secure information
objects per se, not the communications channel that they are
delivered over. This means that hosts attached to an information-
centric network can share resources on an unprecedented scale,
especially when compared to what is possible in an IP network. All
devices with network access and storage capacity can contribute their
resources increasing the value of an information-centric network
(perhaps) much faster than Metcalfe's law.
For example, Jacobson et al. [JBDMM+12] argue that in ICN the "where
and how" of obtaining information are new degrees of freedom. They
illustrate this with a scenario involving a photo sharing application
which takes advantage of whichever access network connectivity is
available at the moment (WLAN, Bluetooth, and even SMS) without
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requiring a centralized infrastructure to synchronize between
numerous devices. It is important to highlight that since the focus
of communication changes, keep-alives in this scenario are simply
unnecessary, as devices participating in the testbed network
contribute resources in order to maintain user content consistency,
not link state information as is the case in the host-centric
paradigm. This means that the notion of "infrastructure" may be
completely different in the future.
Muscariello et al. [MCG11], for instance, presented early work on an
analytical framework that attempts to capture the storage/bandwidth
tradeoffs that ICN enables and can be used as foundation for a
network planning tool. In addition, Chai et al. [CHPP12] explore the
benefits of ubiquitous caching throughout an information-centric
network and argue that "caching less can actually achieve more."
These papers also sit alongside a variety of other studies that look
at various scenarios such as caching HTTP-like traffic [L9] and
BitTorrent-like traffic [TKMEMT12]. We observe that much more work
is needed in order to understand how to make optimal use of all
resources available in an information-centric network. In real-world
deployments, policy and commercial considerations are also likely to
affect the use of particular resources and more work is expected in
this direction as well (see also subsection 2.7).
In conclusion, scenarios in this category, would cover the
communication-computation-storage tradeoffs that an ICN deployment
must consider. This would exercise features relating to network
planning, perhaps capitalizing on user-provided resources, as well as
operational and economical aspects to illustrate the superiority of
ICN over other approaches. An obvious baseline to compare against in
this regard is existing federations of IP-based Content Distribution
Networks (CDNs).
2.5. Content Dissemination
Content dissemination has attracted more attention than other aspects
of ICN, perhaps due to a misunderstanding of what the first "C" in
CCN stands for. Scenarios in this category abound in the literature,
including stored and streaming A/V distribution, file distribution,
mirroring and bulk transfers, versioned content services (c.f.,
Subversion-type revision control), as well as traffic aggregation.
Decentralized content dissemination with on-the-fly aggregation of
information sources was envisaged in [N-Scen], where information
objects can be dynamically assembled based on hierarchically
structured subcomponents. For example, a video stream could be
associated with different audio streams and subtitle sets, which can
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all be obtained from different sources. Using the topology depicted
in Fig. 1 as an example, an application at C1 may end up obtaining,
say, the video content from I1, but the user-selected subtitles from
Px. Semantics and content negotiation, on behalf of the user, were
also considered, e.g., for the case of popular tunes which may be
available in different encoding formats. Effectively this scenario
has the information consumer issuing independent requests for content
based on information identifiers, and stitching the pieces together
irrespective of "where" or "how" they were obtained.
A case in point for content dissemination are vehicular ad-hoc
networks (VANETs), as an ICN approach may address their needs for
information dissemination between vehicles better than today's
solutions, as discussed in the following subsection. The critical
part of information dissemination in a VANET scenario revolves around
"where" and "when". For instance, one may be interested in traffic
conditions 2 km ahead while having no interest in similar information
about the area around the path origin. VANET scenarios may provide
fertile ground for showcasing the ICN advantage with respect to
content dissemination especially when compared with current host-
centric approaches. That said, information integrity and filtering
are challenges that must be addressed. As mentioned earlier, content
dissemination scenarios in VANETs have a particular affinity to the
mobility scenarios discussed earlier.
Content dissemination scenarios, in general, have a large overlap
with those described in the previous sections and are explored in
several papers, such as [DONA] [PSI] [PSIMob] [NetInf] [CCN]
[JBDMM+12] [ANO10], just to name a few. In addition, Chai et al.
[CURLING] present a hop-by-hop hierarchical content resolution
approach, which employs receiver-driven multicast over multiple
domains, advocating another content dissemination approach. Yet,
largely, work in this area did not address the issue of access
authorization in detail. Often, the distributed content is mostly
assumed to be freely accessible by any consumer. Distribution of
paid-for or otherwise restricted content on a public ICN network
requires more attention in the future. Fotiou et al. [FMP12]
consider a scheme to this effect but it still requires access to an
authorization server to verify the user's status after the
(encrypted) content has been obtained. This may effectively negate
the advantage of obtaining the content from any node, especially in a
disruption-prone or mobile network.
In summary, scenarios in this category aim to exercise primarily
scalability, cost and performance attributes of content
dissemination. Particularly, they should highlight the ability of an
ICN to scale to billions of objects, while not exceeding the cost of
existing content dissemination solutions (i.e., CDNs) and, ideally,
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increasing performance. These should be shown in a holistic manner,
improving content dissemination for both information consumers and
publishers of all sizes. We expect that in particular for content
dissemination, both extreme as well as typical scenarios can be
specified drawing data from current CDN deployments.
2.6. Vehicular Networking
Users "on wheels" are interested in road safety, traffic efficiency,
and infotainment applications that can be supported through vehicle-
to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless
communications. These applications exhibit unique features in terms
of traffic generation patterns, delivery requirements, spatial and
temporal scope, which pose great challenges to traditional networking
solutions. VANETs, by their nature, are characterized by challenges
such as fast-changing topology, intermittent connectivity, high node
mobility, but also by the opportunity to combine information from
different sources as each vehicle does not care about "who" delivers
the named data objects.
ICN is an attractive candidate solution for vehicular networking, as
it has several advantages. First, ICN fits well to the nature of
typical vehicular applications that are geography- and time-dependent
(e.g., road traveler information, accident warning, point-of-interest
advertisements) and usually target vehicles in a given area,
regardless of their identity or IP address. These applications are
likely to benefit from in-network and decentralized data caching and
replication mechanisms. Second, content caching is particularly
beneficial for intermittent on-the-road connectivity and can speed up
data retrieval through content replication in several nodes. Caching
can usually be implemented at relatively low cost in vehicles as the
energy demands of the ICN device are likely to be a negligible
fraction of the total vehicle energy consumption, thus allowing for
sophisticated processing, continuous communication and adequate
storage in the vehicle. Finally, ICN natively supports asynchronous
data exchange between end-nodes. By using (and redistributing)
cached named information objects, a mobile node can serve as a link
between disconnected areas. In short, ICN can enable communication
even under intermittent network connectivity, which is typical of
vehicular environments with sparse roadside infrastructure and fast
moving nodes.
The advantages of ICN in vehicular networks were preliminarily
discussed in [BK10] and [DMND], and additionally investigated in
[WWKVZ12] [WAKVWZ12] [AKH11] [TL12] [ACM12] [CRoWN]. For example,
Bai and Krishnamachari [BK10] take advantage of the localized and
dynamic nature of a VANET to explore how a road congestion
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notification application can be implemented. Wang et al. [DMND]
consider data collection where Road-Side Units (RSUs) collect
information from vehicles by broadcasting NDN-like INTEREST packets.
The proposed architecture is evaluated using simulation in a grid
topology and is compared against a host-centric alternative based on
Mobile IP. See Fig. 3 for an indicative example of an urban VANET
topology. Their results indicate high efficiency for ICN even at
high speeds. That said, the authors point out that as this work is a
preliminary exploration of ICN in vehicular environments, many issues
remain to be evaluated, such as system scalability to large numbers
of vehicles and the impact of vehicles forwarding Interests and
relaying data for other vehicles.
+ - - _- - -_- - - -_- - _- - - +
| /_\ /_\ /_\ /_\ |
| o o o o o o o o |
| +-------+ +-------+ _ |
| | | | |/_\ |
| _ | | | |o o |
| /_\| | | | |
| o o+--_----+\===/+--_----+ |
| /_\ |RSU| /_\ |
| o o /===\ o o |
| +-------+ +-------+ _ |
| | | | |/_\ |
| _ | | | |o o |
|/_\ | | | | |
|o o +_-----_+ +_-----_+ |
| /_\ /_\ /_\ /_\ |
+_ _ o_o_ _o_o_ _ _o_o_ _o_o_ _ +
Figure 3. Urban grid VANET topology.
As mentioned in the previous section, due to the short communication
duration between a vehicle and the RSU, and the typically short time
of sustained connectivity between vehicles, VANETs may be a good
showcase for the ICN advantages with respect to content
dissemination. Wang et al. [WWKVZ12], for instance, analyze the
advantages of hierarchical naming for vehicular traffic information
dissemination. Arnould et al. [AKH11] apply ICN principles to safety
information dissemination between vehicles with multiple radio
interfaces. In [TL12], TalebiFard and Leung use network coding
techniques to improve content dissemination over multiple ICN paths.
Amadeo et al. [ACM12][[CRoWN] propose an application-independent ICN
framework for content retrieval and distribution where the role of
provider can be played equivalently by both vehicles and RSUs. ICN
forwarding is extended through path-state information carried in
Interest and Data packets, stored in a new data structure kept by
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vehicular nodes, and exploited also to cope with node mobility.
Typical scenarios for testing content distribution in VANETs may be
highways with vehicles moving in straight lines, with or without RSUs
along the road, as shown in Fig. 4. With a NDN approach in mind, for
example, RSUs may send Interests to collect data from vehicles
[DMND], or vehicles may send Interests to collect data from other
peers [WAKVWZ12] or from RSUs [ACM12]. Fig. 2 applies to content
dissemination in VANET scenarios as well, where C0 represents a
vehicle which can obtain named information objects via multiple
wireless peers and/or RSUs (I2 and I3 in the figure). Grid
topologies such as the one illustrated in Fig. 3 should be considered
in urban scenarios with RSUs at the crossroads, or co-located with
traffic lights as in [CRoWN].
\__/ \__/
|RU| |RU|
================================
_ _ _ _
/_\ /_\ /_\ /_\
_ _ o_o_ _o_o_ _o_o_ _o_o_ _ _ _
_ _ _ _
/_\ /_\ /_\ /_\
o o o o o o o o
================================
Figure 4. Highway VANET topology.
