Internet DRAFT - draft-irtf-coinrg-use-cases
draft-irtf-coinrg-use-cases
COINRG I. Kunze
Internet-Draft K. Wehrle
Intended status: Informational RWTH Aachen
Expires: 26 August 2024 D. Trossen
Huawei
M. J. Montpetit
McGill
X. de Foy
InterDigital Communications, LLC
D. Griffin
M. Rio
UCL
23 February 2024
Use Cases for In-Network Computing
draft-irtf-coinrg-use-cases-05
Abstract
Computing in the Network (COIN) comes with the prospect of deploying
processing functionality on networking devices, such as switches and
network interface cards. While such functionality can be beneficial,
it has to be carefully placed into the context of the general
Internet communication and it needs to be clearly identified where
and how those benefits apply.
This document presents some use cases to demonstrate how a number of
salient COIN-related applications can benefit from COIN.
Furthermore, to guide research on COIN, it further identifies
essential research questions and outlines desirable capabilities that
COIN systems addressing the use cases may need to support. It is a
product of the Computing in the Network Research Group (COINRG). It
is not an IETF product and it is not a standard.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
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Internet-Drafts are draft documents valid for a maximum of six months
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This Internet-Draft will expire on 26 August 2024.
Copyright Notice
Copyright (c) 2024 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Providing New COIN Experiences . . . . . . . . . . . . . . . 4
3.1. Mobile Application Offloading . . . . . . . . . . . . . . 4
3.2. Extended Reality and Immersive Media . . . . . . . . . . 11
3.3. Personalized and interactive performing arts . . . . . . 16
4. Supporting new COIN Systems . . . . . . . . . . . . . . . . . 20
4.1. In-Network Control / Time-sensitive applications . . . . 20
4.2. Large Volume Applications . . . . . . . . . . . . . . . . 22
4.3. Industrial Safety . . . . . . . . . . . . . . . . . . . . 26
5. Improving existing COIN capabilities . . . . . . . . . . . . 27
5.1. Content Delivery Networks . . . . . . . . . . . . . . . . 27
5.2. Compute-Fabric-as-a-Service (CFaaS) . . . . . . . . . . . 30
5.3. Virtual Networks Programming . . . . . . . . . . . . . . 33
6. Enabling new COIN capabilities . . . . . . . . . . . . . . . 37
6.1. Distributed AI Training . . . . . . . . . . . . . . . . . 37
7. Security Considerations . . . . . . . . . . . . . . . . . . . 39
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 41
9. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 41
10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 41
11. Informative References . . . . . . . . . . . . . . . . . . . 42
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 46
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1. Introduction
The Internet was designed as a best-effort packet network, forwarding
packets from source to destination with limited guarantees regarding
their timely and successful reception. Data manipulation,
computation, and more complex protocol functionality is generally
provided by the end-hosts while network nodes are kept simple and
only offer a "store and forward" packet facility. This simplicity of
purpose of the network has shown to be suitable for a wide variety of
applications and has facilitated the rapid growth of the Internet.
However, with the rise of new services, some of which are described
in this document, there are more and more application domains that
require more than best-effort forwarding including strict performance
guarantees or closed-loop integration to manage data flows. In this
context, allowing for a tighter integration of computing and
networking resources for enabling a more flexible distribution of
computation tasks across the network, e.g., beyond 'just' endpoints,
may help to achieve the desired guarantees and behaviors, increase
overall performance, and improve resilience to failures.
The vision of 'in-network computing' and the provisioning of such
capabilities that capitalize on joint computation and communication
resource usage throughout the network is part of the move from a
telephone network analogy of the Internet into a more distributed
computer board architecture. We refer to those capabilities as 'COIN
capabilities' in the remainder of the document.
We believe that this vision of 'in-network computing' can be best
outlined along four dimensions of use cases, namely those that (i)
provide new user experiences through the utilization of COIN
capabilities (referred to as 'COIN experiences'), (ii) enable new
COIN systems, e.g., through new interactions between communication
and compute providers, (iii) improve on already existing COIN
capabilities, and (iv) enable new COIN capabilities. Sections 3
through 6 capture those categories of use cases and provide the main
structure of this document. The goal is to present how computing
resources inside the network impact existing services and
applications or allow for innovation in emerging application domains.
By delving into some individual examples within each of the above
categories, we outline opportunities and propose possible research
questions for consideration by the wider community when pushing
forward 'in-network computing' architectures. Furthermore,
identifying desirable capabilities for an evolving solution space of
COIN systems is another objective of the use case descriptions. To
achieve this, the following taxonomy is proposed to describe each of
the use cases:
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1. Description: High-level presentation of the purpose of the use
case and a short explanation of the use case behavior.
2. Characterization: Explanation of the services that are being
utilized and realized as well as the semantics of interactions in
the use case.
3. Existing solutions: Description of current methods that may
realize the use case (if they exist), not claiming to
exhaustively review the landscape of solutions.
4. Opportunities: An outline of how COIN capabilities may support or
improve on the use case in terms of performance and other
metrics.
5. Research questions: Essential questions that are suitable for
guiding research to achieve the identified opportunities.
6. Desirable capabilities: Description of capabilities for any COIN
solutions that may need development along the opportunities
outlined in item 4; we limit the capabilities to those directly
affecting COIN, recognizing that any use case will realistically
require many additional capabilities for its realization.
This document discusses these six aspects along a number of
individual use cases to demonstrate the diversity of COIN
applications. It is intended as a basis for further analyses and
discussions within the Computing in the Network Research Group
(COINRG) and the wider research community. A companion document
[USECASEANALYSIS] is tasked with performing a cross-use case
analysis, i.e., summarizing and distilling the key research questions
across all use cases. This document represents the consensus of
COINRG.
2. Terminology
This document uses the terminology outlined in [TERMINOLOGY].
3. Providing New COIN Experiences
3.1. Mobile Application Offloading
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3.1.1. Description
This scenario can be exemplified in an immersive gaming application,
where a single user plays a game using a Virtual Reality (VR)
headset.
The headset hosts several (COIN) programs. For instance, the
"display" (COIN) program renders frames to the user, while other
programs are realized for VR content processing and to incorporate
input data received from sensors, e.g., in bodily worn devices
including the VR headset.
Once this application is partitioned into its constituent (COIN)
programs and deployed throughout a COIN system, utilizing the COIN
execution environment, only the "display" (COIN) program may be left
in the headset, while the compute intensive real-time VR content
processing (COIN) program can be offloaded to a nearby resource rich
home PC or a programmable network device (PND, see [TERMINOLOGY]) in
the operator's access network, for a better execution (faster and
possibly higher resolution generation).
3.1.2. Characterization
Partitioning a mobile application into several constituent (COIN)
programs allows for denoting the application as a collection of
(COIN) programs for a flexible composition and a distributed
execution. In our example above, most capabilities of a mobile
application can be categorized into any of three, "receiving",
"processing", and "displaying" groups.
Any device may realize one or more of the (COIN) programs of a mobile
application and expose them to the (COIN) system and its constituent
(COIN) execution environments. When the (COIN) program sequence is
executed on a single device, the outcome is what you see today as
applications running on mobile devices.
However, the execution of a (COIN) program may be moved to other
(e.g., more suitable) devices, including PNDs, which have exposed the
corresponding (COIN) program as individual (COIN) program instances
to the (COIN) system by means of a 'service identifier'. The result
is the equivalent to 'mobile function offloading', for possible
reduction of power consumption (e.g., offloading CPU intensive
process functions to a remote server) or for improved end user
experience (e.g., moving display functions to a nearby smart TV) by
selecting more suitably placed (COIN) program instances in the
overall (COIN) system.
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Figure 1 shows one realization of the above scenario, where a 'DPR
app' is running on a mobile device (containing the partitioned
Display(D), Process(P) and Receive(R) COIN programs) over a
programmable switching, e.g., here an SDN, network. The packaged
applications are made available through a localized 'playstore
server'. The mobile application installation is realized as a
'service deployment' process, combining the local app installation
with a distributed deployment (and orchestration) of one or more
(COIN) programs on most suitable end systems or PNDs ('processing
server').
+----------+ Processing Server
Mobile | +------+ |
+---------+ | | P | |
| App | | +------+ |
| +-----+ | | +------+ |
| |D|P|R| | | | SR | |
| +-----+ | | +------+ | Internet
| +-----+ | +----------+ /
| | SR | | | /
| +-----+ | +----------+ +------+
+---------+ /|SDN Switch|_____|Border|
+-------+ / +----------+ | SR |
| 5GAN |/ | +------+
+-------+ |
+---------+ |
|+-------+| +----------+
||Display|| /|SDN Switch|
|+-------+| +-------+ / +----------+
|+-------+| /|WIFI AP|/
|| D || / +-------+ +--+
|+-------+|/ |SR|
|+-------+| /+--+
|| SR || +---------+
|+-------+| |Playstore|
+---------+ | Server |
TV +---------+
Figure 1: Application Function Offloading Example.
