Internet DRAFT - draft-irtf-nmrg-autonomic-sla-violation-detection
draft-irtf-nmrg-autonomic-sla-violation-detection
Network Management Research Group J. Nobre
Internet-Draft University of Vale do Rio dos Sinos
Intended status: Informational L. Granville
Expires: May 19, 2018 Federal University of Rio Grande do Sul
A. Clemm
Huawei
A. Gonzalez Prieto
VMware
November 15, 2017
Autonomic Networking Use Case for Distributed Detection of SLA
Violations
draft-irtf-nmrg-autonomic-sla-violation-detection-13
Abstract
This document describes an experimental use case for autonomic
networking concerning monitoring of Service Level Agreements (SLAs).
The use case aims to detect violations of SLAs in a distributed
fashion, striving to optimize and dynamically adapt the autonomic
deployment of active measurement probes in a way that maximizes the
likelihood of detecting service level violations with a given
resource budget to perform active measurements, and is able to do so
without any outside guidance or intervention.
This document is a product of the IRTF Network Management Research
Group (NMRG). It is published for informational purposes.
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/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on May 19, 2018.
Nobre, et al. Expires May 19, 2018 [Page 1]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
Copyright Notice
Copyright (c) 2017 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 and restrictions with respect
to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Definitions and Acronyms . . . . . . . . . . . . . . . . . . 5
3. Current Approaches . . . . . . . . . . . . . . . . . . . . . 6
4. Use Case Description . . . . . . . . . . . . . . . . . . . . 6
5. A Distributed Autonomic Solution . . . . . . . . . . . . . . 7
6. Intended User Experience . . . . . . . . . . . . . . . . . . 10
7. Implementation Considerations . . . . . . . . . . . . . . . . 10
7.1. Device Based Self-Knowledge and Decisions . . . . . . . . 11
7.2. Interaction with other devices . . . . . . . . . . . . . 11
8. Comparison with current solutions . . . . . . . . . . . . . . 11
9. Related IETF Work . . . . . . . . . . . . . . . . . . . . . . 12
10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 12
11. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
12. Security Considerations . . . . . . . . . . . . . . . . . . . 13
13. Informative References . . . . . . . . . . . . . . . . . . . 13
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14
1. Introduction
The Internet has been growing dramatically in terms of size,
capacity, and accessibility in the last years. Communication
requirements of distributed services and applications running on top
of the Internet have become increasingly demanding. Some examples
are real-time interactive video or financial trading. Providing such
services involves stringent requirements in terms of acceptable
latency, loss, or jitter.
Performance requirements lead to the articulation of Service Level
Objectives (SLOs) which must be met. Those SLOs are part of Service
Level Agreements (SLAs) that define a contract between the provider
and the consumer of a service. SLOs, in effect, constitute a service
Nobre, et al. Expires May 19, 2018 [Page 2]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
level guarantee that the consumer of the service can expect to
receive (and often has to pay for). Likewise, the provider of a
service needs to ensure that the service level guarantee and
associated SLOs are met. Some examples of clauses that relate to
service level objectives can be found in [RFC7297]).
Violations of SLOs can be associated with significant financial loss,
which can by divided into two categories. For one, there is the loss
that can be incurred by the user of a service when the agreed service
levels are not provided. For example, a financial brokerage's stock
orders might suffer losses when it is unable to execute stock
transactions in a timely manner. An electronic retailer may lose
customers when their online presence is perceived by customers as
sluggish. An online gaming provider may not be able to provide fair
access to online players, resulting in frustrated players who are
lost as customers. In each case, the failure of a service provider
to meet promised service level guarantees can have a substantial
financial impact on users of the service. By the same token, there
is the loss that is incurred by the provider of a service who is
unable to meet promised service level objectives. Those losses can
take several forms, such as penalties for not meeting the service
and, in many cases more important, loss of revenue due to reduced
customer satisfaction. Hence, service level objectives are a key
concern for the service provider. In order to ensure that SLOs are
not being violated, service levels need to be continuously monitored
at the network infrastructure layer in order to know, for example,
when mitigating actions need to be taken. To that end, service level
measurements must take place.