To summarize, VANET scenarios aim to exercise ICN deployment from
various perspectives, including scalability, caching, transport, and
mobility issues. There is a need for further investigation in (i)
challenging scenarios (e.g., disconnected segments); (ii) scenarios
involving both consumer and provider mobility; (iii) smart caching
techniques which take into consideration node mobility patterns,
spatial and temporal relevance, content popularity, and social
relationships between users/vehicles; (iv) identification of new
applications (beyond data dissemination and traffic monitoring) that
could benefit from the adoption of an ICN paradigm in vehicular
networks (e.g., mobile cloud, social networking).
2.7. Multiply Connected Nodes and Economics
The evolution of, in particular, wireless networking technologies has
resulted in a convergence of the bandwidth and capabilities of
various different types of networks. Today a leading edge mobile
telephone or tablet computer will typically be able to access a Wi-Fi
access point, a 4G cellular network and the latest generation of
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Bluetooth local networking. Until recently a node would usually have
a clear favorite network technology appropriate to any given
environment. The choice would, for example, be primarily determined
by the available bandwidth with cost as a secondary determinant.
Furthermore, it is normally the case that a device only uses one of
the technologies at a time for any particular application.
It seems likely that this situation will change so that nodes are
able to use all of the available technologies in parallel. This will
be further encouraged by the development of new capabilities in
cellular networks including Small Cell Networks (SCN) and
Heterogeneous Networks (HetNet). Consequently, mobile devices will
have similar choices to wired nodes attached to multiple service
providers allowing "multi-homing" via the various different
infrastructure networks as well as potential direct access to other
mobile nodes via Bluetooth or a more capable form of ad hoc Wi-Fi.
Infrastructure networks are generally under the control of separate
economic entities that may have different policies about the
information of an ICN deployed within their network caches. As ICN
shifts the focus from nodes to information objects, the interaction
between networks will likely evolve to capitalize on data location
independence, efficient and scalable in-network named object
availability and access via multiple paths. These interactions
become critical in evaluating the technical and economic impact of
ICN architectural choices, as noted in [ArgICN]. Beyond simply
adding diversity in deployment options, these networks have the
potential to alter the incentives among existing, and future, we may
add, network players, as noted in [EconICN].
Moreover, such networks enable more numerous inter-network
relationships where exchange of information may be conditioned on a
set of multilateral policies. For example, shared SCNs are emerging
as a cost-effective way to address coverage of complex environments
such as sports stadiums, large office buildings, malls, etc. [OptSC]
[FEMTO]. Such networks are likely to be a complex mix of different
cellular and WLAN access technologies (such as HSPA, LTE, and Wi-Fi)
as well as ownership models. It is reasonable to assume that access
to content generated in such networks may depend on contextual
information such as the subscription type, timing, and location of
both the owner and requester of the content. The availability of
such contextual information across diverse networks can lead to
network inefficiencies unless data management can benefit from an
information-centric approach. The "Event with Large Crowds"
demonstrator created by the SAIL project investigated this kind of
scenario; more details are available in [SAIL-B3].
Jacobson et al. [CCN] include interactions between networks in their
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overall system design, and mention both "an edge-driven, bottom-up
incentive structure" and techniques based on evolutions of existing
mechanisms both for ICN router discovery by the end-user and for
interconnecting between autonomous systems (AS). For example, a BGP
extension for domain-level content prefix advertisement can be used
to enable efficient interconnection between AS's. Liu et al. [MLDHT]
proposed to address the "suffix-hole" issue found in prefix-based
name aggregation through the use of a combination of Bloom-filter
based aggregation and multi-level DHT.
Name aggregation has been discussed for a flat naming design as well
in [NCOA], which also notes that based on estimations in [DONA] flat
naming may not require aggregation. This is a point that calls for
further study. Scenarios evaluating name aggregation, or lack
thereof, should take into account the amount of state (e.g. size of
routing tables) maintained in edge routers as well as network
efficiency (e.g. amount of traffic generated).
+---------------+
+---------->| Popular Video |
| +---------------+
| ^ ^
| | |
| +-+-+ $0/MB +-+-+
| | A +-------+ B |
| ++--+ +-+-+
| | ^ ^ |
| $8/MB | | | | $10/MB
| v | | v
+-+-+ $0/MB +--+---------+--+
| D +---------+ C |
+---+ +---------------+
Figure 5. Relationships and transit costs between networks A to D.
DiBenedetto et al. [RP-NDN] study policy knobs made available by NDN
to network operators. New policies, which are not feasible in the
current Internet are described, including a "cache sharing peers"
policy, where two peers have an incentive to share content cached in,
but not originating from, their respective network. The simple
example used in the investigation considers several networks and
associated transit costs, as shown in Fig. 5. (based on Fig. 1 of
[RP-NDN]). Agyapong and Sirbu [EconICN] further establish that ICN
approaches should incorporate features that foster (new) business
relationships. For example, publishers should be able to indicate
their willingness to partake in the caching market, proper reporting
should be enabled to avoid fraud, and content should be made
cacheable as much as possible to increase cache hit ratios.
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Ahlgren et al. [SAIL-B3] enable network interactions in the NetInf
architecture using a name resolution service at domain edge routers,
and a BGP-like routing system in the NetInf Default Free Zone.
Business models and incentives are studied in [SAIL-A7] and [SAIL-
A8], including scenarios where the access network provider (or a
virtual CDN) guarantees QoS to end users using ICN. Fig. 6
illustrates a typical scenario topology from this work which involves
an interconnectivity provider.
+----------+ +-----------------+ +------+
| Content | | Access Network/ | | End |
| Provider +---->| ICN Provider +---->| User |
+----------+ +-+-------------+-+ +------+
| |
| |
v v
+-------------------+ +----------------+ +------+
| Interconnectivity | | Access Network | | End |
| Provider +---->| Provider +------>| User |
+-------------------+ +----------------+ +------+
Figure 6. Setup and operating costs of network entities
Jokela et al. [LIPSIN] propose a two-layer approach where additional
rendezvous systems and topology formation functions are placed
logically above multiple networks and enable advertising and routing
content between them. Visala et al. [LANES] further describe an ICN
architecture based on similar principles, which, notably, relies on a
hierarchical DHT-based rendezvous interconnect. Rajahalme et al.
[PSIRP1] describe a rendezvous system using both a BGP-like routing
protocol at the edge and a DHT-based overlay at the core. Their
evaluation model is centered around policy-compliant path stretch,
latency introduced by overlay routing, caching efficacy, and overlay
routing node load distribution.
Rajahalme et al. [ICCP] point out that ICN architectural changes may
conflict with the current tier-based peering model. For example,
changes leading to shorter paths between ISPs are likely to meet
resistance from Tier-1 ISPs. Rajahalme [IDMcast] shows how
incentives can help shape the design of specific ICN aspects, and in
[IDArch] he presents a modeling approach to exploit these incentives.
This includes a network model which describes the relationship
between Autonomous Systems based on data inferred from the current
Internet, a traffic model taking into account business factors for
each AS, and a routing model integrating the valley-free model and
policy-compliance. A typical scenario topology is illustrated in
Fig. 7, which is redrawn here based on Fig. 1 of [ICCP]. Note that
it relates well with the topology illustrated in Fig. 1 of this
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document.
o-----o
+-----+ J +-----+
| o--*--o |
| * |
o--+--o * o--+--o
| H +-----------+ I |
o-*-*-o * o-*-*-o
* * * * *
****** ******* * ******* *******
* * * * *
o--*--o o*-*-*o o--*--o
| E +--------+ F +---------+ G +
o-*-*-o o-----o o-*-*-o
* * * *
****** ******* ****** ******
* * * *
o--*--o o--*--o o--*--o o--*--o
| A | | B +-----------+ C | | D |
o-----o o--+--o o--+--o o----+o
| | ^^ | route
data | data | data || | to
| | || | data
o---v--o o---v--o o++--v-o
| User | | User | | Data |
o------o o------o o------o
Legend:
***** Transit link
+---+ Peering link
+---> Data delivery or route to data
Figure 7. Tier-based set of interconnections between AS A to J.
To sum up, the evaluation of ICN architectures across multiple
network types should include a combination of technical and economic
aspects, capturing their various interactions. These scenarios aim
to illustrate scalability, efficiency and manageability, as well as
traditional and novel network policies. Moreover, scenarios in this
category should specifically address how different actors have proper
incentives, not only in a pure ICN realm, but also during the
migration phase towards this final state.
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2.8. Energy Efficiency
Advantage can be taken of some prominent ICN features to
significantly reduce the energy footprint of future communication
networks. A simple example of a potential energy-saving operation is
caching. If a data object can be retrieved from within a network,
rather than from a distant origin server, clearly, significant
amounts of energy expenditure can be saved by avoiding several
further hops. Alternatively, approaches that aim to simplify
routers, such as [PURSUIT], could also reduce energy consumption by
pushing routing decisions into more energy-efficient data centers.
We elaborate on the energy efficiency potential of ICN based on three
categories of ICN characteristics. Namely, we point out that a) ICN
does not rely solely on end-to-end communication, b) ICN enables
ubiquitous caching, and c) ICN brings awareness of user requests (as
well as their corresponding responses) at the network layer thus
permitting network elements to better schedule their transmission
patterns.
First, ICN does not mandate perpetual end-to-end communication, which
introduces a whole range of energy consumption inefficiencies due to
the extensive signaling, especially in the case of mobile and
wirelessly connected devices. This opens up new opportunities for
accommodating sporadically connected nodes and could be one of the
keys to an order of magnitude decrease in energy consumption. For
example, web applications often need to maintain state at both ends
of a connection in order to verify that the authenticated peer is up
and running. This introduces keep-alive timers and polling behavior
with a high toll on energy consumption. Pentikousis [EEMN] discusses
several related scenarios and explains why the current host-centric
paradigm, which employs end-to-end always-on connections, introduces
built-in energy inefficiencies argueing that patches to make
currently deployed protocols energy-aware cannot provide for an order
of magnitude increase in energy efficiency.
Second, ICN network elements come with built-in caching capabilities,
which is often referred to as ubiquitous caching. Pushing data
objects to caches closer to end user devices, for example, could
significantly reduce the amount of transit traffic in the core
network, thereby reducing the energy used for data transport. Guan
et al. [EECCN] study the energy efficiency of CCNx (based on their
proposed energy model) and compare it with conventional content
dissemination systems such as CDNs and P2P. Their model is based on
the analysis of the topological structure and the average hop-length
from all consumers to the nearest cache location. Their results show
that ICN can be more energy efficient in delivering popular content.
In particular, they also note that different network element design
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choices (e.g. the optical bypass approach) can be more energy-
efficient in delivering infrequently accessed content.