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Such localized deployment could, for instance, be provided by a
visiting site, such as a hotel or a theme park. Once the
'processing' (COIN) program is terminated on the mobile device, the
'service routing' (SR) elements in the network route (service)
requests instead to the (previously deployed) 'processing' (COIN)
program running on the processing server over an existing SDN
network. Here, capabilities and other constraints for selecting the
appropriate (COIN) program, in case of having deployed more than one,
may be provided both in the advertisement of the (COIN) program and
the service request itself.
As an extension to the above scenarios, we can also envision that
content from one processing (COIN) program may be distributed to more
than one display (COIN) program, e.g., for multi/many-viewing
scenarios. Here, an offloaded "processing" program may collate input
from several users in the (virtual) environment to generate a
possibly three-dimensional render that is then distributed via a
service-level multicast capability towards more than one "display"
(COIN) program.
3.1.3. Existing Solutions
The ETSI Mobile Edge Computing (MEC) [ETSI] suite of technologies
provides solutions for mobile function offloading by allowing mobile
applications to select resources in edge devices to execute functions
instead of the mobile device directly. For this, ETSI MEC utilizes a
set of interfaces for the selection of suitable edge resources,
connecting to so-called MEC application servers, while also allowing
for sending data for function execution to the application server.
However, the technologies do not utilize micro-services
[Microservices] but mainly rely on virtualization approaches such as
containers or virtual machines, thus requiring a heavier processing
and memory footprint in the COIN execution environment and the
executing intermediaries. Also, the ETSI work does not allow for the
dynamic selection and redirection of (COIN) program calls to varying
edge resources rather than a single MEC application server.
Also, the selection of the edge resource (the app server) is
relatively static, relying on DNS-based endpoint selection, which
does not cater to the requirements of the example provided above,
where the latency for redirecting to another device lies within few
milliseconds for aligning with the framerate of the display micro-
service.
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Lastly, MEC application servers are usually considered resources
provided by the network operator through its MEC infrastructure,
while our use case here also foresees the placement and execution of
micro-services in end user devices.
There also exists a plethora of mobile offloading platforms provided
through proprietary platforms, all of which follow a similar approach
as ETSI MEC in that a selected edge application server is being
utilized to send functional descriptions and data for execution.
The draft at [APPCENTRES] outlines a number of enabling technologies
for the use case, some of which have been realized in an Android-
based realization of the micro-services as a single application,
which is capable to dynamically redirect traffic to other micro-
service instances in the network. This capability, together with the
underlying path-based forwarding capability (using SDN) was
demonstrated publicly, e.g., at the Mobile World Congress 2018 and
2019.
3.1.4. Opportunities
* The packaging of (COIN) programs into existing mobile application
packaging may enable the migration from current (mobile) device-
centric execution of those mobile applications toward a possible
distributed execution of the constituent (COIN) programs that are
part of the overall mobile application.
* The orchestration for deploying (COIN) program instances in
specific end systems and PNDs alike may open up the possibility
for localized infrastructure owners, such as hotels or venue
owners, to offer their compute capabilities to their visitors for
improved or even site-specific experiences.
* The execution of (current mobile) app-level (COIN) programs may
speed up the execution of said (COIN) program by relocating the
execution to more suitable devices, including PNDs that may reside
better located in relation to other (COIN) programs and thus
improve performance, such as latency.
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* The support for service-level routing of requests (service routing
in [APPCENTRES] may support higher flexibility when switching from
one (COIN) program instance to another, e.g., due to changing
constraints for selecting the new (COIN) program instance. Here,
PNDs may support service routing solutions by acting as routing
overlay nodes to implement the necessary additional lookup
functionality and also possibly support the handling of affinity
relations, i.e., the forwarding of one packet to the destination
of a previous one due to a higher level service relation, as
discussed and described in [SarNet2021].
* The ability to identify service-level COIN elements will allow for
routing service requests to those COIN elements, including PNDs,
therefore possibly allowing for new COIN functionality to be
included in the mobile application.
* The support for constraint-based selection of a specific (COIN)
program instance over others (constraint-based routing in
[APPCENTRES], showcased for PNDs in [SarNet2021]) may allow for a
more flexible and app-specific selection of (COIN) program
instances, thereby allowing for better meeting the app-specific
and end user requirements.
3.1.5. Research Questions
* RQ 3.1.1: How to combine service-level orchestration frameworks
with app-level packaging methods?
* RQ 3.1.2: How to reduce latencies involved in (COIN) program
interactions where (COIN) program instance locations may change
quickly?
* RQ 3.1.3: How to signal constraints used for routing requests
towards (COIN) program instances in a scalable manner?
* RQ 3.1.4: How to identify (COIN) programs and program instances?
* RQ 3.1.5: How to identify a specific choice of (COIN) program
instances over others?
* RQ 3.1.6: How to provide affinity of service requests towards
(COIN) program instances, i.e., longer-term transactions with
ephemeral state established at a specific (COIN) program instance?
* RQ 3.1.7: How to provide constraint-based routing decisions that
can be realized at packet forwarding speed, e.g., using techniques
explored in [SarNet2021] at the forwarding plane or using
approaches like [Multi2020] for extended routing protocols?
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* RQ 3.1.8: What COIN capabilities may support the execution of
(COIN) programs and their instances?
3.1.6. Desirable Capabilities
* Capability 3.1.1: Any COIN system must provide means for routing
of service requests between resources in the distributed
environment.
* Capability 3.1.2: Any COIN system must provide means for
identifying services exposed by (COIN) programs for directing
service requests.
* Capability 3.1.3: Any COIN system must provide means for
identifying (COIN) program instances for directing (affinity)
requests to a specific (COIN) program instance.
* Capability 3.1.4: Any COIN system must provide means for
dynamically choosing the best possible service sequence of one or
more (COIN) programs for a given application experience, i.e.,
support for chaining (COIN) program executions.
* Capability 3.1.5: Means for discovering suitable (COIN) programs
should be provided.
* Capability 3.1.6: Any COIN system must provide means for pinning
the execution of a service of a specific (COIN) program to a
specific resource, i.e., (COIN) program instance in the
distributed environment.
* Capability 3.1.7: Any COIN system should provide means for
packaging micro-services for deployments in distributed networked
computing environments.
* Capability 3.1.8: The packaging may include any constraints
regarding the deployment of (COIN) program instances in specific
network locations or compute resources, including PNDs.
* Capability 3.1.9: Such packaging should conform to existing
application deployment models, such as mobile application
packaging, TOSCA orchestration templates or tar balls or
combinations thereof.
* Capability 3.1.10: Any COIN system must provide means for real-
time synchronization and consistency of distributed application
states.
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3.2. Extended Reality and Immersive Media
3.2.1. Description
Extended reality (XR) encompasses VR, Augmented Reality (AR) and
Mixed Reality (MR). It provides the basis for the metaverse and is
the driver of a number of advances in interactive technologies.
While initially associated with gaming and immersive entertainment,
applications now include remote diagnosis, maintenance, telemedicine,
manufacturing and assembly, intelligent agriculture, smart cities,
and immersive classrooms. XR is one example of the multisource-
multidestination problem that combines video and haptics in
interactive multi-party interactions under strict delay requirements
that can benefit from a functional distribution that includes fog
computing for local information processing, the edge for aggregation,
and the cloud for image processing.
XR stands to benefit significantly from computing capabilities in the
network. For example, XR applications can offload intensive
processing tasks to edge servers, considerably reducing latency when
compared to cloud-based applications and enhancing the overall user
experience. More importantly, COIN can enable collaborative XR
experiences, where multiple users interact in the same virtual space
in real-time, regardless of their physical locations, by allowing
resource discovery and re-rerouting of XR streams. While not a
feature of most XR implementations, this capability opens up new
possibilities for remote collaboration, training, and entertainment.
Furthermore, COIN can support dynamic content delivery, allowing XR
applications to seamlessly adapt to changing environments and user
interactions. Hence, the integration of computing capabilities into
the network architecture enhances the scalability, flexibility, and
performance of XR applications by supplying telemetry and advanced
stream management, paving the way for more immersive and interactive
experiences.
Indeed, XR applications require real-time interactivity for immersive
and increasingly mobile applications with tactile and time-sensitive
data. Because high bandwidth is needed for high resolution images
and local rendering for 3D images and holograms, strictly relying on
cloud-based architectures, even with headset processing, limits some
of its potential benefits in the collaborative space. As a
consequence, innovation is needed to unlock the full potential of XR.