Network measurements can be performed using active or passive
measurement techniques. In passive measurements, production traffic
is observed and no monitoring traffic is created by the measurement
process itself. That is, network conditions are checked in a non
intrusive way. In the context of IP Flow Information eXport (IPFIX),
several documents were produced that define how to export data
associated with flow records, i.e. data that is collected as part of
passive measurement mechanisms, generally applied against flows of
production traffic (e.g., [RFC7011]). In addition, it would be
possible to collect real data traffic (not just summarized flow
records) with time-stamped packets, possibly sampled (e.g., per
[RFC5474], as a means of measuring and inferring service levels.
Active measurements, on the other hand, are more intrusive to the
network in the sense that it involves injecting synthetic test
traffic into the network to measure network service levels, as
opposed to simply observing production traffic. The IP Performance
Metrics (IPPM) WG produced documents that describe active measurement
mechanisms, such as: One-Way Active Measurement Protocol (OWAMP)
[RFC4656], Two-Way Active Measurement Protocol (TWAMP) [RFC5357], and
Nobre, et al. Expires May 19, 2018 [Page 3]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
Cisco Service Level Assurance Protocol (SLA) [RFC6812]. In addition,
there are some mechanisms that do not cleanly fit into either active
or passive categories, such as Performance and Diagnostic Metrics
Destination Option (PDM) techniques [RFC8250].
Active measurement mechanisms offer a high level of control of what
and how to measure. They do not require inspecting production
traffic. Because of this, active measurements usually offer better
accuracy and privacy than passive measurement mechanisms. Traffic
encryption and regulations that limit the amount of payload
inspection that can occur are non-issues. Furthermore, active
measurement mechanisms are able to detect end-to-end network
performance problems in a fine-grained way (e.g., simulating the
traffic that must be handled considering specific Service Level
Objectives - SLOs). As a result, active measurements are often
preferred over passive measurement for SLA monitoring. Measurement
probes must be hosted in network devices and measurement sessions
must be activated to compute the current network metrics (e.g.,
considering those described in [RFC4148]). This activation should be
dynamic in order to follow changes in network conditions, such as
those related with routes being added or new customer demands.
While offering many advantages, active measurements are expensive in
terms of network resource consumption. Active measurements generally
involve measurement probes that generate synthetic test traffic that
is directed at a responder. The responder needs to timestamp test
traffic it receives and reflect it back to the originating
measurement probe. The measurement probe subsequently processes the
returned packets along with time stamping information in order to
compute service levels. Accordingly, active measurements consume
substantial CPU cycles as well as memory of network devices to
generate and process test traffic. In addition, synthetic traffic
increases network load. Active measurements thus compete for
resources with other functions, including routing and switching.
The resources required and traffic generated by the active
measurement sessions are to a large part a function of the number of
measured network destinations. (In addition, the amount of traffic
generated for each measurement plays a role, which in turn influences
the accuracy of the measurement.) The more destinations are being
measured, the larger the amount of resources consumed and traffic
needed to perform the measurements. Thus, to have a better
monitoring coverage it is necessary to deploy more sessions which
consequently increases consumed resources. Otherwise, enabling the
observation of just a small subset of all network flows can lead to
an insufficient coverage.
Nobre, et al. Expires May 19, 2018 [Page 4]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
Furthermore, while some end-to-end service levels can be determined
by adding up the service levels observed across different path
segments, the same is not true for all service levels. For example,
the end-to-end delay or packet loss from a node A to a node C routed
via a node B can often be computed simply by adding delays (or loss)
from A to B, and B to C. This allows to decompose a large set of
end-to-end measurements into a much smaller set of segment
measurements. However, end-to-end jitter and (for example) Mean
Opinion Scores cannot be decomposed as easily and, for higher
accuracy, must be measured end-to-end.