Lee et al. [EECD] investigate the energy efficiency of various
network devices deployed in access, metro, and core networks for both
CDNs and ICN. They use trace-based simulations to show that an ICN
approach can substantially improve the network energy efficiency for
content dissemination mainly due to the reduction in the number of
hops required to obtain a data object, which can be served by
intermediate nodes in ICN. They also emphasize that the impact of
cache placement (in incremental deployment scenarios) and
local/cooperative content replacement strategies need to be carefully
investigated in order to better quantify the energy efficiencies
arising from adopting an ICN paradigm.
Third, as mentioned earlier, energy efficiency can be tackled by
different ICN approaches in ways that it cannot in a host-centric
paradigm. We already mentioned that in ICN, perpetual (always-on)
connectivity is not necessary, therefore mechanisms that capitalize
on powering down network interfaces are easier to accommodate. Since
all ICN elements are aware of the user request and its corresponding
data response, due to the nature of name-based routing, they can
employ power consumption optimization processes for determining their
transmission schedule. For example, network coding [NCICN] or
adaptive video streaming [COAST] can be used in individual ICN
elements so that redundant transmissions, possibly passing through
intermediary networks, could be significantly reduced, thereby saving
energy by avoiding to carry redundant traffic.
Alternatively, approaches that aim to simplify routers could also
reduce energy consumption by pushing routing decisions to a more
energy-efficient entity. Along these lines, Ko et al. [ICNDC] design
a data center network architecture based on ICN principles and
decouple the router control-plane and data-plane functionalities.
Thus, data forwarding is performed by simplified network entities
while the complicated routing computation is carried out in more
energy-efficient data centers.
To summarize, energy efficiency has been discussed in ICN evaluation
studies but most published work is preliminary in nature. Thus, we
suggest that more work is needed in this front. Evaluating energy
efficiency does not require the definition of new scenarios or
baseline topologies, but does require the establishment of clear
guidelines so that different ICN approaches can be compared not only
in terms of scalability, for example, but also in terms to power
consumption.
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2.9. Delay- and Disruption-Tolerance
Delay- and Disruption-Tolerant Networking (DTN) [DTN] [TBB13]
originated as a means to extend the Internet to interplanetary
communications. However, it was subsequently found to be an
appropriate architecture for many terrestrial situations as well.
Typically, this was where delays were greater than protocols such as
TCP could handle, and where disruptions to communications were the
norm rather than occasional annoyances, e.g., where an end-to-end
path does not necessarily exist when communication is initiated. DTN
has now been applied to many situations, including opportunistic
content sharing, handling infrastructural issues during emergency
situations (e.g., earthquakes) and providing connectivity to remote
rural areas without existing Internet provision and little or no
communications or power infrastructure.
The DTN architecture [RFC4838] is based on a "store, carry and
forward" paradigm that has been applied extensively to situations
where data is carried between network nodes by a "data mule", which
carries bundles of data stored in some convenient storage medium
(e.g., a USB memory stick). With the advent of sensor and peer-to-
peer (P2P) networks between mobile nodes, DTN is becoming a more
commonplace type of networking than originally envisioned. Since ICN
also does not rely on the familiar end-to-end communications
paradigm, there are, thus, clear synergies [DTN]. It could therefore
be argued that many of the key principles embodied within DTN also
exist in ICN, as we explain next.
First, both approaches rely on in-network storage. In the case of
DTN, bundles are stored temporarily on devices on a hop-by-hop basis.
In the case of ICN, information objects are also cached on devices
in a similar fashion. As such, both paradigms must provision storage
within the network.
Second, both approaches espouse late binding of names to locations
due to the potentially large interval between request and response
generation. In the case of DTN, it is often impossible to predict
the exact location (in a disconnected topology) where a node will be
found. Similarly, in the case of ICN, it is also often impossible to
predict where an information object might be found. As such, the
binding of a request/bundle to a destination (or routing locator)
must be performed as late as possible.
Third, both approaches treat data as a long-lived component that can
exist in the network for extended periods of time. In the case of
DTN, bundles are carried by nodes until appropriate next hops are
discovered. In the case of ICN, information objects are typically
cached until storage is exhausted. As such, both paradigms require a
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direct shift in the way applications interact with the network.
Through these similarities, it becomes possible to identify many DTN
principles that are already in existence within ICN architectures.
For example, ICN nodes will often retain publications locally, making
them accessible later on, much as DTN bundles are handled.
Consequently, these synergies suggest strong potential for marrying
the two technologies. This, for instance, could include building new
integrated Information-Centric Delay Tolerant Network (ICDTN)
protocols or, alternatively, building ICN schemes over existing DTN
protocols (or vice versa).
The above similarities suggest that integration of the two principles
would be certainly feasible. Beyond this, there are also a number of
direct benefits identifiable. Through caching and replication, ICN
offers strong information resilience, whilst, through store-and-
forward, DTN offers strong connectivity resilience. As such, both
architectures could benefit greatly from each other. Initial steps
have already been taken in the DTN community to integrate ICN
principles, e.g., the Bundle Protocol Query Block [BPQ] has been
added to the DTN Bundle Protocol [RFC5050]. Whilst, similarly,
initial steps have also been taken in the ICN community, such as
[SLINKY]. In fact, the SAIL project has recently developed a
prototype implementation of NetInf running over the DTN Bundle
Protocol.
For the purpose of evaluating the use of ICNs in a DTN setting, two
key scenarios are identified in this document (note the rest of this
section uses the term ICDTN). These are both prominent use cases
that are currently active in both the ICN and DTN communities. The
first is opportunistic content sharing, whilst the second is the use
of ad hoc networks during disaster recovery (e.g., earthquakes).
These are discussed in the context of a simulation-based evaluation;
due to the scale and mobility of DTN-like setups, this is the primary
method of evaluation used. Within the DTN community, the majority of
simulations are performed using the Opportunistic Network Environment
(ONE) simulator [ONE], which is referred to in this document. Before
exploring the two scenarios, the key shared components of their
simulation are discussed. This is separated into the two primary
inputs that are required: the environment and the workload.
In the case of both scenarios, the environment can be abstractly
modeled by a time series of active connections between device pairs.
Unlike other scenarios in this document, an ICDTN scenario does not
depend on (relatively) static topologies but, rather, a set of time-
varying disconnected topologies. In opportunistic networks, these
topologies are actually products of the mobility of users. For
example, if two users walk past each other, an opportunistic link can
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be created. There are two methods used to generate these mobility
patterns and, in turn, the time series of topologies. The first is
synthetic, whereby a (mathematical) model of user behavior is created
in an agent-based fashion, e.g., random waypoint, Gauss-Markov. The
second is trace-driven, whereby the mobility of real users is
recorded and used. In both cases, the output is a sequence of time-
stamped "contacts", i.e. a period of time in which two devices can
communicate. An important factor missing from typical mobility
traces, however, is the capacity of these contacts: how much data can
be transferred? In both approaches to modeling mobility, links are
usually configured as Bluetooth or WiFi (ONE easily allows this,
although lower layer considerations are ignored, e.g., interference).
This is motivated by the predominance of these technologies on
mobile phones.
The workload in an ICDTN is modeled much like the workload within the
other scenarios. It involves object creation/placement and object
retrieval. Object creation/placement can either be done statically
at the beginning of the simulations or, alternatively, dynamically
based on a model of user behavior. In both cases, the latter is
focused on, as it models far better the characteristics of the
scenarios.
Once the environment and workload has been configured, the next step
is to decide the key metrics for the study. Unlike traditional ICN,
the quality of service expectations are typically far lower in an
ICDTN, thereby moving away from metrics such as throughput. At a
high-level, it is of clear interest to evaluate different ICN
approaches with respect to both their delay- and disruption-
tolerance, i.e., how effective is the approach when used in an
environment subject to significant delay and/or disruption; and to
their active support for operations in a DTN environment.
The two most prominent metrics considered in a host-centric DTN are
delivery probability and delivery delay. The former relates to the
probability that a sent message will be received within a certain
delay bound, whilst the latter captures the average length of time it
takes for nodes to receive the message. These metrics are similarly
important in an ICDTN, although they are slightly different due to
the request-response nature of ICN. Therefore, the two most
prominent evaluative metrics are:
o Satisfaction Probability: The probability by which an information
request (e.g., Interest) will be satisfied (i.e., how often a Data
response will be received).
o Satisfaction Delay: The length of time it takes an information
request to be satisfied.
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Note that the key difference between the host-centric and
information-centric metrics is the need for a round-trip rather than
a one-way communication. Beyond this, depending on the focus of the
work, other elements that may be investigated include name
resolution, routing and forwarding in disconnected parts of the
network; support for unidirectional links; number of round trips
needed to complete a data transfer; long-term content availability
(or resilience); efficiency in the face of disruption, and so on. It
is also important to weigh these performance metrics against the
necessary overheads. In the case of an ICDTN, this is generally
measured by the number of message replicas required to access content
(note that routing in a DTN is often replication-based, which leads
to many copies of the same message).
The first key baseline scenario in this context is opportunistic
content sharing. This occurs when mobile nodes create opportunistic
links between each other to share content of interest. For example,
this might occur on an underground train, in which people pass news
items between their mobile phones. Equally, content generated on the
phones (e.g., tweets [TWIMIGHT]) could be stored for later forwarding
(or even forwarded amongst interested passengers on the train). Such
networks are often termed pocket-switched networks, as they are
independently formed between the user devices. Here, the evaluative
scenario of ICDTN microblogging is proposed. As previously
discussed, the construction of such an evaluative scenario requires a
formalization of its environment and workload. Luckily, there exist
a number of datasets that offer exactly this information required for
microblogging.
In terms of the environment (i.e., mobility patterns), the Haggle
project produced contact traces based on conference attendees using
Bluetooth. These traces are best targeted at application scenarios
in which a small group of (50-100) people are in a relatively
confined space. In contrast, larger scale traces are also available,
most notably MIT's Reality Mining project. These are better suited
for cases where longer-term movement patterns are of interest.
The second input, workload, relates to the creation and consumption
of microblogs (e.g. tweets). This can be effectively captured
because subscriptions conveniently formalize who consumes what. For
bespoke purposes, specific data can be directly collected from
Twitter for trace-driven simulations. Several Twitter datasets are
already available to the community containing a variety of data,
ranging from Tweets to follower graphs. Sources include:
http://www.tweetarchivist.com/
http://twapperkeeper.com/
http://www.infochimps.com/collections/twitter-census
http://socialcomputing.asu.edu/datasets/Twitter
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These datasets can therefore be used to extract information
production, placement and consumption.