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3.2.2. Characterization
As mentioned above, XR experiences, especially those involving
collaboration, are difficult to deliver with a client-server cloud-
based solution as they require a combination of multi-stream
aggregation, low delays and delay variations, means to recover from
losses, and optimized caching and rendering as close as possible to
the user at the network edge. Hence, implementing such XR solutions
necessitates substantial computational power and minimal latency,
which, for now, has spurred the development of better headsets not
networked or distributed solutions as factors like distance from
cloud servers and limited bandwidth can still significantly lower
application responsiveness. Furthermore, when XR deals with
sensitive information, XR applications must also provide a secure
environment and ensure user privacy, which represent additional
burdens for delay sensitive applications. Additionally, the sheer
amount of data needed for and generated by the XR applications, such
as video holography, put them squarely in the realm of data-driven
applications that can use recent trend analysis and mechanisms, as
well as machine learning to find the optimal caching and processing
solution and, ideally, reduce the size of the data that needs
transiting through the network. Other mechanisms, such as data
filtering and reduction, and functional distribution and partitioning
are also needed to accommodate the low delay needs for the same
applications.
With functional decomposition the goal of a better XR experience, the
elements involved in a COIN XR implementation include:
* the XR application residing in the headset,
* edge federation services that allow local devices to communicate
with one another directly,
* egde application servers that enable local processing but also
intelligent stream aggregation to reduce bandwidth requirements,
* edge data networks to allow pre-caching of information based on
locality and usage,
* cloud-based services for image processing and application
training, and
* intelligent 5G/6G core networks for managing advanced access
services and providing performance data for XR stream management.
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These characteristics of XR paired with the capabilities of COIN make
it likely that COIN can help to realize XR over networks for
collaborative applications. In particular, COIN functions can enable
the distribution of the service components across different nodes in
the network. For example, data filtering, image rendering, and video
processing leveraging different hardware capabilities with
combinations of CPU and GPU at the network edge and in the fog, where
the content is consumed, represent possible remedies for the high
bandwidth demands of XR. Machine learning across the network nodes
can better manage the data flows by distributing them over more
adequate paths. In order to provide adequate quality of experience,
multi-variate and heterogeneous resource allocation and goal
optimization problems need to be solved, likely requiring advanced
analysis and articificial intelligence. For the purpose of this
document, it is important to note that the use of COIN for XR does
not imply a specific protocol but targets an architecture enabling
the deployment of the services. In this context, similar
considerations as for Section 3.1 apply.
3.2.3. Existing Solutions
The XR field has profited from extensive research in the past years
in gaming, machine learning, network telemetry, high resolution
imaging, smart cities, and IoT. Information Centric Networking (and
related) approaches that combine publish subscribe and distributed
storage are also very suited for the multisource-multidestination
applications of XR. New AR/VR headsets and glasses have continued to
evolve towards autonomy with local computation capabilities,
increasingly performing many of the processing that is needed to
render and augment the local images. Mechanisms aimed at enhancing
the computational and storage capacities of mobile devices could also
improve XR capabilities as they include the discovery of available
servers within the environment and using them opportunistically to
enhance the performance of interactive applications and distributed
file systems.
Summarizing, some XR solutions exist and headsets continue to evolve
to what is now claimed to be spatial computing. Additionally, with
recent work on the Metaverse, the number of publications related to
XR has skyrocketed. However, in terms of networking, which is the
focus of this document, current deployments do not take advantage of
network capabilities. The information is rendered and displayed
based on the local processing but does not readily discover the other
elements in the vicinity or in the network that could improve its
performance either locally, at the edge, or in the cloud. Yet, there
are still very few interactive immersive media applications over
networks that allow for federating systems capabilities.
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3.2.4. Opportunities
While delay is inherently related to information transmission and if
we continue the analogy of the computer board to highlight some of
the COIN capabilities in terms of computation and storage but also
allocation of resources, there are some opportunities that XR could
take advantage of:
* Round trip time: 20 ms is usually cited as an upper limit for XR
applications. Storage and preprocessing of scenes in local
elements (includng in the mobile network) could extend the reach
of XR applications at least over the extended edge.
* Video transmission: The use of better transcoding, advanced
context-based compression algorithms, pre-fetching and pre-
caching, as well as movement prediction all help to reduce
bandwidth consumption. While this is now limited to local
processing it is not outside the realm of COIN to push some of
these functionalities to the network especially as realted to
caching/fetching but also context based flow direction and
aggregation.
* Monitoring: Since bandwidth and data are fundamental for XR
deployment, COIN functionality could help to better monitor and
distribute the XR services over collaborating network elements to
optimize end-to-end performance.
* Functional decomposition: Advanced functional decomposition,
localization, and discovery of computing and storage resources in
the network can help to optimize user experience in general.
* Intelligent network management and configuration: The move to
artificial intelligence in network management to learn about flows
and adapt resources based on both dataplane and control plane
programmability can help the overall deployment of XR services.
3.2.5. Research Questions
* RQ 3.2.1: Can current PNDs provide the speed required for
executing complex filtering operations, including metadata
analysis for complex and dynamic scene rendering?
* RQ 3.2.2: Where should PNDs equipped with these operations be
located for optimal performance gains?
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* RQ 3.2.3: Can the use of federated learning algorithms across both
data center and edge computers be used to create optimal function
allocation and the creation of semi-permanent datasets and
analytics for usage trending and flow management resulting in
better localization of XR functions?
* RQ 3.2.4: Can COIN improve the dynamic distribution of control,
forwarding, and storage resources and related usage models in XR?
* RQ 3.2.5: How COIN provide the necessary infrastructure for the
use of interactive XR everywhere?
3.2.6. Desirable Capabilities
* Capability 3.2.1: COIN systems for XR must allow joint
collaboration across all segments of the network (fog, edge, core,
and cloud) to support functional decompositions.
* Capability 3.2.2: COIN systems for XR should provide multi-stream
efficient transmission and stream combining at the edge.
* Capability 3.2.3: COIN systems for XR should be able to
dynamically include extra streams, such as audio and extra video
tracks.
* Capability 3.2.4: COIN systems for XR may use edge networking and
computing for improved performance and performance management
without the neeed for a cloud connection.
* Capability 3.2.5: COIN systems for XR may integrate local and fog
caching with cloud-based pre-rendering.
* Capability 3.2.6: COIN systems for XR should jointly optimize COIN
and higher layer protocols to reduce latency.
* Capability 3.2.7: COIN systems for XR should provide means for
performance optimization that reduces transmitted data and
optimizes loss protection.
* Capability 3.2.8: COIN systems for XR may provide means for trend
analysis and telemetry to measure performance.
* Capability 3.2.9: COIN systems for XR should interact with indoor
and outdoor positioning systems to improve service location.
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* Capability 3.2.10: COIN systems for XR may provide means for
managing the quality of XR sessions through reduced in-network
congestion and improve flow delivery by determining how to
prioritize XR data.
3.3. Personalized and interactive performing arts
3.3.1. Description
This use case is a deeper dive into a specific scenario of the
immersive and extended reality class of use cases discussed in
Section 3.2. It focuses on live productions of the performing arts
where the performers and audience members are geographically
distributed. The performance is conveyed through multiple networked
streams which are tailored to the requirements of the remote
performers, the director, sound and lighting technicians, and
individual audience members; performers need to observe, interact and
synchronize with other performers in remote locations; and the
performers receive live feedback from the audience, which may also be
conveyed to other audience members.
There are two main aspects: i) to emulate as closely as possible the
experience of live performances where the performers, audience,
director, and technicians are co-located in the same physical space,
such as a theater; and ii) to enhance traditional physical
performances with features such as personalization of the experience
according to the preferences or needs of the performers, directors,
and audience members.
Examples of personalization include:
* Viewpoint selection such as choosing a specific seat in the
theater or for more advanced positioning of the audience member's
viewpoint outside of the traditional seating - amongst, above, or
behind the performers (but within some limits which may be imposed
by the performers or the director, for artistic reasons);
* Augmentation of the performance with subtitles, audio-description,
actor-tagging, language translation, advertisements/product-
placement, other enhancements/filters to make the performance
accessible to disabled audience members (removal of flashing
images for epileptics, alternative color schemes for color-blind
audience members, etc.).
3.3.2. Characterization
There are several chained functional entities which are candidates
for being deployed as (COIN) programs:
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* Performer aggregation and editing functions
* Distribution and encoding functions
* Personalization functions
- to select which of the existing streams should be forwarded to
the audience member, remote performer, or member of the
production team
- to augment streams with additional metadata such as subtitles
- to create new streams after processing existing ones, e.g., to
interpolate between camera angles to create a new viewpoint or
to render point clouds from an audience member's chosen
perspective
- to undertake remote rendering according to viewer position,
e.g., creation of VR headset display streams according to
audience head position - when this processing has been
offloaded from the viewer's end-system to the COIN function due
to limited processing power in the end-system, or to limited
network bandwidth to receive all of the individual streams to
be processed.