Hence, the decision how to place measurement probes becomes an
important management activity. The goal is to obtain maximum
benefits of service level monitoring with a limited amount of
measurement overhead. Specifically, the goal is to maximize the
number of service level violations that are detected with a limited
amount of resources.
The use case and the solution approach described in this document
address an important practical issue. They are intended to provide a
basis for further experimentation to lead into solutions for wider
deployment. This document represents the consensus of the IRTF's
Network Management Research Group (NMRG). It was discussed
extensively and received three separate in-depth reviews.
2. Definitions and Acronyms
Active Measurements: Techniques to measure service levels that
involve generating and observing synthetic test traffic
Passive Measurements: Techniques used to measure service levels based
on observation of production traffic
AN: Autonomic Network; a network containing exclusively autonomic
nodes, requiring no configuration and deriving all required
information through self-knowledge, discovery, or intent.
Autonomic Service Agent (ASA): An agent implemented on an autonomic
node that implements an autonomic function, either in part (in the
case of a distributed function, as in the context of this document),
or whole.
Measurement Session: A communications association between a Probe and
a Responder used to send and reflect synthetic test traffic for
active measurements
Probe: The source of synthetic test traffic in an active measurement
Nobre, et al. Expires May 19, 2018 [Page 5]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
Responder: The destination for synthetic test traffic in an active
measurement
SLA: Service Level Agreement
SLO: Service Level Objective
P2P: Peer-to-Peer
(Note: definitions of AN and ASA are borrowed from [RFC7575]).
3. Current Approaches
The current best practice in feasible deployments of active
measurement solutions to distribute the available measurement
sessions along the network consists in relying entirely on the human
administrator expertise to infer which would be the best location to
activate such sessions. This is done through several steps. First,
it is necessary to collect traffic information in order to grasp the
traffic matrix. Then, the administrator uses this information to
infer which are the best destinations for measurement sessions.
After that, the administrator activates sessions on the chosen subset
of destinations considering the available resources. This practice,
however, does not scale well because it is still labor intensive and
error-prone for the administrator to determine which sessions should
be activated given the set of critical flows that needs to be
measured. Even worse, this practice completely fails in networks
whose critical flows are too short in time and dynamic in terms of
traversing network path, like in modern cloud environments. That is
so because fast reactions are necessary to reconfigure the sessions
and administrators are just not quick enough in computing and
activating the new set of required sessions every time the network
traffic pattern changes. Finally, the current active measurements
practice usually covers only a fraction of the network flows that
should be observed, which invariably leads to the damaging
consequence of undetected SLA violations.
4. Use Case Description
The use case involves a service level provider who needs to monitor
the network to detect service level violations using active service
level measurements, and wants to be able to do so with minimal human
intervention. The goal is to conduct the measurements in an
effective manner maximizing the percentage of detected service level
violations. The service level provider has a bounded resource budget
with regards to measurements that can be performed, specifically,
with regards to the number of measurements that can be conducted
concurrently from any one network device, and possibly with regards
Nobre, et al. Expires May 19, 2018 [Page 6]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
to the total amount of measurement traffic on the network. However,
while at any one point in time the number of measurements conducted
is limited, it is possible for a device to change which destinations
to measure over time. This can be exploited to achieve a balance of
eventually covering all possible destinations using a reasonable
amount of "sampling" where measurement coverage of a destination
cannot be continuous. The solution needs to be dynamic and be able
to cope with network conditions which may change over time. The
solution should also be embeddable inside network devices that
control the deployment of active measurement mechanisms.
The goal is to conduct the measurements in a smart manner that
ensures that the network is broadly covered and the likelihood of
detecting service level violations is maximized. In order to
maximize that likelihood, it is reasonable to focus measurement
resources on destinations that are more likely to incur a violation,
while spending less resources on destinations that are more likely to
be in compliance. In order to do so, there are various aspects that
can be exploited, including past measurements (destinations close to
a service level threshold requiring more focus than destinations
further from it), complementation with passive measurements such as
flow data (to identify network destinations that are currently
popular and critical), and observations from other parts of the
network. In addition, measurements can be coordinated among
different network devices to avoid hitting the same destination at
the same time and to be able to share results that may be useful in
future probe placement.