The second key baseline scenario in this context relates to the use
of ICDTNs in emergency scenarios. In these situations it is typical
for infrastructure to be damaged or destroyed, leading to the
collapse of traditional forms of communications (e.g., cellular
telephone networks). This has been seen in the recent North India
flooding, as well as the 2011 Tohoku earthquake and tsunami. Power
problems often exacerbate the issue, with communication problems
lasting for days. Therefore, in order to address this, DTNs have
been used due to their high levels of resilience and independence
from fixed infrastructure. The most prominent use of DTNs in
disaster areas would be the dissemination of information, e.g.,
warnings and evacuation maps. Here, we focus on the dissemination of
standard broadcast information that should be received by all
parties.
For the environmental setup, there are no commonly used mobility
traces for disaster zones, unlike in the previous evaluative
scenario. This is clearly due to the difficultly (near
impossibility) of acquiring them in a real setting. That said,
various synthetic models are available. The Post Disaster Mobility
Model [MODEL1] models civilians and emergency responders after a
disaster has occurred, with people attempting to reach evacuation
points (this has also been implemented in ONE). [MODEL2] focuses on
emergency responders, featuring the removal of nodes from the
disaster zone, as well as things like obstacles (e.g. collapsed
buildings). [MODEL3] also looks at emergency responders, but focuses
on patterns associated with common procedures. For example, command
and control centers are typically set up with emergency responders
periodically returning. Clearly, the mobility of emergency
responders is particularly important in this setting because they
usually are the ones who will "carry" information into the disaster
zone. It is recommended that one of these emergency-specific models
are used during any evaluations, due to the inaccuracy of alternate
models used for "normal" behavior.
The workload input in this scenario is far simpler than for the
previous scenario. In emergency cases, the dissemination of
individual pieces of information to all parties is the norm. This is
often embodied using things like the Common Alert Protocol (CAP). As
such, small objects (e.g. 512KB to 2MB) are usually generated
containing text and images; note that the ONE simulator offers
utilities to easily generate these. These messages are also always
generated by central authorities, therefore making the placement
problem easier (they would be centrally generated and given to
emergency responders to disseminate as they pass through the disaster
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zone). The key variable is therefore the generation rate, which is
synonymous with the rate that microblogs are written in the previous
scenario. This will largely be based on the type of disaster
occurring, however, hourly updates would be an appropriate
configuration. Higher rates can also be tested, based on the rate at
which situations change (lands slides, for example, can exhibit
highly dynamic properties).
To summarize, this section has highlighted the applicability of ICN
principles to existing DTN scenarios. Two evaluative setups have been
described in detail, namely, mobile opportunistic content sharing
(microblogging) and emergency information dissemination.
2.10. Internet of Things
Advances in electronics miniaturization combined with low-power
wireless access technologies (e.g., ZigBee, NFC, Bluetooth and
others) have enabled the coupling of interconnected digital services
with everyday objects. As devices with sensors and actuators connect
into the network, they become "smart objects" and form the foundation
for the so-called Internet of Things (IoT). IoT is expected to
increase significantly the amount of content carried by the network
due to machine-to-machine (M2M) communication as well as novel user
interaction possibilities.
Yet, the full potential of IoT does not lie in simple remote access
to smart object data. Instead, it is the intersection of Internet
services with the physical world that will bring about the most
dramatic changes. Burke [IoTEx], for instance, makes a very good
case for creating everyday experiences using interconnected things
through participatory sensing applications. In this case, inherent
ICN capabilities for data discovery, caching, and trusted
communication are leveraged to obtain sensor information and enable
content exchange between mobile users, repositories, and
applications.
Kutscher and Farrell [IWMT] discuss the benefits that ICN can provide
in these environments in terms of naming, caching, and optimized
transport. The Named Information URI scheme (ni) [RFC6920], for
instance, could be used for globally unique smart object
identification, although an actual implementation report is not
currently available. Access to information generated by smart
objects can be of varied nature and often vital for the correct
operation of large systems. As such, supporting timestamping,
security, scalability, and flexibility need to be taken into account.
Ghodsi et al. [NCOA] examine hierarchical and self-certifying naming
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schemes and point out that ensuring reliable and secure content
naming and retrieval may pose stringent requirements (e.g., the
necessity for employing PKI), which can be too demanding for low-
powered nodes, such as sensors. That said, earlier work by Heidemann
et al. [nWSN] shows that, for dense sensor network deployments,
disassociating sensor naming from network topology and using named
content at the lowest level of communication in combination with in-
network processing of sensor data is feasible in practice and can be
more efficient than employing a host-centric binding between node
locator and the content existing therein.
Burke et al. [NDNl] describe the implementation of a lighting control
building automation system where the security, naming and device
discovery NDN mechanisms are leveraged to provide configuration,
installation and management of residential and industrial lighting
control systems. The goal is an inherently resilient system, where
even smartphones can be used for control. Naming reflects fixtures
with evolved identification and node reaching capabilities thus
simplifying bootstrapping, discovery, and user interaction with
nodes. The authors report that this ICN-based system requires less
maintenance and troubleshooting than typical IP-based alternatives.
Biswas et al. [CIBUS] visualize ICN as a contextualized information-
centric bus (CIBUS) over which diverse sets of service producers and
consumers co-exist with different requirements. ICN is leveraged to
unify different platforms to serve consumer-producer interaction in
both infrastructure and ad hoc settings. Ravindran et al. [Homenet],
show the application of this idea in the context of a home network,
where consumers (residents) require policy-driven interactions with
diverse services such as climate control, surveillance systems, and
entertainment systems. Name-based protocols are developed to enable
zero-configuration node and service discovery, contextual service
publishing and subscription, policy-based routing and forwarding with
name-based firewall, and hoc device-to-device communication.
IoT exposes ICN concepts to a stringent set of requirements which are
exacerbated by the amount of nodes, as well as by the type and volume
of information that must be handled. A way to address this is
proposed in [IoTScope], which tackles the problem of mapping named
information to an object, diverting from the currently typical
centralized discovery of services and leveraging the intrinsic ICN
scalability capabilities for naming. It extends the base [PURSUIT]
design with hierarchically-based scopes, facilitating lookup, access,
and modifications of only the part of the object information that the
user is interested in. Another important aspect is how to
efficiently address resolution and location of the information
objects, particularly when large numbers of nodes are connected, as
in IoT deployments. In [ICN-DHT], Katsaros et al. propose a
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Distributed Hash Table (DHT) which is compared with DONA [DONA].
Their results show how topological routing information has a positive
impact on resolution, at the expense of memory and processing
overhead.
The use of ICN mechanisms is IoT scenarios faces the most dynamic and
heterogeneous type of challenges, when taking into consideration the
requirements and objectives of such integration. The disparity in
technologies (not only in access technologies, but also in terms of
end-node diversity such as sensors, actuators and their
characteristics) as well as in the information that is generated and
consumed in such scenarios, will undoubtedly bring about many of the
considerations presented in the previous sections. For instance, IoT
shares similarities with the constraints and requirements applicable
to vehicular networking. Here, a central problem is the deployment
of mechanisms that can use opportunistic connectivity in unreliable
networking environments (similarly to the vehicular networking and
DTN scenarios).
However, one important concern in IoT scenarios, also motivated by
this strongly heterogeneous environment, is how content dissemination
will be affected by the different semantics of the disparate
information and content being shared. In fact, this is already a
difficult problem that goes beyond the scope of ICN [SEMANT]. With
the ability of the network nodes to cache forwarded information to
improve future requests, a challenge arises regarding whether the ICN
fabric should be involved in any kind of procedure (e.g., tagging)
that facilitates the relationship or the interpretation of the
different sources of information.
Another issue lies with the need for having energy-efficiency
mechanisms related to the networking capabilities of IoT
infrastructures. Often, the devices in IoT deployments have limited
battery capabilities, and thus need low power consumption schemes
working at multiple levels. In principle, energy efficiency gains
should be observed from the inherent in-network caching capability.
However, this might not be the most usual case in IoT scenarios,
where the information (particularly from sensors, or controlling
actuators) is more akin to real-time traffic, thus reducing the scale
of potential savings due to ubiquitous in-network caching.
ICN approaches, therefore, should be evaluated with respect to their
capacity to handle the content produced and consumed by extremely
large numbers of diverse devices. IoT scenarios aim to exercise ICN
deployment from different aspects, including ICN node design
requirements, efficient naming, transport, and caching of time-
restricted data. Scalability is particularly important in this
regard as the successful deployment of IoT principles could expand
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both device and content numbers dramatically beyond all current
expectations.
2.11. Smart City
The rapid increase in urbanization sets the stage for the most
compelling and challenging environments for networking. By 2050 the
global population will reach nine billion people, 75% of which will
dwell in urban areas. In order to cope with this influx, many cities
around the world have started their transformation toward the Smart
City vision. Smart cities will be based on the following innovation
axes: smart mobility, smart environment, smart people, smart living,
and smart governance. In development terms, the core goal of a smart
city is to become a business-competitive and attractive environment,
while serving citizen well being [CPG].
In a smart city, ICT plays a leading role and acts as the glue
bringing together all actors, services, resources (and their
interrelationships), that the urban environment is willing to host
and provide [MVM]. ICN appears particularly suitable for these
scenarios. Domains of interest include intelligent transportation
systems, energy networks, health care, A/V communications, peer-to-
peer and collaborative platforms for citizens, social inclusion,
active participation in public life, e-government, safety and
security, sensor networks. Clearly, this scenario has close ties to
the vision of IoT, discussed in the previous subsection, as well as
to vehicular networking.
Nevertheless, the road to build a real information-centric digital
ecosystem will be long and more coordinated effort is required to
drive innovation in this domain. We argue that smart city needs and
ICN technologies can trigger a virtuous innovation cycle toward
future ICT platforms. Recent concrete ICN-based contributions have
been formulated for home energy management [iHEMS], geo-localized
services [ACC], smart city services [IB], and traffic information
dissemination in vehicular scenarios [WAKVWZ12]. Some of the
proposed ICN-based solutions are implemented in real testbeds while
others are evaluated through simulation.
Zhang et al. [iHEMS] propose a secure publish-subscribe architecture
for handling the communication requirements of Home Energy Management
Systems (HEMS). The objective is to safely and effectively collect
measurement and status information from household elements, aggregate
and analyze the data, and ultimately enable intelligent control
decisions for actuation. They consider a simple experimental test-
bed for their proof-of-concept evaluation, exploiting open source
code for the ICN implementation, and emulating some node
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functionality in order to facilitate system operation.
A different scenario is considered in [ACC], where DHTs are employed
for distributed, scalable, and geographically-aware service lookup in
a smart city. Also in this case, the ICN application is validated by
considering a small-scale testbed: a small number of nodes are
realized with simple embedded PCs or specific hardware boards (e.g.,
for some sensor nodes); other nodes realizing the network connecting
the principal actors of the tests are emulated with workstations.