* Audience feedback sensor processing functions
* Audience feedback aggregation functions
These are candidates for deployment as (COIN) Programs in PNDs rather
than being located in end-systems (at the performers' site, the
audience members' premises or in a central cloud location) for
several reasons:
* Personalization of the performance according to viewer preferences
and requirements makes it infeasible to be done in a centralized
manner at the performer premises: the computational resources and
network bandwidth would need to scale with the number of
personalized streams.
* Rendering of VR headset content to follow viewer head movements
has an upper bound on lag to maintain viewer QoE, which requires
the processing to be undertaken sufficiently close to the viewer
to avoid large network latencies.
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* Viewer devices may not have the processing-power to perform the
personalization tasks, or the viewers' network may not have the
capacity to receive all of the constituent streams to undertake
the personalization functions.
* There are strict latency requirements for live and interactive
aspects that require the deviation from the direct network path
between performers and audience members to be minimized, which
reduces the opportunity to route streams via large-scale
processing capabilities at centralized data-centers.
3.3.3. Existing solutions
Note: Existing solutions for some aspects of this use case are
covered in Section 3.1, Section 3.2, and Section 5.1.
3.3.4. Opportunities
* Executing media processing and personalization functions on-path
as (COIN) Programs in PNDs can avoid detour/stretch to central
servers, thus reducing latency and bandwidth consumption. For
example, the overall delay for performance capture, aggregation,
distribution, personalization, consumption, capture of audience
response, feedback processing, aggregation, and rendering should
be achieved within an upper bound of latency (the tolerable amount
is to be defined, but in the order of 100s of ms to mimic
performers perceiving audience feedback, such as laughter or other
emotional responses in a theater setting).
* Processing of media streams allows (COIN) Programs, PNDs and the
wider (COIN) System/Environment to be contextually aware of flows
and their requirements which can be used for determining network
treatment of the flows, e.g., path selection, prioritization,
multi-flow coordination, synchronization and resilience.
3.3.5. Research Questions:
* RQ 3.3.1: In which PNDs should (COIN) Programs for aggregation,
encoding, and personalization functions be located? Close to the
performers or close to the viewers?
* RQ 3.3.2: How far from the direct network path from performer to
viewer should (COIN) programs be located, considering the latency
implications of path-stretch and the availability of processing
capacity at PNDs? How should tolerances be defined by users?
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* RQ 3.3.3: Should users decide which PNDs should be used for
executing (COIN) Programs for their flows or should they express
requirements and constraints that will direct decisions by the
orchestrator/manager of the COIN System?
* RQ 3.3.4: How to achieve synchronization across multiple streams
to allow for merging, audio-video interpolation, and other cross-
stream processing functions that require time synchronization for
the integrity of the output? How can this be achieved considering
that synchronization may be required between flows that are: i) on
the same data pathway through a PND/router, ii) arriving/leaving
through different ingress/egress interfaces of the same PND/
router, iii) routed through disjoint paths through different PNDs/
routers? This RQ raises issues associated with synchronisation
across multiple media streams and sub-streams [RFC7272] as well as
time synchronisation between PNDs/routers on multiple paths
[RFC8039].
* RQ 3.3.5: Where will COIN Programs be executed? In the data-plane
of PNDs, in other on-router computational capabilities within
PNDs, or in adjacent computational nodes?
* RQ 3.3.6: Are computationally-intensive tasks - such as video
stitching or media recognition and annotation (cf. Section 3.2) -
considered as suitable candidate (COIN) Programs or should they be
implemented in end-systems?
* RQ 3.3.7: If the execution of COIN Programs is offloaded to
computational nodes outside of PNDs, e.g. for processing by GPUs,
should this still be considered as COIN? Where is the boundary
between COIN capabilities and explicit routing of flows to
endsystems?
3.3.6. Desirable Capabilities
* Capability 3.3.1: Users should be able to specify requirements on
network and processing metrics (such as latency and throughput
bounds).
* Capability 3.3.2 The COIN System should be able to respect user-
specified requirements and constraints when routing flows and
selecting PNDs for executing (COIN) Programs.
* Capability 3.3.3: A COIN System should be able to synchronize flow
treatment and processing across multiple related flows which may
be on disjoint paths.
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4. Supporting new COIN Systems
4.1. In-Network Control / Time-sensitive applications
4.1.1. Description
The control of physical processes and components of industrial
production lines is essential for the growing automation of
production and ideally allows for a consistent quality level.
Traditionally, the control has been exercised by control software
running on programmable logic controllers (PLCs) located directly
next to the controlled process or component. This approach is best-
suited for settings with a simple model that is focused on a single
or few controlled components.
Modern production lines and shop floors are characterized by an
increasing number of involved devices and sensors, a growing level of
dependency between the different components, and more complex control
models. A centralized control is desirable to manage the large
amount of available information which often has to be pre-processed
or aggregated with other information before it can be used. As a
result, computations are increasingly spatially decoupled and moved
away from the controlled objects, thus inducing additional latency.
Instead moving compute functionality onto COIN execution environments
inside the network offers a new solution space to these challenges,
providing new compute locations with much smaller latencies.
4.1.2. Characterization
A control process consists of two main components as illustrated in
Figure 2: a system under control and a controller. In feedback
control, the current state of the system is monitored, e.g., using
sensors, and the controller influences the system based on the
difference between the current and the reference state to keep it
close to this reference state.
reference
state ------------ -------- Output
----------> | Controller | ---> | System | ---------->
^ ------------ -------- |
| |
| observed state |
| --------- |
-------------------| Sensors | <-----
---------
Figure 2: Simple feedback control model.
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Apart from the control model, the quality of the control primarily
depends on the timely reception of the sensor feedback which can be
subject to tight latency constraints, often in the single-digit
millisecond range. Even shorter feedback requirements may exist in
other use cases, such as interferometry or high-energy physics, but
these use cases are out of scope for this document. While low
latencies are essential, there is an even greater need for stable and
deterministic levels of latency, because controllers can generally
cope with different levels of latency, if they are designed for them,
but they are significantly challenged by dynamically changing or
unstable latencies. The unpredictable latency of the Internet
exemplifies this problem if, e.g., off-premise cloud platforms are
included.
4.1.3. Existing Solutions
Control functionality is traditionally executed on PLCs close to the
machinery. These PLCs typically require vendor-specific
implementations and are often hard to upgrade and update which makes
such control processes inflexible and difficult to manage. Moving
computations to more freely programmable devices thus has the
potential of significantly improving the flexibility. In this
context, directly moving control functionality to (central) cloud
environments is generally possible, yet only feasible if latency
constraints are lenient.
Early approaches such as [RUETH] and [VESTIN] have already shown the
general applicability of leveraging COIN for in-network control.
4.1.4. Opportunities
* Performing simple control logic on PNDs and/or in COIN execution
environments can bring the controlled system and the controller
closer together, possibly satisfying the tight latency
requirements.
* Creating a coupled control that is exercised via (i) simplified
approximations of more complex control algorithms deployed in COIN
execution environments, and (ii) more complex overall control
schemes deployed in the cloud can allow for quicker, yet more
inaccurate responses from within the network while still providing
for sufficient control accuracy at higher latencies from afar.
4.1.5. Research Questions
* RQ 4.1.1: How to derive simplified versions of the global
(control) function?
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- How to account for the limited computational precision of PNDs,
typically only allowing for integer precision computation,
while floating-point precision is needed by most control
algorithms (cf. [KUNZE-APPLICABILITY])?
- How to find suitable tradeoffs regarding simplicity of the
control function ("accuracy of the control") and implementation
complexity ("implementability")?
* RQ 4.1.2: How to distribute the simplified versions in the
network?
- Can there be different control levels, e.g., "quite inaccurate
& very low latency" (PNDs, deep in the network), "more accurate
& higher latency" (more powerful COIN execution environments,
farer away), "very accurate & very high latency" (cloud
environments, far away)?
- Who decides which control instance is executed and how?
- How do the different control instances interact?
4.1.6. Desirable Capabilities
* Capability 4.1.1: The interaction between the COIN execution
environments and the global controller should be explicit.
* Capability 4.1.2: The interaction between the COIN execution
environments and the global controller must not negatively impact
the control quality.
* Capability 4.1.3: Actions of the COIN execution environments must
be overridable by the global controller.