Clearly, static solutions will have severe limitations. At the same
time, human administrators cannot be in the loop for continuous
dynamic measurement probe reconfigurations. Accordingly, an
automated or, ideally, autonomic solution is needed in which network
measurements are automatically orchestrated and dynamically
reconfigured from within the network. This can be accomplished using
an autonomic solution that is distributed, using Autonomic Service
Agents that are implemented on nodes in the network.
5. A Distributed Autonomic Solution
The use of Autonomic Networking (AN) [RFC7575] can help such
detection through an efficient activation of measurement sessions.
Such an approach, along with a detailed assessment confirming its
viability, has been described [P2PBNM-Nobre-2012]. The problem to be
solved by AN in the present use case is how to steer the process of
Measurement Session activation by a complete solution that sets all
necessary parameters for this activation to operate efficiently,
reliably and securely, with no required human intervention other than
setting overall policy.
Nobre, et al. Expires May 19, 2018 [Page 7]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
When a node first comes online, it has no information about which
measurements are more critical than others. In the absence of
information about past measurements and information from measurement
peers, it may start with an initial set of measurement sessions,
possibly randomly seeding a set of starter measurements, perhaps
taking a round robin approach for subsequent measurement rounds.
However, as measurements are collected, a node will gain increasing
information that it can utilize to refine its strategy of selecting
measurement targets going forward. For one, it may take note of
which targets returned measurement results very close to service
level thresholds that may therefore require closer scrutiny compared
to others. Second, it may utilize observations that are made by its
measurement peers in order to conclude which measurement targets may
be more critical than others, and in order to ensure that proper
overall measurement coverage is obtained (so that not every node
incidentally measure the same targets, while other targets are not
measured at all).
We advocate for embedding Peer-to-Peer (P2P) technology in network
devices in order to conduct the Measurement Session activation
decisions using autonomic control loops. Specifically, we advocate
for network devices to implement an autonomic function to monitor
service levels for violations of service level objectives,
determining which Measurement Sessions to set up at any given point
in time based on current and past observations of the node, and of
other peer nodes.
By performing these functions locally and autonomically on the device
itself, which measurements to conduct can be modified quickly based
on local observations while taking local resource availability into
account. This allows a solution to be more robust and react more
dynamically to rapidly changing service levels than a solution that
has to rely on central coordination. However, in order to optimize
decisions which measurements to conduct, a node will need to
communicate with other nodes. This allows a node to take into
account other nodes' observations in addition to its own in its
decisions.
For example, remote destinations whose observed service levels are on
the verge of violating stated objectives may require closer
monitoring than remote destinations that are comfortably within a
range of tolerance. It also allows nodes to coordinate their probing
decisions to collectively achieve the best possible measurement
coverage. As the amount of resources available for monitoring and
for exchange of measurement data and coordination with other nodes
are limited, a node may further be interested in identifying other
nodes whose observations are most similar to and correlated with its
own. This helps a node prioritize and guide with which other nodes
Nobre, et al. Expires May 19, 2018 [Page 8]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
to primarily coordinate and exchange data with. All of this requires
the use of a P2P overlay.
A P2P overlay is essential for several reasons:
o It makes it possible for nodes (respectively Autonomic Service
Agents that are deployed on those nodes) in the network to
autonomically set up Measurement Sessions, without having to rely
on central management system or controller to perform
configuration operations associated with configuring measurement
probes and responders.
o It facilitates the exchange of data between different nodes to
share measurement results so that each node can refine its
measurement strategy based not just its own observations, but
observations from its peers.
o It allows nodes to coordinate their measurements to obtain the
best possible test coverage and avoid measurements that have a
very low likelihood of detecting service level violations.
The provisioning of the P2P overlay should be transparent for the
network administrator. An Autonomic Control Plane such as defined in
[I-D.anima-autonomic-control-plane] provides an ideal candidate for
the P2P overlay to run on.