The proposal in [IB] draws from a smart city scenario (mainly
oriented towards waste collection management) comprising sensors and
moving vehicles, as well as a cloud computing system that supports
data retrieval and storage operations. The main aspects of this
proposal are analyzed via simulation using open source code which is
publicly available. Some software applications are designed on real
systems (e.g., PCs and smartphones).
To sum up, smart city scenarios aim to exercise several ICN aspects
in an urban environment. In particular, they can be useful to (i)
analyze the capacity of using ICN for managing extremely large data
sets; (ii) study ICN performance in terms of scalability in
distributed services; (iii) verify the feasibility of ICN in a very
complex application like vehicular communication systems; and (iv)
examine the possible drawbacks related to privacy and security issues
in complex networked environments.
2.12. Operation across Multiple Network Paradigms
Today the overwhelming majority of networks are integrated with the
well-connected Internet with IP at the "waist" of the technology
hourglass. However there is a large amount of ongoing research into
alternative paradigms that can cope with conditions other than the
standard set assumed by the Internet. Perhaps the most advanced of
these is Delay- and Disruption-Tolerant Networking (DTN). DTN is
considered as one of the scenarios for the deployment in
subection 2.9 but here we consider how ICN can operate in an
integrated network that has essentially disjoint "domains" (a highly-
overloaded term!) or regions that use different network paradigms and
technologies, but with gateways that allow interoperation.
ICN operates in terms of named data objects so that requests and
deliveries of information objects can be independent of the
networking paradigm. Some researchers have contemplated some form of
ICN becoming the new waist of the hourglass as the basis of a future
reincarnation of the Internet, e.g., [ArgICN], but there are a large
number of problems to resolve, including authorization and access
control (see subsection 4.2) and transactional operation for
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applications such as banking, before some form of ICN can be
considered as ready to take over from IP as the dominant networking
technology. In the meantime, ICN architectures will operate in
conjunction with existing network technologies as an overlay or in
cooperation with the lower layers of the "native" technology.
It seems likely that as the reach of the "Internet" is extended,
other technologies such as DTN will be needed to handle scenarios
such as space communications where inherent delays are too large for
TCP/IP to cope with effectively. Thus, demonstrating that ICN
architectures can work effectively in and across the boundaries of
different networking technologies will be important. The NetInf
architecture in particular targets the inter-domain scenario by the
use of a convergence layer architecture [SAIL-B3] and PSIRP/PURSUIT
is envisaged as a candidate for an IP replacement.
The key items for evaluation over and above the satisfactory
operation of the architecture in each constituent domain will be to
ensure that requests and responses can be carried across the network
boundaries with adequate performance and do not cause malfunctions in
applications or infrastructure because of the differing
characteristics of the gatewayed domains.
2.13. Summary
We conclude Section 2 with a brief summary of the evaluation aspects
we have seen across a range of scenarios.
The scalability of different mechanisms in an ICN architecture stands
out as an important concern (cf. subsections 2.1-2, 2.5-7, 2.10-11,)
as does network, resource and energy efficiency (cf. subsections 2.1,
2.3-4, 2.7-8). Operational aspects such as network planing,
manageability, reduced complexity and overhead (cf. subsection 2.2-4,
2.7, 2.10) should not be neglected especially as ICN architectures
are evaluated with respect to their potential for deployment in the
real world. Accordingly, further research in economic aspects as
well as in the communication, computation, and storage tradeoffs
entailed in each ICN architecture is needed.
With respect to purely technical requirements, support for multicast,
mobility, and caching lie at the core of many scenarios (cf.
subsections 2.1, 2.3, 2.5-6). ICN must also be able to cope when the
Internet expands to incorporate additional network paradigms (cf.
subsection 2.12). We have also seen that being able to address
stringent QoS requirements and increase reliability and resilience
should also be evaluated following well-established methods (cf.
subsections 2.2, 2.9-10).
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Finally, we note that new applications that significantly improve the
end user experience and forge a migration path from today's host-
centric paradigm could be the key to a sustained and increasing
deployment of the ICN paradigm in the real world (cf. subsections
2.2-3, 2.6, 2.10-11).
3. Evaluation Methodology
As we have seen in the previous section, different ICN approaches
have been evaluated in the peer-reviewed literature using a mixture
of theoretical analysis, simulation and emulation techniques, and
empirical (testbed) measurements. These are all popular methods for
evaluating network protocols, architectures, and services in the
networking community. Typically, researchers follow a specific
methodology based on the goal of their experiment, e.g., whether they
want to evaluate scalability, quantify resource utilization, analyze
economic incentives, and so on, as we have discussed earlier. In
addition, though, we observe that ease and convenience of setting up
and running experiments can sometimes be a factor in published
evaluations.
It is worth pointing out that for well-established protocols, such as
TCP, performance evaluation using actual network deployments has the
benefit of realistic workloads and reflects the environment where the
service or protocol will be deployed. However, results obtained in
this environment are often difficult to replicate independently.
Beyond this, the difficulty of deploying future Internet
architectures and then engaging sufficient users to make such
evaluation realistic is often prohibitive.
Moreover, for ICN in particular, it is not yet clear what qualifies
as a "realistic workload". As such, trace-based analysis of ICN is
in its infancy, and more work is needed towards defining
characteristic workloads for ICN evaluation studies. Accordingly,
the experimental process itself as well as the evaluation methodology
are being actively researched for ICN architectures. Numerous
factors affect the experimental results, including the topology
selected; the background traffic that an application is being
subjected to; network conditions such as available link capacities,
link delays, and loss-rate characteristics throughout the selected
topology; failure and disruption patterns; node mobility; as well as
other aspects such as the diversity of devices used, and so on, as we
explain in the remainder of this section.
Apart from the technical evaluation of the functionality of an ICN
architecture, its future success will be largely driven by its
deployability and economic viability. Thus evaluations should also
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include an assessment of incremental deployability in the existing
network environment together with a view of how the technical
functions will incentivize deployers to invest in the capabilities
that allow the architecture to spread across the network.
In this section, we present various techniques and considerations for
evaluating different ICN architectures. We do not intend to develop
a complete methodology or a benchmarking tool. Instead, this
document proposes key guidelines alongside suggested data sets and
high-level approaches that we expect to be of interest to ICNRG and
the ICN community as a whole. Through this, researchers and
practitioners alike would be able to compare and contrast different
ICN designs against each other, and identify the respective strengths
and weaknesses.
3.1. ICN Simulators and Testbeds
Since ICN is still an emerging area, the community is still in the
process of developing effective evaluation environments, including
simulators, emulators, and testbeds. To date, none of the available
evaluation methodologies can be seen as the one and only community
reference evaluation tool. Furthermore, no single environment
supports all well-known ICN approaches. Simulators and emulators
should be able to capture, faithfully, all features and operations of
the respective ICN architecture(s). It is also essential that these
tools and environments come with adequate logging facilities so that
one can use them for in-depth analysis as well as debugging.
Additional requirements include the ability to support mid- to large-
scale experiments, the ability to quickly and correctly set various
configurations and parameters, as well as to support the playback of
traffic traces captured on a real testbed or network. Obviously, this
does not even begin to touch upon the need for strong validation of
any evaluated implementations.
The rest of this subsection summarizes the ICN simulators and
testbeds currently available to the community.
3.1.1. CCN and NDN
The CCN project has open-sourced a software reference implementation
of the architecture and protocol called CCNx (www.ccnx.org). CCNx is
available for deployment on various operating systems and includes C
and Java libraries that can be used to build CCN applications. CCN-
lite (www.ccn-lite.net) is a lightweight implementation of the CCN
protocol, supports most of the key features of CCNx, and is
interoperable with CCNx. The core CCNx logic has been implemented in
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about 1000 lines of code and is ideal for classroom work and course
projects as well as for quickly experimenting with CCNx extensions.
ndnSIM [ndnSIM] is a module that can be plugged into the ns-3
simulator and supports the core features of CCN. One can use ndnSIM
to experiment with various CCN applications and services as well as
components developed for CCN such as routing protocols, caching and
forwarding strategies. The code for ns-3 and ndnSIM is openly
available to the community and can be used as the basis for
implementing ICN protocols or applications. For more details see
http://www.nsnam.org and http://www.ndnsim.net.
ccnSim [ccnSim] is another CCN-specific simulator that was specially
designed to handle forwarding of a large number of CCN-chunks.
ccnSim is written in C++ for the OMNeT++ simulation framework
(www.omnetpp.org). Interested readers could consider also the
Content Centric Networking Packet Level Simulator [CCNPL]. Finally,
CCN-Joker [CGB12] is an application-layer platform that can be used
to build a CCN overlay. CCN-Joker emulates in user-space all basic
aspects of a CCN node (e.g., handling of Interest and Data packets,
cache sizing, replacement policies), including both flow and
congestion control. The code is open source and is suitable for both
emulation-based analyses and real experiments.
An example of a testbed that supports CCN is the Open Network Lab
(see https://onl.wustl.edu/). The ONL testbed currently comprises 18
extensible gigabit routers and over 100 computers representing
clients and is freely available to the public for running CCN
experiments. Nodes in ONL are preloaded with CCNx software. ONL
provides a graphical user interface for easy configuration and
testbed set up as per the experiment requirements, and also serves as
a control mechanism, allowing access to various control variables and
traffic counters. It is also possible to run and evaluate CCN over
popular testbeds such as PlanetLab (www.planet-lab.org), Emulab
(www.emulab.net), and Deter (www.isi.deterlab.net) by directly
running the CCNx open-source code on PlanetLab and Deter nodes,
respectively.
NEPI, the Network Experimentation Programming Interface,
(http://nepi.inria.fr) is a tool developed for controlling and
managing large-scale network experiments. NEPI provides an
experiment description language to design network experiments,
describing topology, applications, and a controller to automatically
deploy those experiments on target experimentation environments, such
as PlanetLab. The controller is also capable of collecting result and
log files during the experiment execution. NEPI also allows to
specify node selection filters while designing the experiment,
thereby supporting automatic discovery and provisioning of testbed
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nodes during experiment deployment, without the user having to hand-
pick them. It is simple and efficient to use NEPI to evaluate CCNx
on large-scale testbeds such as PlanetLab.
3.1.2. PSI
The PSIRP project has open-sourced its Blackhawk publish-subscribe
(Pub/Sub) implementation for FreeBSD; more details are available
online at http://www.psirp.org/downloads.html. Despite being limited
to one operating system, the code base also provides a virtual image
to allow its deployment on other environments through virtualization.