* Capability 4.1.4: Functions in COIN execution environments should
be executed with predictable delay.
* Capability 4.1.5: Functions in COIN execution environments must be
executed with predictable accuracy.
4.2. Large Volume Applications
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4.2.1. Description
In modern industrial networks, processes and machines are extensively
monitored by distributed sensors with a large spectrum of
capabilities, ranging from simple binary (e.g., light barriers) to
sophisticated sensors with varying degrees of resolution. Sensors
further serve different purposes, as some are used for time-critical
process control while others represent redundant fallback platforms.
Overall, there is a high level of heterogeneity which makes managing
the sensor output a challenging task.
Depending on the deployed sensors and the complexity of the observed
system, the resulting overall data volume can easily be in the range
of several Gbit/s [GLEBKE]. These volumes are often already
difficult to handle in local environments and it becomes even more
challenging when off-premise clouds are used for managing the data.
While large networking companies can simply upgrade their
infrastructure to accommodate the accruing data volumes, most
industrial companies operate on tight infrastructure budgets and
upgrading is hence not always feasible or possible. A major
challenge is thus to devise a methodology that is able to handle such
amounts of data over limited access links.
Data filtering and pre-processing, similar to the considerations in
Section 3.2, can be building blocks for new solutions in this space.
Such solutions, however, might also have to address the added
challenge of business data leaving the premises and control of the
company. As this data could include sensitive information or
valuable business secrets, additional security measures have to be
taken. Yet, typical security measures such as encrypting the data
make filtering or pre-processing approaches hardly applicable as they
typically work on unencrypted data. Consequently, incorporating
security into these approaches, either by adding functionality for
handling encrypted data or devising general security measures, is
thus an additional auspicious field for research.
4.2.2. Characterization
In essence, the described monitoring systems consist of sensors that
produce large volumes of monitoring data. This data is then
transmitted to additional components that provide data processing and
analysis capabilities or simply store the data in large data silos.
As sensors are often set up redundantly, part of the collected data
might also be redundant. Moreover, sensors are often hard to
configure or not configurable at all which is why their resolution or
sampling frequency is often larger than required. Consequently, it
is likely that more data is transmitted than is needed or desired,
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prompting the deployment of filtering techniques. For example, COIN
programs deployed in the on-premise network could filter out
redundant or undesired data before it leaves the premise using simple
traffic filters, thus reducing the required (upload) bandwidths. The
available sensor data could be scaled down using standard statistical
sampling, packet-based sub-sampling, i.e., only forwarding every n-th
packet, or using filtering as long as the sensor value is in an
uninteresting range while forwarding with a higher resolution once
the sensor value range becomes interesting (cf. [KUNZE-SIGNAL]).
While the former variants are oblivious to the semantics of the
sensor data, the latter variant requires an understanding of the
current sensor levels. In any case, it is important that end-hosts
are informed about the filtering so that they can distinguish between
data loss and data filtered out on purpose.
In practice, the collected data is further processed using various
forms of computation. Some of them are very complex or need the
complete sensor data during the computation, but there are also
simpler operations which can already be done on subsets of the
overall dataset or earlier on the communication path as soon as all
data is available. One example is finding the maximum of all sensor
values which can either be done iteratively at each intermediate hop
or at the first hop, where all data is available. Using expert
knowledge about the exact computation steps and the concrete
transmission path of the sensor data, simple computation steps can
thus be deployed in the on-premise network, again reducing the
overall data volume.
4.2.3. Existing Solutions
Current approaches for handling such large amounts of information
typically build upon stream processing frameworks such as Apache
Flink. These solutions allow for handling large volume applications
and map the compute functionality to performant server machines or
distributed compute platforms. Augmenting the existing capabilities,
COIN offers a new dimension of platforms for such processing
frameworks.
4.2.4. Opportunities
* (Stream) processing frameworks can become more flexible by
introducing COIN execution environments as additional deployment
targets.
* (Semantic) packet filtering based on packet header and payload, as
well as multi-packet information can (drastically) reduce the data
volume, possibly even without losing any important information.
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* (Semantic) data (pre-)processing, e.g., in the form of
computations across multiple packets and potentially leveraging
packet payload, can also reduce the data volume without losing any
important information.
4.2.5. Research Questions
Some of the following research questions are also relevant in the
context of general stream processing systems.
* RQ 4.2.1: How can the overall data processing pipeline be divided
into individual processing steps that could then be deployed as
COIN functionality?
* RQ 4.2.2: How to design COIN programs for (semantic) packet
filtering?
- Which criteria for filtering make sense?
* RQ 4.2.3: Which kinds of COIN programs can be leveraged for
(pre-)processing steps?
- How complex can they become?
* RQ 4.2.4: How to distribute and coordinate COIN programs?
* RQ 4.2.5: How to dynamically change COIN programs?
* RQ 4.2.6: How to incorporate the (pre-)processing and filtering
steps into the overall system?
- How can changes to the data by COIN programs be signaled to the
end-hosts?
4.2.6. Desirable capabilities
* Capability 4.2.1: Filters and preprocessors must conform to
application-level syntax and semantics.
* Capability 4.2.2: Filters and preprocessors may leverage packet
header and payload information.
* Capability 4.2.3: Filters and preprocessors should be
reconfigurable at run-time.
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4.3. Industrial Safety
4.3.1. Description
Despite an increasing automation in production processes, human
workers are still often necessary. Consequently, safety measures
have a high priority to ensure that no human life is endangered. In
traditional factories, the regions of contact between humans and
machines are well-defined and interactions are simple. Simple safety
measures like emergency switches at the working positions are enough
to provide a good level of safety.
Modern factories are characterized by increasingly dynamic and
complex environments with new interaction scenarios between humans
and robots. Robots can directly assist humans, perform tasks
autonomously, or even freely move around on the shopfloor. Hence,
the intersect between the human working area and the robots grows and
it is harder for human workers to fully observe the complete
environment. Additional safety measures are essential to prevent
accidents and support humans in observing the environment.
4.3.2. Characterization
Industrial safety measures are typically hardware solutions because
they have to pass rigorous testing before they are certified and
deployment-ready. Standard measures include safety switches and
light barriers. Additionally, the working area can be explicitly
divided into 'contact' and 'safe' areas, indicating when workers have
to watch out for interactions with machinery. For example, markings
on the factory floor can show the areas where robots move or indicate
their maximum physical reach.
These measures are static solutions, potentially relying on
specialized hardware, and are challenged by the increased dynamics of
modern factories where the factory configuration can be changed on
demand or where all entities are freely moving around. Software
solutions offer higher flexibility as they can dynamically respect
new information gathered by the sensor systems, but in most cases
they cannot give guaranteed safety. COIN systems could leverage the
increased availability of sensor data and the detailed monitoring of
the factories to enable additional safety measures with shorter
response times and higher guarantees. Different safety indicators
within the production hall could be combined within the network so
that PNDs can give early responses if a potential safety breach is
detected. For example, the positions of human workers and robots
could be tracked and robots could be stopped when they get too close
to a human in a non-working area or if a human enters a defined
safety zone. More advanced concepts could also include image data or
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combine arbitrary sensor data. Finally, the increasing
softwarization of industrial processes can also lead to new problems,
e.g., if software bugs cause unintended movements of robots. Here,
COIN systems could independently double check issued commands to void
unsafe commands.
4.3.3. Existing Solutions
Due to the importance of safety, there is a wide range of software-
based approaches aiming at enhancing security. One example are tag-
based systems, e.g., using RFID, where drivers of forklifts can be
warned if pedestrian workers carrying tags are nearby. Such
solutions, however, require setting up an additional system and do
not leverage existing sensor data.
4.3.4. Opportunities
* Executing safety-critical COIN functions on PNDs could allow for
early emergency reactions based on diverse sensor feedback with
low latencies.
* COIN software could provide independent on-path surveillance of
control software-initiated actions to block unsafe commands.
4.3.5. Research Questions
* RQ 4.3.1: Which additional safety measures can be provided?
- Do these measures actually improve safety?
* RQ 4.3.2: Which sensor information can be combined and how?
4.3.6. Desirable capabilities
* Capability 4.3.1: COIN-based safety measures must not degrade
existing safety measures.
* Capability 4.3.2: COIN-based safety measures may enhance existing
safety measures.
5. Improving existing COIN capabilities
5.1. Content Delivery Networks
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5.1.1. Description
Delivery of content to end users often relies on Content Delivery
Networks (CDNs). CDNs store said content closer to end users for
latency-reduced delivery as well as to reduce load on origin servers.
For this, they often utilize DNS-based indirection to serve the
request on behalf of the origin server. Both of these objectives are
within scope to be addressed by COIN methods and solutions.