An autonomic solution for the distributed detection of SLA violations
provide several benefits. First, efficiency: this solution should
optimize the resource consumption and avoid resource starvation on
the network devices. A device that is "self-aware" of its available
resources will be able to adjust measurement activities rapidly as
needed, without requiring a separate control loop involving resource
monitoring by an external system. Secondly, placing logic where to
conduct measurements in the node enables rapid control loops in which
devices are able to react instantly to observations and adjust their
measurement strategy. For example, a device could decide to adjust
the amount of synthetic test traffic being sent during the
measurement itself depending on results observed so far on this and
on other concurrent measurement sessions. As a result, the solution
could decrease the time necessary to detect SLA violations.
Adaptivity features of an autonomic loop could capture faster the
network dynamics than an human administrator and even a central
controller. Finally, the solution could help to reduce the workload
of human administrator, or, at least, to avoid their need to perform
operational tasks.
In practice, these factors combine to maximize the likelihood of SLA
violations being detected while operating within a given resource
Nobre, et al. Expires May 19, 2018 [Page 9]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
budget, allowing to conduct a continuous measurement strategy that
takes into account past measurement results, observations of other
measures such as link utilization or flow data, sharing of
measurement results between network devices, and coordinating future
measurement activities among nodes. Combined this can result in
efficient measurement decisions that achieve a golden balance between
broad network coverage and honing in on service level "hot spots".
6. Intended User Experience
The autonomic solution should not require any human intervention in
the distributed detection of SLA violations. By virtue of the
solution being autonomic, human users will not have to plan which
measurements to conduct in a network, often a very labor intensive
task today that requires detailed analysis of traffic matrices and
network topologies and is not prone to easy dynamic adjustment.
Likewise, they will not have to configure measurement probes and
responders.
There are some ways in which a human administrator may still interact
with the solution. For one, the human administrator will of course
be notified and obtain reports about service level violations that
are observed. Second, a human administrator may set a policies
regarding how closely to monitor the network for service level
violations and how many resources to spend. For example, an
administrator may set a resource budget that is assigned to network
devices for measurement operations. With that given budget, the
number of SLO violations that are detected will be maximized.
Alternatively, an administrator may set a target for the percentage
of SLO violations that must be detected, i.e. a target for the ratio
between the number of detected SLO violations, and the number of
total SLO violations that are actually occurring (some of which might
go undetected). In that case, the solution will aim to minimize the
resources spent (i.e. the amount of test traffic and Measurement
Sessions) that are required to achieve that target.
7. Implementation Considerations
The active measurement model assumes that a typical infrastructure
will have multiple network segments and Autonomous Systems (ASs), and
a reasonably large number of routers. It also considers that
multiple SLOs can be in place at a given time. Since
interoperability in a heterogenous network is a goal, features found
on different active measurement mechanisms (e.g. OWAMP, TWAMP, and
IPSLA) and device programability interfaces (such as Juniper's Junos
API or Cisco's Embedded Event Manager) could be used for the
implementation. The autonomic solution should include and/or
reference specific algorithms, protocols, metrics and technologies
Nobre, et al. Expires May 19, 2018 [Page 10]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
for the implementation of distributed detection of SLA violations as
a whole.
Finally, it should be noted that there are multiple deployment
scenarios, including deployment scenarios that involve physical
devices hosting autonomic functions, or virtualized infrastructure
hosting the same. Co-deployment in conjunction with Virtual Network
Functions (VNF) is a possibility for further study.
7.1. Device Based Self-Knowledge and Decisions
Each device has self-knowledge about the local SLA monitoring. This
could be in the form of historical measurement data and SLOs.
Besides that, the devices would have algorithms that could decide
which probes should be activated in a given time. The choice of
which algorithm is better for a specific situation would be also
autonomic.
7.2. Interaction with other devices
Network devices should share information about service level
measurement results. This information can speed up the detection of
SLA violations and increase the number of detected SLA violations.