The code distribution features a kernel module, a file system and
scope daemon, as well as a set of tools, test applications and
scripts. This work was extended as part of the PURSUIT project,
resulting in the development of the Blackadder prototype for Linux
and FreeBSD. It currently runs on a testbed across Europe, America
(MIT) and Japan (NICT). All sites are connected via OpenVPN, which
exports a virtual Ethernet device to all machines in the testbed. In
total, 40 machines in a graph topology containing one Topology
Manager and one Rendezvous node that handle all publish/subscribe and
topology formation requests are interconnected [PTA13].
Moreover, the ICN simulation environment [VBYR12] allows the
simulation of new techniques for topology management following the
Publish-Subscribe paradigm and the PSIRP approach. The simulator is
based on the OMNET++ simulator and the INET/MANET frameworks. It is
currently publicly available at
http://sourceforge.net/projects/icnsim. A design characteristic of
this platform is the separation between the network and topology
management policies. An interface is used to provide this
functionality and policies can be imported and applied in the network
as topology manager applications running on top of this interface.
3.1.3. NetInf
The EU FP7 4WARD and SAIL projects have made a set of open-source
implementations available; see http://www.netinf.org/open-source for
more details. Of note, two software packages are available. The
first one is a set of tools for NetInf implementing different aspects
of the protocol (e.g., NetInf URI format, HTTP and UDP convergence
layer) using different programming languages. The Java
implementation provides a local caching proxy and client. The second
one, is a OpenNetInf prototype from the 4WARD project. Besides a
rich set of NetInf mechanisms implemented, it also provides a browser
plug-in and video streaming software.
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The SAIL project developed a hybrid host-centric and information-
centric network architecture called the Global Information Network
(GIN). The prototype code for this can be downloaded from
http://gin.ngnet.it.
3.1.4. COMET
The EU FP7 COMET project developed a simulator, called Icarus, which
implements ProbCache [PCP12], centrality-based in-network caching
[CHPP12] and the hash-route-based algorithms detailed in [SPP13].
The simulator is built in Python and makes use of the Fast Network
Simulator Setup tool [SCP13] to configure the related parameters of
the simulation. The simulator is available from:
https://github.com/lorenzosaino/icarus/
3.1.5. Large-scale Testing
An important consideration in the evaluation of any kind of future
Internet mechanism, lies in the characteristics of that evaluation
itself. Often, central to the assessment of the features provided by
a novel mechanism, lies the consideration of how it improves over
already existing technologies, and by "how much." With the
disruptive nature of clean-slate approaches generating new and
different technological requirements, it is complex to provide
meaningful results for a network layer framework, in comparison with
what is deployed in the current Internet. Thus, despite the
availability of ICN implementations and simulators, the need for
large-scale environments supporting experimental evaluation of novel
research is of prime importance to the advancement of ICN deployment.
In this regard, initiatives such as the Future Internet Research and
Experimentation Initiative (www.ict-fire.eu), enable researchers to
test new protocols and architectures in real conditions over
production networks (e.g., through virtualization and software-
defined networking mechanisms), simplifying the validation of future
evolutions and reducing the gap between research and deployment.
Similarly, Future Internet Design (www.nets-find.net) is a long-term
initiative along the same direction in the US. GENI (www.geni.net)
also offers experimentation infrastructure as does PlanetLab
(www.planet-lab.org), which likely offers the largest testbed
available today. Those wishing to perform smaller, more controlled
experiments can also consider the Emulab testbed (www.emulab.net),
which allows various topologies to be configured.
Finally, the National Institute of Information and Communications
Technology (NICT) builds and operates the high-performance testbed
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JGN-X (see http://www.jgn.nict.go.jp/english/index.html), which has
cutting-edge network functions and technologies including those
currently in development. JGN-X aims to establish new-generation
network technology and accelerate the R&D in areas such as network
virtualization and advanced operations of virtualized layers. JGN-X
is used for collaboration among developers in order to foster the
establishment and expansion of new-generation network technology.
3.2. Topology Selection
Section 2 introduced several topologies that have been used in ICN
studies so far but, to date and to the best of our understanding,
there is no single topology that can be used to easily evaluate all
aspects of the ICN paradigm. There is rough consensus that the
classic dumbbell topology cannot serve well future evaluations of ICN
approaches. Therefore, one should consider a range of topologies,
each of which would stress different aspects, as outlined earlier in
this document. Current Internet traces are also available to assist
in this, e.g. see http://www.caida.org/data/active/internet-topology-
data-kit and
http://www.cs.washington.edu/research/networking/rocketfuel.
Depending on what is the focus of the evaluation, intra-domain
topologies alone may be appropriate. However, those interested, for
example, in quantifying transit costs will require inter-domain
traces (note that the above CAIDA traces offer this). Scalability is
an important aspect in such an evaluation. For instance, CAIDA's
ITDK traces record millions of routers across thousands of domains.
Beyond these "real-world" traces there is a wide range of synthetic
topologies, such as the Barabasi-Albert model [BA99] and the Watts-
Strogatz small-world topology [WS98]. These synthetic traces allow
experiments to be performed whilst controlling various key parameters
(e.g. degree). Through this, different aspects can be investigated,
such as inspecting resilience properties. For some lines of ICN
research, this may be more appropriate as, practically speaking,
there are no assurances that a future ICN will share the same
topology with today's networks.
Besides defining the evaluation topology as a graph G = (V,E), where
V is the set of vertices (nodes) and E is the set of edges (links),
one should also clearly define and list the respective matrices that
correspond to the network, storage and computation capacities
available at each node as well as the delay characteristics of each
link, so that the results obtained can be easily replicated in other
studies. Recent work by Hussain and Chen [Montage], although
currently addressing host-centric networks, could also be leveraged
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and be extended by the ICN community. Measurement information can
also be taken from existing platforms such as iPlane
(http://iplane.cs.washington.edu), which can be used to provide
configuration parameters such as access link capacity and delay.
Alternatively, synthetic models such as [KPLKTS09] can be used to
configure such topologies.
Finally, the dynamic aspects of a topology, such as node and content
mobility, disruption patterns, packet loss rates as well as link and
node failure rates, to name a few, should also be carefully
considered. As mentioned in subsection 2.9, for example, contact
traces from the DTN community could also be used in ICN evaluations.
3.3. Traffic Load
As we are still lacking ICN-specific traffic workloads we can
currently only extrapolate from today's workloads. In this
subsection we provide a first draft of a set of common guidelines, in
the form of what we will refer to as a content catalog for different
scenarios. This catalog, which is based on previously published
work, could be used to evaluate different ICN proposals, for example,
on routing, congestion control, and performance, and can be
considered as other kinds of ICN contributions emerge.
We take scenarios from today's Web, file sharing (BitTorrent-like)
and User Generated Content (UGC) platforms (e.g., YouTube), as well
as Video on Demand (VoD) services. Publicly available traces for
these include those available from web sites such as
http://mikel.tlm.unavarra.es/~mikel/bt_pam2004,
http://multiprobe.ewi.tudelft.nl/multiprobe.html,
http://an.kaist.ac.kr/traces/IMC2007.html, and
http://traces.cs.umass.edu/index.php/Network/Network.
The content catalog for each type of traffic can be characterized by
a specific set of parameters: the cardinality of the estimated
content catalog, the average size of the exchanged contents (either
chunks or entire named information objects), and the statistical
distribution that best reflect the popularity of objects and their
request frequency. Table I summarizes such as content catalog. With
this shared point of reference, the use of the same set of parameters
(depending on the scenario of interest) among researchers will be
eased, and different proposals could be compared on a common base.
Several previous studies have stated that Zipf's law is the discrete
distribution that best represents the request frequency in a number
of application scenarios, ranging from typical Web access to VoD
services. The key aspect of this distribution is that the frequency
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of a content request is inversely proportional to the rank of the
content itself, i.e., the smaller the rank, the higher the request
frequency. If we denote with M the content catalog cardinality and
with 1 <= i <= M the rank of the i-th most popular content, we can
express the probability of requesting the content with rank "i" as:
P(X=i) = ( 1/i^(alpha) ) / C, with C = SUM(1 / j^(alpha)), alpha > 0
where the sum is obtained considering all values of j, 1 <= j <= M.
Table I. Content Catalog
Traffic | Catalog | Mean Object Size | Popularity Distribution
Load | Size | [L4][L5][L7][L8] | [L3][L5][L6][L11][L12]
| [L1][L2]| [L9][L10] |
| [L3][L5]| |
==================================================================
Web | 10^12 | Chunk: 1-10 kB | Zipf with
| | | 0.64 <= alpha <= 0.83
------------------------------------------------------------------
File | 5x10^6 | Chunk: 250-4096 kB | Zipf with
sharing | | Object: ~800 MB | 0.75 <= alpha<= 0.82
------------------------------------------------------------------
UGC | 10^8 | Object: ~10 MB | Zipf, alpha >= 2
------------------------------------------------------------------
VoD | 10^4 | Object: ~100 MB | Zipf, 0.65 <= alpha <= 1
==================================================================
* UGC = User Generated Content ** VoD = Video on Demand
Further, a variation of the Zipf distribution, termed the Mandelbrot-
Zipf distribution, has been suggested by [SH06] to better model
environments where nodes can locally store previously requested
content. For example, it was observed that peer-to-peer file sharing
applications typically exhibited a 'fetch-at-most-once' style of
behavior. This is because peers tend to persistently store the files
they download, a behavior that may also be prevalent in ICN.
3.4. Choosing Relevant Metrics
ICN is a networking concept that spun out of the desire to align the
operation model of a network with the model of its typical use. For
TCP/IP networks, this means a fundamental change in data access and
transport mechanisms from a host-to-host model to a user-to-
information model. The premise is that the effort invested in
changing models will be offset, or even surpassed, by the potential
of a "better" network. However, such a claim can be validated only
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if it is quantified.
Quantification of network performance requires a set of standard
metrics. These metrics should be broad enough so that they can be
applied equally to host-centric and information-centric (or other)
networks. This will allow reasoning about a certain ICN approach in
relation to an earlier version of the same approach, to a different
ICN approach, and to the incumbent host-centric approach. It will
therefore be less difficult to gauge optimization and research
direction. On the other hand, metrics should be targeted to network
performance primarily and should avoid unnecessary expansion into the
physical and application layers. Similarly, at this point, it is
more important to capture as metrics only the main figures of merit
and to leave more esoteric and less frequent cases for the future.