5.1.2. Characterization
From the perspective of this draft, a CDN can be interpreted as a
(network service level) set of (COIN) programs. These programs
implement a distributed logic for first distributing content from the
origin server to the CDN ingress and then further to the CDN
replication points which ultimately serve the user-facing content
requests.
5.1.3. Existing Solutions
CDN technologies have been well described and deployed in the
existing Internet. Core technologies like Global Server Load
Balancing (GSLB) [GSLB] and Anycast server solutions are used to deal
with the required indirection of a content request (usually in the
form of an HTTP request) to the most suitable local CDN server.
Content is replicated from seeding servers, which serve as injection
points for content from content owners/producers, to the actual CDN
servers, who will eventually serve the user's request. The
replication architecture and mechanisms itself differs from one (CDN)
provider to another, and often utilizes private peering or network
arrangements in order to distribute the content internationally and
regionally.
Studies such as those in [FCDN] have shown that content distribution
at the level of named content, utilizing efficient (e.g., Layer 2)
multicast for replication towards edge CDN nodes, can significantly
increase the overall network and server efficiency. It also reduces
indirection latency for content retrieval as well as required edge
storage capacity by benefiting from the increased network efficiency
to renew edge content more quickly against changing demand. Works
such as those in [SILKROAD] utilize ASICs to replace server-based
load balancing with significant cost reductions, thus showcasing the
potential for in-network CN operations.
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5.1.4. Opportunities
* Supporting service-level routing of requests (service routing in
[APPCENTRES]) to specific (COIN) program instances may improve on
end user experience in faster retrieving (possibly also more,
e.g., better quality) content.
* COIN instances may also be utilized to integrate service-related
telemetry information to support the selection of the final
service instance destination from a pool of possible choices
* Supporting the selection of a service destination from a set of
possible (e.g., virtualized, distributed) choices, e.g., through
constraint-based routing decisions (see [APPCENTRES]) in (COIN)
program instances to improve the overall end user experience by
selecting a 'more suitable' service destination over another,
e.g., avoiding/reducing overload situations in specific service
destinations.
* Supporting Layer 2 capabilities for multicast (compute
interconnection and collective communication in [APPCENTRES]),
e.g., through in-network/switch-based replication decisions (and
their optimizations) based on dynamic group membership
information, may reduce the network utilization and therefore
increase the overall system efficiency.
5.1.5. Research Questions
In addition to the research questions in Section 3.1.5:
* RQ 5.1.1: How to utilize L2 multicast to improve on CDN designs?
How to utilize COIN capabilities in those designs, such as through
on-path optimizations for fanouts?
* RQ 5.1.2: What forwarding methods may support the required
multicast capabilities (see [FCDN]) and how could programmable
COIN forwarding elements support those methods (e.g., extending
current SDN capabilities)?
* RQ 5.1.3: What are the constraints, reflecting both compute and
network capabilities, that may support joint optimization of
routing and computing? How could intermediary (COIN) program
instances support, e.g., the aggregation of those constraints to
reduce overall telemetry network traffic?
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* RQ 5.1.4: Could traffic steering be performed on the data path and
per service request, e.g., through (COIN) program instances that
perform novel routing request lookup methods? If so, what would
be performance improvements?
* RQ 5.1.5: How could storage be traded off against frequent,
multicast-based replication (see [FCDN])? Could intermediary/in-
network (COIN) elements support the storage beyond current
endpoint-based methods?
* RQ 5.1.6: What scalability limits exist for L2 multicast
capabilities? How to overcome them, e.g., through (COIN) program
instances serving as stateful subtree aggregators to reduce the
needed identifier space for, e.g., bit-based forwarding?
5.1.6. Desirable Capabilities
Capabilities 3.1.1 through 3.1.6 also apply for CDN service access.
In addition:
* Capability 5.1.1: Any solution should utilize Layer 2 multicast
transmission capabilities for responses to concurrent service
requests.
5.2. Compute-Fabric-as-a-Service (CFaaS)
5.2.1. Description
We interpret connected compute resources as operating at a suitable
layer, such as Ethernet, InfiBand but also at Layer 3, to allow for
the exchange of suitable invocation methods, such as exposed through
verb-based or socket-based APIs. The specific invocations here are
subject to the applications running over a selected pool of such
connected compute resources.
Providing such pool of connected compute resources, e.g., in regional
or edge data centers, base stations, and even end user devices, opens
up the opportunity for infrastructure providers to offer CFaaS-like
offerings to application providers, leaving the choice of the
appropriate invocation method to the app and service provider.
Through this, the compute resources can be utilized to execute the
desired (COIN) programs of which the application is composed, while
utilizing the interconnection between those compute resources to do
so in a distributed manner.
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5.2.2. Characterization
We foresee those CFaaS offerings to be tenant-specific, a tenant here
defined as the provider of at least one application. For this, we
foresee an interaction between CFaaS provider and tenant to
dynamically select the appropriate resources to define the demand
side of the fabric. Conversely, we also foresee the supply side of
the fabric to be highly dynamic with resources being offered to the
fabric through, e.g., user-provided resources (whose supply might
depend on highly context-specific supply policies) or infrastructure
resources of intermittent availability such as those provided through
road-side infrastructure in vehicular scenarios.
The resulting dynamic demand-supply matching establishes a dynamic
nature of the compute fabric that in turn requires trust
relationships to be built dynamically between the resource
provider(s) and the CFaaS provider. This also requires the
communication resources to be dynamically adjusted to suitably
interconnect all resources into the (tenant-specific) fabric exposed
as CFaaS.
5.2.3. Existing Solutions
There exist a number of technologies to build non-local (wide area)
Layer 2 as well as Layer 3 networks, which in turn allows for
connecting compute resources for a distributed computational task.
For instance, 5G-LAN [SA2-5GLAN] specifies a cellular L2 bearer for
interconnecting L2 resources within a single cellular operator. The
work in [ICN5GLAN] outlines using a path-based forwarding solution
over 5G-LAN as well as SDN-based LAN connectivity together with an
ICN-based naming of IP and HTTP-level resources to achieve
computational interconnections, including scenarios such as those
outlined in Section 3.1. L2 network virtualization (see, e.g.,
[L2Virt]) is one of the methods used for realizing so-called 'cloud-
native' applications for applications developed with 'physical'
networks in mind, thus forming an interconnected compute and storage
fabric.
5.2.4. Opportunities
* Supporting service-level routing of compute resource requests
(service routing in [APPCENTRES]) may allow for utilizing the
wealth of compute resources in the overall CFaaS fabric for
execution of distributed applications, where the distributed
constituents of those applications are realized as (COIN) programs
and executed within a COIN system as (COIN) program instances.
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* Supporting the constraint-based selection of a specific (COIN)
program instance over others (constraint-based routing in
[APPCENTRES]) will allow for optimizing both the CFaaS provider
constraints as well as tenant-specific constraints.
* Supporting Layer 2 and 3 capabilities for multicast (compute
interconnection and collective communication in [APPCENTRES]) will
allow for decreasing both network utilization but also possible
compute utilization (due to avoiding unicast replication at those
compute endpoints), thereby decreasing total cost of ownership for
the CFaaS offering.
* Supporting the enforcement of trust relationships and isolation
policies through intermediary (COIN) program instances, e.g.,
enforcing specific traffic shares or strict isolation of traffic
through differentiated queueing.
5.2.5. Research Questions
In addition to the research questions in Section 3.1.5:
* RQ 5.2.1: How to convey tenant-specific requirements for the
creation of the CFaaS fabric?
* RQ 5.2.2: How to dynamically integrate resources, particularly
when driven by tenant-level requirements and changing service-
specific constraints?
* RQ 5.2.3: How to utilize COIN capabilities to aid the availability
and accountability of resources, i.e., what may be (COIN) programs
for a CFaaS environment that in turn would utilize the distributed
execution capability of a COIN system?
* RQ 5.2.4: How to utilize COIN capabilities to enforce traffic and
isolation policies for establishing trust between tenant and CFaaS
provider in an assured operation?
5.2.6. Desirable Capabilities
Capabilities 3.1.1 through 3.1.6 also apply for the provisioning of
services atop the CFaaS. In addition:
* Capability 5.2.1: Any solution should expose means to specify the
requirements for the tenant-specific compute fabric being utilized
for the service execution.
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* Capability 5.2.2: Any solution should allow for dynamic
integration of compute resources into the compute fabric being
utilized for the app execution; those resources include, but are
not limited to, end user provided resources. From a COIN system
perspective, new resources must be possible to be exposed as
possible (COIN) execution environments.
* Capability 5.2.3: Any solution must provide means to optimize the
interconnection of compute resources, including those dynamically
added and removed during the provisioning of the tenant-specific
compute fabric.