For example, if one device detects that a remote destination is in
danger of violating an SLO, other devices may conduct additional
measurements to the same destination or other destinations in its
proximity. For any given network device, the exchange of data may be
more important with some devices (for example, devices in the same
network neighborhood, or devices that are "correlated" by some other
means) than with others. The definition of network devices that
exchange measurement data, i.e., management peers, creates a new
topology. Different approaches could be used to define this topology
(e.g., correlated peers [P2PBNM-Nobre-2012]). To bootstrap peer
selection, each device should use its known endpoints neighbors
(e.g., FIB and RIB tables) as the initial seed to get possible peers.
It should be noted that a solution will benefit if topology
information and network discovery functions are provided by the
underlying autonomic framework. A solution will need to be able to
discover measurement peers as well as measurement targets,
specifically measurement targets that support active measurement
responders and which will be able to respond to measurement requests
and reflect measurement traffic as needed.
8. Comparison with current solutions
There is no standardized solution for distributed autonomic detection
of SLA violations. Current solutions are restricted to ad hoc
scripts running on a per node fashion to automate some
Nobre, et al. Expires May 19, 2018 [Page 11]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
administrator's actions. There are some proposals for passive probe
activation (e.g., DECON and CSAMP), but without the focus on
autonomic features.
9. Related IETF Work
The following paragraphs discuss related IETF work and are provided
for reference. This section is not exhaustive, rather it provides an
overview of the various initiatives and how they relate to autonomic
distributed detection of SLA violations.
1. [LMAP]: The Large-Scale Measurement of Broadband Performance
Working Group aims at the standards for performance management.
Since their mechanisms also consist in deploying measurement
probes the autonomic solution could be relevant for LMAP
specially considering SLA violation screening. Besides that, a
solution to decrease the workload of human administrators in
service providers is probably highly desirable.
2. [IPFIX]: IP Flow Information EXport (IPFIX) aims at the process
of standardization of IP flows (i.e., netflows). IPFIX uses
measurement probes (i.e., metering exporters) to gather flow
data. In this context, the autonomic solution for the activation
of active measurement probes could be possibly extended to
address also passive measurement probes. Besides that, flow
information could be used in the decision making of probe
activation.
3. [ALTO]: The Application Layer Traffic Optimization Working Group
aims to provide topological information at a higher abstraction
layer, which can be based upon network policy, and with
application-relevant service functions located in it. Their work
could be leveraged for the definition of the topology regarding
the network devices which exchange measurement data.
10. Acknowledgements
We wish to acknowledge the helpful contributions, comments, and
suggestions that were received from Mohamed Boucadair, Brian
Carpenter, Hanlin Fang, Bruno Klauser, Diego Lopez, Vincent Roca, and
Eric Voit. In addition, we thank Diego Lopez, Vincent Roca, and
Brian Carpenter for their detailed reviews.
11. IANA Considerations
This memo includes no request to IANA.
Nobre, et al. Expires May 19, 2018 [Page 12]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
12. Security Considerations
Security of the solution hinges on the security of the network
underlay, i.e. the Autonomic Control Plane. If the Autonomic Control
Plane were to be compromised, an attacker could undermine the
effectiveness of measurement coordination by reporting fraudulent
measurement results to peers. This would cause measurement probes to
be deployed in an ineffective manner that would increase the
likelihood that violations of service level objectives go undetected.
Likewise, security of the solution hinges on the security of the
deployment mechanism for autonomic functions, in this case, the
autonomic function that conducts the service level measurements. If
an attacker were able to hijack an autonomic function, it could try
to exhaust or exceed the resources that should be spent on autonomic
measurements in order to deplete network resources, including network
bandwidth due to higher-than-necessary volumes of synthetic test
traffic generated by measurement probes. Again, it could also lead
to reporting of misleading results, among other things resulting in
non-optimal selection of measurement targets and in turn an increase
in the likelihood that service level violations go undetected.