To arrive at a set of relevant metrics, it would be beneficial to
look at the metrics considered in previously published evaluations
for several ICN approaches, such as CCN [CCN] [VoCCN] [NDNProj];
NetInf [4WARD6.1] [4WARD6.3] [SAIL-B2] [SAIL-B3]; PURSUIT [PRST4.5],
COMET [CMT-D5.2] [CMT-D6.2]; Connect [MCG11] [RealCCN]; and
CONVERGENCE [ICN-Web] [ICN-Scal] [ICN-Tran]. The metrics used in
these studies fall into two categories: metrics for the approach as a
whole, and metrics for individual components (resolution, routing,
etc.). Metrics for the entire approach are further subdivided into
traffic and system metrics.
It is important to note that sometimes ICN approaches do not name or
define metrics consistently. This is a major problem when trying to
find metrics that allow comparison between approaches. For the
purposes of exposition, in what follows we have tried to smooth
differences by pitting similarly defined metrics under the same name.
Also, due to space constraints, we have chosen to report here only
the most common metrics between approaches. For more details the
reader should consult the references provided in this document for
each approach.
Traffic metrics in existing ICN approaches are summarized in Table
II. These metrics capture mainly the perspective of the end user,
i.e., the consumer, provider, or owner of the content or service.
Depending on the level where these metrics are measured, we have made
the distinction into user, application and network-level traffic
metrics. So, for example, network-level metrics are mostly focused on
packet characteristics, whereas user-level metrics can cover elements
of human perception. The approaches do not make this distinction
explicitly, but we can see from the table that CCN and NetInf have
used metrics from all levels, PURSUIT and COMET have focused on
lower-level metrics, and Connect and CONVERGENCE prefer higher-level
metrics. Throughput and download time seem to be the most popular
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metrics altogether.
Table II. Traffic metrics used in ICN evaluation studies
User | Application | Network
======================================================
Download | Goodput | Startup | Throughput | Packet
time | | latency | | delay
==================================================================
CCN | x | x | | x | x
------------------------------------------------------------------
NetInf | x | | x | x | x
------------------------------------------------------------------
PURSUIT | | | x | x | x
------------------------------------------------------------------
COMET | | | x | x |
------------------------------------------------------------------
Connect | x | | | |
------------------------------------------------------------------
CONVERGENCE | x | x | | |
==================================================================
While traffic metrics are more important for the end user, the owner
or operator of the networking infrastructure is normally more
interested in system metrics, which can reveal the efficiency of an
approach. Although different ICN approaches have used system metrics
in their respective evaluation studies, the situation is not as
coherent as with the traffic metrics. The most common system metrics
used are: protocol overhead, total traffic, transit traffic, cost
savings, router cost, and router energy consumption.
Besides traffic and systems metrics that aim to evaluate an approach
as a whole, all of the surveyed approaches also evaluate the
performance of individual components. Name resolution, request/data
routing, and data caching are the most typical components, so Table
III presents the popular metrics for each of those components. FIB
size and path length, i.e., the routing component metrics, are almost
ubiquitous between different studies, perhaps due to the networking
background of the involved researchers. That might be also the
reason for the sometimes decreased focus on traffic and system
metrics, in favor of component metrics. It can certainly be argued
that traffic and system metrics are affected by component metrics,
however no approach has made the relationship clear. With this in
mind, and also taking into account that traffic and system metrics
are readily useful to end users and network operators, we will
restrict ourselves to those in the following sections.
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Table III. Component metrics in current ICN evaluations
Resolution | Routing | Cache
======================================================
Resolution | Request | FIB | Path | Size | Hit
time | rate | size | length | | ratio
==================================================================
CCN | x | | x | x | x | x
------------------------------------------------------------------
NetInf | x | x | | x | | x
------------------------------------------------------------------
PURSUIT | | | x | x | |
------------------------------------------------------------------
COMET | x | x | x | x | | x
------------------------------------------------------------------
CONVERGENCE | | x | x | | x |
==================================================================
Before proceeding, we should note that we would like our metrics to
be applicable to host-centric networks as well. Standard metrics
already exist for IP networks and it would certainly be beneficial to
take them into account. It is encouraging that many of the metrics
used by existing ICN approaches can also be used on IP networks and
that all of the approaches have tried on occasion to draw the
parallels.
3.4.1. Traffic Metrics
At their core, host-centric and information-centric networking
function as data transport services. Information of interest to a
user resides in one or more storage points connected to the network
and, on the user's request, the network transports this information
to the user for consumption. We could therefore quantify the data
transport performance of the network in terms of well-established
Quality of Service (QoS) metrics.
The IETF has been working for more than a decade on devising metrics
and methods for measuring the performance of IP networks. The work
has been carried out largely within the IPPM WG, guided by a relevant
framework [RFC2330]. IPPM metrics include delay, delay variation,
loss, reordering, and duplication. While the IPPM work is certainly
based on packet-switched IP networks, it is conceivable that it can
be modified and extended to cover ICN networks as well. However,
more study is necessary to turn this claim into a certainty. Many
experts have toiled for a long time on devising and refining the IPPM
metrics and methods, so it would be an advantage to use IPPM on
measuring ICN performance. In addition, IPPM works already for host-
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centric networks, so comparison with information-centric networks
would entail only the ICN extension of the IPPM framework. Finally,
an important benefit of measuring the transport performance of a
network at its output, using QoS metrics such as IPPM, is that it can
be done mostly without any dependence on particular applications.
Another option for measuring transport performance would be to use
QoS metrics, not at the output of the network like with IPPM, but at
the input to the application. So for an application like live video
streaming, the relevant metrics would be startup latency, playout lag
and playout continuity. The benefit of this approach is that it
abstracts away all details of the underlying transport network, so it
can be readily applied to compare networks of different architecture
(host-centric, information-centric, or other). As implied earlier,
the drawback of this approach is its dependence on the application,
so it is likely that different (types of) applications will require
different metrics. It might be possible to identify standard metrics
for each type of application, but the situation is not as clear as
with IPPM metrics and further investigation is necessary.
At a higher level of abstraction, we could measure the network's
transport performance at the application output. This entails
measuring the quality of the transported and reconstructed
information as perceived by the user during consumption. In such an
instance we would use Quality of Experience (QoE) metrics, which are
by definition dependent on the application. For example, the
standardized methods for obtaining a Mean Opinion Score (MOS) for
VoIP (e.g., ITU-T P.800) is quite different from those for IPTV
(e.g., PEVQ). These methods are notoriously hard to implement, as
they involve real users in a controlled environment. Such
constraints can be relaxed or dropped by using methods that model
human perception under certain environments, but these methods are
typically intrusive. The most important drawback of measuring
network performance at the output of the application is that only one
part of each measurement is related to network performance. The rest
is related to application performance, e.g., video coding, or even
device capabilities, both of which are irrelevant to our purposes
here and are generally hard to separate. We therefore see the use of
QoE metrics in measuring ICN performance as a poor choice at this
stage.
3.4.2. System Metrics
Overall system metrics that need to be considered include
reliability, scalability, energy efficiency, and delay/disconnection
tolerance. In deployments where ICN is addressing specific
scenarios, relevant system metrics could be derived from current
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experience. For example, in IoT scenarios, which were discussed
earlier in subsection 2.10, it is reasonable to consider the current
generation of sensor nodes, sources of information, and even
measurement gateways (e.g., for smart metering at homes) or
smartphones. In this case, ICN operation ought to be evaluated with
respect not only to overall scalability and network efficiency, but
also the impact on the nodes themselves. Karnouskos et al. [KVHHM11]
provide a comprehensive set of sensor and IoT-related requirements,
for example, which include aspects such as resource utilization,
service life-cycle management and device management.
Additionally, various specific metrics are critical in constrained
environments, such as CPU processing requirements, signaling
overhead, and memory allocation for caching procedures in addition to
power consumption and battery lifetime. For gateway nodes, which are
typically a point of service to a large number of nodes and have to
satisfy the information requests from remote entities, we need to
consider scalability-related metrics, such as frequency of requests
received and processing of successfully satisfied information
requests.
Finally, given the in-network caching functionality in ICN, metrics
for the efficiency and performance of in-network caching have to be
defined, which can quantify the performance of in-network caching
algorithms. A first step on this direction has been made in [L9].
The paper proposes a formula that approximates the proportion of time
that a data object is stored in a network cache. The model takes as
input the rate of requests for a given content, named the Content of
Interest (CoI), and the rate of requests for all other data objects
that go through the given network element (router) and move the CoI
down in the (LRU) cache. The formula takes also into account the
size of the cache of this router.
The output of the model essentially reflects the probability that the
CoI will be found in a given cache. The initial study [L9] is
applied to the CCN/NDN framework, where contents get cached at every
node they traverse, while efforts are underway to assess the accuracy
of the model for other caching strategies. The formula according to
which the probability or proportion is calculated is given by:
pi = [mu/(mu+lambda)]^N,
where lambda is the request rate for CoI, mu is the request rate for
contents that move CoI down the cache, and N is the size of the cache
(in slots).
The formula can be used to assess the caching performance of the
system and can also potentially be used to identify the gain of the
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system due to caching. This can then be used to compare against
gains by other factors, e.g., addition of extra bandwidth in the
network.
3.5. Resource Equivalence and Tradeoffs
As we have seen above, every information-centric network is built
from a set of resources, which include link capacities, different
types of memory structures and repositories used for storing named
information objects and chunks temporarily (i.e. caching) or
persistently, as well as name resolution and other lookup services.
Complexity and processing needs in terms of forwarding decisions,
management (e.g., need for manual configuration, explicit garbage
collection, and so on), and routing (i.e., amount of state needed,
need for manual configuration of routing tables, support for
mobility, etc.) set the stage for a range of engineering tradeoffs.
In order to be able to compare different ICN approaches it would be
beneficial to to define equivalence in terms of different resources
which today are considered incomparable. For example, would
provisioning an additional 5 Mb/s link capacity lead to better
performance than adding 100 GB of in-network storage? Within this
context one would consider resource equivalence (and the associated
tradeoffs) for example for cache hit ratios per GB of cache,
forwarding decision times, CPU cycles per forwarding decision, and so
on.
3.6. Technology Evolution Assumptions
TBD
4. Security Considerations
The introduction of an information-centric networking architecture
and the corresponding communication paradigm changes many aspects of
network security. Additional evaluation will be required to ensure
relevant security requirements are appropriately met by the
implementation of the chosen architecture in the scenarios presented
in Section 2.
The various ICN architectures that are currently proposed have
concentrated on authentication of delivered content to ensure content
integrity. However the approaches are primarily applicable to freely
accessible content that does not require access authorization,
although they will generally support delivery of encrypted content.