* Capability 5.2.4: Any solution must provide means for ensuring
that availability and usage of resources is accounted for.
5.3. Virtual Networks Programming
5.3.1. Description
The term "virtual network programming" is proposed to describe
mechanisms by which tenants deploy and operate COIN programs in their
virtual network. Such COIN programs can, e.g., be P4 programs,
OpenFlow rules, or higher layer programs. This feature can enable
other use cases described in this draft to be deployed using virtual
networks services, over underlying networks such as datacenters,
mobile networks, or other fixed or wireless networks.
For example, COIN programs could perform the following on a tenant's
virtual network:
* Allow or block flows, and request rules from an SDN controller for
each new flow, or for flows to or from specific hosts that need
enhanced security
* Forward a copy of some flows towards a node for storage and
analysis
* Update counters based on specific sources/destinations or
protocols, for detailed analytics
* Associate traffic between specific endpoints, using specific
protocols, or originated from a given application, to a given
slice, while other traffic uses a default slice
* Experiment with a new routing protocol (e.g., ICN), using a P4
implementation of a router for this protocol
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5.3.2. Characterization
To provide a concrete example of virtual COIN programming, we
consider a use case using a 5G underlying network, the 5GLAN
virtualization technology, and the P4 programming language and
environment. Section 5.1 of [I-D.ravi-icnrg-5gc-icn] provides a
description of the 5G network functions and interfaces relevant to
5GLAN, which are otherwise specified in [TS23.501] and [TS23.502].
From the 5GLAN service customer/tenant standpoint, the 5G network
operates as a switch.
In the use case depicted in Figure 3, the tenant operates a network
including a 5GLAN network segment (seen as a single logical switch),
as well as fixed segments. The mobile devices (or User Equipment
nodes) UE1, UE2, UE3 and UE4 are in the same 5GLAN, as well as
Device1 and Device2 (through UE4). This scenario can take place in a
plant or enterprise network, using, e.g., a 5G Non-Public Network.
The tenant uses P4 programs to determine the operation of both the
fixed and 5GLAN switches. The tenant provisions a 5GLAN P4 program
into the mobile network, and can also operate a controller.
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..... Tenant ........
P4 program : :
deployment : Operation :
V :
+-----+ air interface +----------------+ :
| UE1 +----------------+ | :
+-----+ | | :
| | :
+-----+ | | V
| UE2 +----------------+ 5GLAN | +------------+
+-----+ | Logical +------+ Controller |
| Switch | P4 +-------+----+
+-----+ | | runtime |
| UE3 +----------------+ | API |
+-----+ | | |
| | |
+-----+ | | |
+-+ UE4 +----------------+ | |
| +-----+ +----------------+ |
| |
| Fixed or wireless connection |
| P4 runtime API |
| +---------+ +-------------------------------+
+--+ Device1 | |
| +---------+ |
| |
| +---------+ +------+-----+
`--+ Device2 +----+ P4 Switch +--->(fixed network)
+---------+ +------------+
Figure 3: 5G Virtual Network Programming Overview
5.3.3. Existing Solutions
Research has been conducted, for example by [Stoyanov], to enable P4
network programming of individual virtual switches. To our
knowledge, no complete solution has been developed for deploying
virtual COIN programs over mobile or datacenter networks.
5.3.4. Opportunities
Virtual network programming by tenants could bring benefits such as:
* A unified programming model, which can facilitate porting COIN
programs between data centers, 5G networks, and other fixed and
wireless networks, as well as sharing controller, code and
expertise.
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* Increasing the level of customization available to customers/
tenants of mobile networks or datacenters compared to typical
configuration capabilities. For example, 5G network evolution
points to an ever increasing specialization and customization of
private mobile networks, which could be handled by tenants using a
programming model similar to P4.
* Using network programs to influence underlying network services,
e.g., request specific QoS for some flows in 5G or datacenters, to
increase the level of in-depth customization available to tenants.
5.3.5. Research Questions
* RQ 5.3.1: Underlying Network Awareness: a virtual COIN program can
be able to influence, and be influenced by, the underling network.
Since some information and actions may be available on some nodes
and not others, underlying network awareness may impose additional
constraints on distributed network programs location.
* RQ 5.3.2: Splitting/Distribution: a virtual COIN program may need
to be deployed across multiple computing nodes, leading to
research questions around instance placement and distribution.
For example, program logic should be applied exactly once or at
least once per packet, while allowing optimal forwarding path by
the underlying network. Research challenges include defining
manual (by the programmer) or automatic methods to distribute COIN
programs that use a low or minimal amount of resources.
Distributed P4 programs are studied in
[I-D.hsingh-coinrg-reqs-p4comp] and [Sultana].
* RQ 5.3.3: Multi-Tenancy Support: multiple virtual COIN program
instances can run on the same compute node. While mechanisms were
proposed for P4 multi-tenancy in a switch [Stoyanov], research
questions remain about isolation between tenants and fair
repartition of resources.
* RQ 5.3.4: Security: how can tenants and underlying networks be
protected against security risks, including overuse or misuse of
network resources, injection of traffic, or access to unauthorized
traffic?
* RQ 5.3.5: Higher layer processing: can a virtual network model
facilitate the deployment of COIN programs acting on application
layer data? This is an open question since the present section
focused on packet/flow processing.
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5.3.6. Desirable Capabilities
* Capability 5.3.1: A COIN system supporting virtualization should
enable tenants to deploy COIN programs onto their virtual
networks.
* Capability 5.3.2: A virtual COIN program should process flows/
packets once and only once (or at least once for idempotent
operations), even if the program is distributed over multiple
PNDs.
* Capability 5.3.3: Multi-tenancy should be supported for virtual
COIN programs, i.e., instances of virtual COIN programs from
different tenants can share underlying PNDs. This includes
requirements for secure isolation between tenants, and fair (or
policy-based) sharing of computing resources.
* Capability 5.3.4: Virtual COIN programs should support mobility of
endpoints.
6. Enabling new COIN capabilities
6.1. Distributed AI Training
6.1.1. Description
There is a growing range of use cases demanding for the realization
of AI training capabilities among distributed endpoints. Such demand
may be driven by the need to increase overall computational power for
large-scale problems. From a COIN perspective, those capabilities
may be realized as (COIN) programs and executed throughout the COIN
system, including in PNDs.
Some solutions may desire the localization of reasoning logic, e.g.,
for deriving attributes that better preserve privacy of the utilized
raw input data. Quickly establishing (COIN) program instances in
nearby compute resources, including PNDs, may even satisfy such
localization demands on-the-fly (e.g., when a particular use is being
realized, then terminated after a given time).
6.1.2. Characterization
Examples for large-scale AI training problems include biotechnology
and astronomy related reasoning over massive amounts of observational
input data. Examples for localizing input data for privacy reasons
include radar-like applications for the development of topological
mapping data based on (distributed) radio measurements at base
stations (and possibly end devices), while the processing within
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radio access networks (RAN) already constitutes a distributed AI
training problem to a certain extent albeit with little flexibility
in distributing the execution of the AI logic. Other examples
include the extraction of key patient features from local private
patient data, e.g., held at organizationally separate health trusts.
6.1.3. Existing Solutions
Reasoning frameworks, such as TensorFlow, may be utilized for the
realization of the (distributed) AI training logic, building on
remote service invocation through protocols such as gRPC [GRPC] or
MPI [MPI] with the intention of providing an on-chip NPU (neural
processor unit) like abstraction to the AI framework.
A number of activities on distributed AI training exist in the area
of developing the 5th and 6th generation mobile network with various
activities in the 3GPP SDO as well as use cases developed for the
ETSI MEC initiative mentioned in previous use cases.
6.1.4. Opportunities
* Supporting service-level routing of training requests (service
routing in [APPCENTRES]), with AI services being exposed to the
network, where (COIN) program instances may support the selection
of the most suitable service instance based on control plane
information, e.g., on AI worker compute capabilities, being
distributed across (COIN) program instances.
* Supporting the collective communication primitives, such as all-
to-all, scatter-gather, utilized by the (distributed) AI workers
to increase the overall network efficiency, e.g., through avoiding
endpoint-based replication or even directly performing, e.g.,
reduce, collective primitive operations in (COIN) program
instances placed in topologically advantageous places.
* Supporting collective communication between multiple instances of
AI services, i.e., (COIN) program instances, may positively impact
network but also compute utilization by moving from unicast
replication to network-assisted multicast operation.
6.1.5. Research Questions
In addition to the research questions in Section 3.1.5:
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* RQ 6.1.1: What are the communication patterns that may be
supported by collective communication solutions, where those
solutions directly utilize (COIN) program instance capabilities
within the network (e.g., reduce in a central (COIN) program
instance)?