13. Informative References
[draft-anima-boot]
Pritikin, M., Richardson, M., Behringer, M., Bjarnason,
S., and K. Watsen, "draft-ietf-anima-bootstrapping-
keyinfra", draft-ietf-anima-bootstrapping-keyinfra-08
(work in progress), October 2017.
[I-D.anima-autonomic-control-plane]
Behringer, M., Eckert, T., and S. Bjarnason, "An Autonomic
Control Plane", draft-ietf-anima-autonomic-control-
plane-12 (work in progress), October 2017.
[P2PBNM-Nobre-2012]
Nobre, J., Granville, L., Clemm, A., and A. Gonzalez
Prieto, "Decentralized Detection of SLA Violations Using
P2P Technology, 8th International Conference Network and
Service Management (CNSM)", 2012,
<http://ieeexplore.ieee.org/xpls/
abs_all.jsp?arnumber=6379997>.
[RFC4148] Stephan, E., "IP Performance Metrics (IPPM) Metrics
Registry", BCP 108, RFC 4148, DOI 10.17487/RFC4148, August
2005, <https://www.rfc-editor.org/info/rfc4148>.
Nobre, et al. Expires May 19, 2018 [Page 13]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
[RFC4656] Shalunov, S., Teitelbaum, B., Karp, A., Boote, J., and M.
Zekauskas, "A One-way Active Measurement Protocol
(OWAMP)", RFC 4656, DOI 10.17487/RFC4656, September 2006,
<https://www.rfc-editor.org/info/rfc4656>.
[RFC5357] Hedayat, K., Krzanowski, R., Morton, A., Yum, K., and J.
Babiarz, "A Two-Way Active Measurement Protocol (TWAMP)",
RFC 5357, DOI 10.17487/RFC5357, October 2008,
<https://www.rfc-editor.org/info/rfc5357>.
[RFC5474] Duffield, N., Ed., Chiou, D., Claise, B., Greenberg, A.,
Grossglauser, M., and J. Rexford, "A Framework for Packet
Selection and Reporting", RFC 5474, DOI 10.17487/RFC5474,
March 2009, <https://www.rfc-editor.org/info/rfc5474>.
[RFC6812] Chiba, M., Clemm, A., Medley, S., Salowey, J., Thombare,
S., and E. Yedavalli, "Cisco Service-Level Assurance
Protocol", RFC 6812, DOI 10.17487/RFC6812, January 2013,
<https://www.rfc-editor.org/info/rfc6812>.
[RFC7011] Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
"Specification of the IP Flow Information Export (IPFIX)
Protocol for the Exchange of Flow Information", STD 77,
RFC 7011, DOI 10.17487/RFC7011, September 2013,
<https://www.rfc-editor.org/info/rfc7011>.
[RFC7297] Boucadair, M., Jacquenet, C., and N. Wang, "IP
Connectivity Provisioning Profile (CPP)", RFC 7297,
DOI 10.17487/RFC7297, July 2014,
<https://www.rfc-editor.org/info/rfc7297>.
[RFC7575] Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
Networking: Definitions and Design Goals", RFC 7575,
DOI 10.17487/RFC7575, June 2015,
<https://www.rfc-editor.org/info/rfc7575>.
[RFC8250] Elkins, N., Hamilton, R., and M. Ackermann, "IPv6
Performance and Diagnostics Metrics (PDM) Destination
Option", RFC 8250, October 2017.
Authors' Addresses
Nobre, et al. Expires May 19, 2018 [Page 14]
Internet-Draft AN Use Case Detection of SLA Violations November 2017
Jeferson Campos Nobre
University of Vale do Rio dos Sinos
Porto Alegre
Brazil
Email: jcnobre@unisinos.br
Lisandro Zambenedetti Granvile
Federal University of Rio Grande do Sul
Porto Alegre
Brazil
Email: granville@inf.ufrgs.br
Alexander Clemm
Huawei
Santa Clara, California
USA
Email: ludwig@clemm.org
Alberto Gonzalez Prieto
VMware
Palo Alto, California
USA
Email: agonzalezpri@vmware.com
Nobre, et al. Expires May 19, 2018 [Page 15]