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The introduction of widespread caching mechanisms may also provide
additional attack surfaces. The caching architecture to be used also
needs to be evaluated to ensure that it meets the requirements of the
usage scenarios.
In practice, the work on security in the various ICN research
projects has been heavily concentrated on authentication of content.
In general, work on authorization, access control, privacy and
security threats due to the expanded role of in-network caches has
been quite limited. A roadmap for improving the security model in
NetInf can be found in [RAA09]. In the rest of this section we
briefly consider the issues at hand and provide pointers to the work
that has been done on the security aspects of the architectures
proposed.
4.1. Authentication
For fully secure content distribution, content access requires that
the receiver needs to be able to reliably assess validity,
provenance, and relevance. Validity relates to whether the received
data object is a complete, uncorrupted copy of what was originally
published. Provenance means that the receiver can identify the
publisher, and, if so, whether it and the source of any cached
version of the document can be adequately trusted. Relevance
ascertains that the content obtained answers the question that the
receiver asked.
All ICN architectures considered in this document primarily target
the validity requirement using strong cryptographic means to tie the
content request name to the content. Provenance and relevance are
directly targeted to varying extents: There is a tussle or trade-off
between simplicity and efficiency of access and level of assurance of
all these traits. For example, maintaining provenance information
can become extremely costly, particularly when considering (historic)
relationships between multiple objects. Architectural decisions have
therefore been taken in each case as to whether the assessment is
carried out by the ICN or left to the application.
An additional consideration for authentication is whether a name
should be irrevocably and immutably tied to a static piece of
preexisting content or whether the name can be used to refer to
dynamically or subsequently generated content. Schemes that only
target immutable content can be less resource hungry as they can use
digest functions rather than public key cryptography for generating
and checking signatures. However, this can increase the load on
applications as they are required to manage many names, rather than
using a single name for an item of evolving content that changes over
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time (e.g. a piece of data containing an age reference).
NetInf, for instance, uses the Named Information (ni) URI scheme
[RFC6920] to identify content. This allows NetInf to assure validity
without any additional information but gives no assurance on
provenance or relevance. A "search" request allows an application to
identify relevant content. Applications may choose to structure
content to allow provenance assurance but this will typically require
additional network access. NetInf validity authentication is
consequently efficient in a network environment with intermittent
connectivity as it does not force additional network accesses and
allows the application to decide on provenance validation if
required. NetInf primarily targets static content, but an extension
would allow dynamic content to be handled. The immutable case only
uses digest functions.
DONA [DONA] and CCN [CCN] [SJ09] integrate most of the data needed to
verify provenance into all content retrievals but need to be able to
retrieve additional information (typically a security certificate) in
order to complete the provenance authentication. Whether the
application has any control of this extra retrieval will depend on
the implementation. CCN is explicitly designed to handle dynamic
content allowing names to be pre-allocated and attached to
subsequently generated content. DONA offers variants for dynamic and
immutable content.
PSI allows the authentication mechanism to be chosen so that it can,
in theory, adopt the authentication strategy of any other other
architectures [PRST2.4]. In the light of such choices, however,
interoperability (if required by the chosen deployment) needs to be
taken care of through dedicated solutions.
4.2. Authorization, Access Control and Statistics
A potentially major concern for all ICN architectures considered here
is that they do not provide any inbuilt support for an authorization
framework or for statistics monitoring. Once content has been
published and cached in servers, routers or end points not controlled
by the publisher, the publisher has no way to enforce access control,
determine which users have accessed the content or revoke its
publication. In fact, in some cases, it is even difficult for the
publishers themselves to perform access control, where requests do
not necessarily contain host/user identifier information.
Access could be limited by encrypting the content but the necessity
of distributing keys out-of-band appears to negate the advantages of
in-network caching. This also creates significant challenges when
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attempting to manage and restrict key access. An authorization
delegation scheme has been proposed in [FMP12] but this requires
access to a server controlled by the publisher to obtain an access
token making it essentially just an out-of-band key distribution
system.
Evaluating the impact of the absence of these features will be
essential for any scenario where an ICN architecture might be
deployed. It may have a seriously negative impact on the
applicability of ICN in commercial environments unless a solution can
be found.
4.3. Privacy
Another area where the architectures have not been significantly
analyzed is privacy. Caching implies a trade-off between network
efficiency and privacy. The activity of users is significantly more
exposed to the scrutiny of cache owners with whom they may not have
any relationship.
Although in many ICN architectures, the source of a request is not
explicitly identified, an attacker may be able to obtain considerable
information if s/he can monitor transactions on the cache and obtain
details of the objects accessed, the topological direction of
requests and information about the timing of transactions. The
persistence of data in the cache can make life easier for an attacker
by giving a longer timescale for analysis.
The impact of CCN on privacy has been investigated in [Lau10]. The
analysis in this master's thesis is to a large degree applicable to
all of ICN architectures because it is mostly focused on the common
caching aspect. The privacy risks of named data networking are also
highlighted in [LLRSBK12]. Further work on privacy in ICNs can be
found in [CDKU12].
4.4. Changes to the Network Security Threat Model
The architectural differences of the various ICN models as compared
to TCP/IP have consequences for network security. There is limited
consideration of the threat models and potential mitigation in the
various documents describing the architectures. The changed threat
model is also discussed in [Lau10] and [CDKU12]. Some of the key
aspects are:
o Caching implies a tradeoff between network efficiency and user
privacy as discussed in subsection 4.3.
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o More powerful routers upgraded to handle persistent caching
increase the network's attack surface. This is particularly the
case in systems (e.g., CCN) that may need to perform cryptographic
checks on content that is being cached. For example, not doing
this could lead routers to disseminate invalid content.
o ICN makes it difficult to identify the origin of a request as
mentioned in subsection 4.3, slowing down the process of blocking
requests and requiring alternative mechanisms to differentiate
legitimate requests from inappropriate ones, as access control
lists (ACLs) will probably be of little value for ICN requests.
o Denial-of-service (DoS) attacks may require more effort on ICN
than in TCP/IP, but they are still feasible. One reason for this
is that it is difficult for the attacker to force repeated
requests for the same content onto a single node. Information-
centric networks naturally spread content so that after the
initial few requests, subsequent requests will generally be
satisfied by alternative sources, blunting the impact of a DoS
attack. That said, there are many ways around this, e.g.,
generating random suffix identifiers that always result in cache
misses.
o Per-request state in routers can be abused for DoS attacks.
o Caches can be misused in the following ways:
+ Attackers can use caches as storage to make their own content
available.
+ The efficiency of caches can be decreased by attackers with the
goal of DoS attacks.
+ Content can be extracted by any attacker connected to the
cache, putting users' privacy at risk.
Appropriate mitigation of these threats will need to be considered in
each scenario.
5. IANA Considerations
This document presents no IANA considerations.
6. Acknowledgments
This document has benefited from pointers to the growing ICN
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literature, suggestions, comments and proposed text provided by the
following members of the IRTF Information-Centric Networking Research
Group (ICNRG), listed in alphabetical order: Marica Amadeo, Hitoshi
Asaeda, Claudia Campolo, Luigi Alfredo Grieco, Myeong-Wuk Jang, Ren
Jing, Will Liu, Ioannis Psaras, Dirk Trossen, Jianping Wang, Yuanzhe
Xuan, and Xinwen Zhang.
7. Informative References
[RFC4838] Cerf, V., Burleigh, S., Hooke, A., Torgerson, L., Durst,
R., Scott, K., Fall, K., and H. Weiss, "Delay-Tolerant
Networking Architecture", RFC 4838, April 2007.
[RFC6920] Farrell, S., Kutscher, D., Dannewitz, C., Ohlman, B.,
Keranen, A., and P. Hallam-Baker, "Naming Things with
Hashes", RFC 6920, April 2013.
[RFC2330] Paxson, V., Almes, G., Mahdavi, J., and M. Mathis,
"Framework for IP Performance Metrics", RFC 2330, May
1998.
[RFC5050] Scott, K. and S. Burleigh, "Bundle Protocol
Specification", RFC 5050, November 2007.
[ndnSIM] Afanasyev, A., Moiseenko, I., and L. Zhang, "ndnSIM: NDN
simulator for NS-3", NDN Technical Report NDN-0005,
Revision 2, October 2012.
[NetInf] Ahlgren, B., D'Ambrosio, M., Marchisio, M., Marsh, I.,
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Authors' Addresses
Kostas Pentikousis (editor)
Huawei Technologies
Carnotstrasse 4
10587 Berlin
Germany
Email: k.pentikousis@huawei.com
Pentikousis, et al. Expires January 16, 2014 [Page 62]
INTERNET DRAFT ICN Baseline Scenarios July 15, 2013
Borje Ohlman
Ericsson Research
S-16480 Stockholm
Sweden
Email: Borje.Ohlman@ericsson.com
Daniel Corujo
Instituto de Telecomunicacoes
Campus Universitario de Santiago
P-3810-193 Aveiro
Portugal
Email: dcorujo@av.it.pt
Gennaro Boggia
Dep. of Electrical and Information Engineering
Politecnico di Bari
Via Orabona 4
70125 Bari
Italy
Email: g.boggia@poliba.it
Gareth Tyson
School and Electronic Engineering and Computer Science
Queen Mary, University of London
United Kingdom
Email: gareth.tyson@eecs.qmul.ac.uk
Elwyn Davies
Trinity College Dublin/Folly Consulting Ltd
Dublin, 2
Ireland
Email: davieseb@scss.tcd.ie
Priya Mahadevan
Palo Alto Research Center
3333 Coyote Hill Rd
Palo Alto, CA 94304
USA
Pentikousis, et al. Expires January 16, 2014 [Page 63]
INTERNET DRAFT ICN Baseline Scenarios July 15, 2013
Email: Priya.Mahadevan@parc.com
Spiros Spirou
Intracom Telecom
19.7 km Markopoulou Avenue
19002 Peania, Athens
Greece
Email: spis@intracom.com
Antonella Molinaro
Dep. of Information, Infrastructures, and Sustainable
Energy Engineering
Universita' Mediterranea di Reggio Calabria
Via Graziella 1
89100 Reggio Calabria
Italy
Email: antonella.molinaro@unirc.it
Dorothy Gellert
InterDigital Communications, LLC
781 Third Avenue
King Of Prussia, PA 19406-1409
USA
Email: dorothy.gellert@interdigital.com
Suyong Eum
National Institute of Information and Communications Technology
4-2-1, Nukui Kitamachi, Koganei
Tokyo 184-8795
Japan
Phone: +81-42-327-6582
Email: suyong@nict.go.jp
Pentikousis, et al. Expires January 16, 2014 [Page 64]