* RQ 6.1.2: How to achieve scalable multicast delivery for
collective communication primitives with rapidly changing receiver
sets, e.g., where training workers may be dynamically selected
based on energy efficiency constraints [GREENAI]?
* RQ 6.1.3: What COIN capabilities may support the collective
communication patterns found in distributed AI problems?
* RQ 6.1.4: How to provide a service routing capability that
supports AI-specific invocation protocols, such as MPI or RDMA?
6.1.6. Desirable Capabilities
Capabilities 3.1.1 through 3.1.6 also apply for general distributed
AI capabilities. In addition:
* Capability 6.1.1: Any COIN system must provide means to specify
the constraints for placing (AI) execution logic in the form of
(COIN) programs in certain logical execution points (and their
associated physical locations), including PNDs.
* Capability 6.1.2: Any COIN system must provide support for app/
micro-service specific invocation protocols for requesting (COIN)
program services exposed to the COIN system.
7. Security Considerations
COIN systems, like any other system using ``middleboxes'', can have
different security and privacy implications that strongly depend on
the used platforms, the provided functionality, and the deployment
domain, with most if not all considerations for general middleboxes
also applying for COIN systems.
One critical aspect for early COIN systems is the use of early-
generation PNDs, many of which do not have cryptography support and
only have limited computational capabilities. Hence, PND-based COIN
systems typically work on unencrypted data and often customize packet
payload while concepts, such as homomorphic encryption, could serve
as workarounds, allowing PNDs to perform simple operations on the
encrypted data without having access to it. All these approaches
introduce the same or very similar security implications as any
middlebox operating on unencrypted traffic or having access to
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encryption: a middlebox can itself have malicious intentions, e.g.,
because it got compromised, or the deployment of functionality offers
new attack vectors to outsiders.
However, similar to middlebox deployments, risks for privacy and of
data exposure have to be carefully considered in the context of the
concrete deployment. For example, exposing data to an external
operator for mobile application offloading leads to a significant
privacy loss of the user in any case. In contrast, such privacy
considerations are not as relevant for COIN systems where all
involved entities are under the same control, such as in an
industrial context. Here, exposed data and functionality can instead
lead to stolen business secrets or the enabling of, e.g., DoS
attacks. Hence, even in fully controlled scenarios, COIN
intermediaries, and middleboxes in general, are ideally operated in a
least-privilege mode, where they have exactly those permissions to
read and alter payload that are necessary to fulfil their purpose.
Research on granting middleboxes access to secured traffic is only in
its infancy and a variety of different approaches are proposed and
analyzed [TLSSURVEY]. In a SplitTLS [SPLITTLS] deployment, e.g.,
middleboxes have different incoming and outgoing TLS channels, such
that they have full read and write access to all intercepted traffic.
More restrictive approaches for deploying middleboxes rely on
searchable encryption or zero-knowledge proofs to expose less data to
intermediaries, but those only offer limited functionality.
MADTLS[MADTLS] is tailored to the industrial domain and offers bit-
level read and write access to intermediaries with low latency and
bandwidth overhead, at the cost of more complex key management.
Overall, different proposals offer different advantages and
disadvantages that must be carefully considered in the context of
concrete deployments. Further research could pave the way for a more
unified and configurable solution that is easier to maintain and
deploy.
Finally, COIN systems and other middlebox deployments can also lead
to security risks even if the attack stems from an outsider without
direct access to any devices. As such, metadata about the entailed
processing (processing times, changes in incoming and outgoing data)
can allow an attacker to extract valuable information about the
process. Moreover, such deployments can become central entities
that, if paralyzed (e.g., through extensive requests), can be
responsible for large-scale outages. In particular, some deployments
could be used to amplify DoS attacks. Similar to other middlebox
deployments, these potential risks must be considered when deploying
COIN functionality and may influence the selection of suitable
security protocols.
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8. IANA Considerations
N/A
9. Conclusion
This document presented use cases gathered from several application
domains that can and could profit from capabilities that are provided
by in-network and, more generally, distributed compute capabilities.
We distinguished between use cases in which COIN may enable new
experiences (Section 3), expose new features (Section 6), or improve
on existing system capabilities (Section 5), and other use cases
where COIN capabilities enable totally new applications, for example,
in industrial networking (Section 4).
Beyond the mere description and characterization of those use cases,
we identified opportunities arising from utilizing COIN capabilities
as well as research questions that may need to be addressed before
being able to reap those opportunities. We also outlined desirable
capabilities for COIN systems realizing these use cases.
We acknowledge that this work offers no comprehensive overview of
possible use cases and is thus only a snapshot of what may be
possible if COIN capabilities existed.
In fact, the decomposition of many current client-server applications
into node by node transit could identify other opportunities for
adding computing to forwarding notably in supply-chain, health care,
intelligent cities and transportation and even financial services
(among others).
With this in mind, updates to this document might become necessary or
desirable in the future to capture this extended view on what may be
possible. We are, however, confident that the current selection of
use cases, each describing the dimensions of opportunities, research
questions, and desired capabilities, already represents a useful set
of scenarios that yield themselves for a subsequent analysis that is
currently intended to be performed in [USECASEANALYSIS]. Through
this, the use cases presented here together with the intended
analysis provide direct input into the milestones of COINRG in terms
of required functionalities.
10. Acknowledgements
The authors would like to thank Eric Wagner for providing text on the
security considerations. The authors would further like to thank
Chathura Sarathchandra, David Oran, Phil Eardley, Stuart Card,
Jeffrey He, Toerless Eckert, and Jon Crowcroft for reviewing the
document.
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11. Informative References
[APPCENTRES]
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[ETSI] ETSI, "Multi-access Edge Computing (MEC)", 2022,
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[FCDN] Al-Naday, M., Reed, M. J., Riihijarvi, J., Trossen, D.,
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[GLEBKE] Glebke, R., Henze, M., Wehrle, K., Niemietz, P., Trauth,
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03, 18 February 2021,
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[I-D.ravi-icnrg-5gc-icn]
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[KUNZE-APPLICABILITY]
Kunze, I., Glebke, R., Scheiper, J., Bodenbenner, M.,
Schmitt, R., and K. Wehrle, "Investigating the
Applicability of In-Network Computing to Industrial
Scenarios", 2021 4th IEEE International Conference on
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Kunze, I., Niemietz, P., Tirpitz, L., Glebke, R., Trauth,
D., Bergs, T., and K. Wehrle, "Detecting Out-Of-Control
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[Microservices]
Richardson, C., "What are microservices?", 2024,
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[RFC7272] van Brandenburg, R., Stokking, H., van Deventer, O.,
Boronat, F., Montagud, M., and K. Gross, "Inter-
Destination Media Synchronization (IDMS) Using the RTP
Control Protocol (RTCP)", RFC 7272, DOI 10.17487/RFC7272,
June 2014, <https://www.rfc-editor.org/rfc/rfc7272>.
[RFC8039] Shpiner, A., Tse, R., Schelp, C., and T. Mizrahi,
"Multipath Time Synchronization", RFC 8039,
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"Flightplan: Dataplane Disaggregation and Placement for P4
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[TERMINOLOGY]
Hong, J., Kunze, I., Wehrle, K., Trossen, D., Montpetit,
M., de Foy, X., Griffin, D., and M. Rio, "Terminology for
Computing in the Network", Work in Progress, Internet-
Draft, draft-irtf-coinrg-coin-terminology-01, 10 July
2023, <https://datatracker.ietf.org/doc/html/draft-irtf-
coinrg-coin-terminology-01>.
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[USECASEANALYSIS]
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Authors' Addresses
Ike Kunze
RWTH Aachen University
Ahornstr. 55
D-52074 Aachen
Germany
Email: kunze@comsys.rwth-aachen.de
Klaus Wehrle
RWTH Aachen University
Ahornstr. 55
D-52074 Aachen
Germany
Email: wehrle@comsys.rwth-aachen.de
Dirk Trossen
Huawei Technologies Duesseldorf GmbH
Riesstr. 25C
D-80992 Munich
Germany
Email: Dirk.Trossen@Huawei.com
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Marie-Jose Montpetit
McGill University
680 Sherbrooke Street W.
Montreal H3A 3R1
Canada
Email: marie-jose.montpetit@mcgill.ca
Xavier de Foy
InterDigital Communications, LLC
1000 Sherbrooke West
Montreal H3A 3G4
Canada
Email: xavier.defoy@interdigital.com
David Griffin
University College London
Gower St
London
WC1E 6BT
United Kingdom
Email: d.griffin@ucl.ac.uk
Miguel Rio
University College London
Gower St
London
WC1E 6BT
United Kingdom
Email: miguel.rio@ucl.ac.uk
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