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          13 rfc8316                                                    
Network Management Research Group                               J. Nobre
Internet-Draft                       University of Vale do Rio dos Sinos
Intended status: Informational                              L. Granville
Expires: September 3, 2017       Federal University of Rio Grande do Sul
                                                                A. Clemm
                                                      A. Gonzalez Prieto
                                                           Cisco Systems
                                                           March 2, 2017

     Autonomic Networking Use Case for Distributed Detection of SLA


   This document describes a use case for autonomic networking in
   distributed detection of Service Level Agreement (SLA) violations.

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 http://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 September 3, 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
   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
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   to this document.  Code Components extracted from this document must

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   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  . . . . . . . . . . . . . . . . . . . . .   5
   4.  Use Case Description  . . . . . . . . . . . . . . . . . . . .   6
   5.  A Distributed Autonomic Solution  . . . . . . . . . . . . . .   7
   6.  Intended User and Administrator Experience  . . . . . . . . .   8
   7.  Analysis of Parameters and Information Involved . . . . . . .   8
     7.1.  Device Based Self-Knowledge and Decisions . . . . . . . .   8
     7.2.  Interaction with other devices  . . . . . . . . . . . . .   9
   8.  Comparison with current solutions . . . . . . . . . . . . . .   9
   9.  Related IETF Work . . . . . . . . . . . . . . . . . . . . . .   9
   10. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  10
   11. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  10
   12. Security Considerations . . . . . . . . . . . . . . . . . . .  10
   13. Informative References  . . . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  12

1.  Introduction

   The Internet has been growing dramatically in terms of size and
   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
   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

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   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)
   WG, several documents were produced to define passive measurement
   mechanisms (e.g., flow records specification [RFC3954]).  Active
   measurements, on the other hand, are intrusive in the sense that it
   involves injecting synthetic test traffic into the network to measure
   network service levels.  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 Cisco Service
   Level Assurance Protocol (SLA) [RFC6812].  In addition, there are
   some mechanisms that do not fit into either active or passive
   categories, such as Performance and Diagnostic Metrics Destination
   Option (PDM) techniques [draft-ietf-ippm-6man-pdm-option].

   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

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   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 turns increases consumed resources.  Otherwise, enabling
   the observation of just a small subset of all network flows can lead
   to an insufficient coverage.

   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.

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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

   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

   Responder: The destination for synthetic test traffic in an active

   SLA: Service Level Agreement

   SLO: Service Level Objective

   P2P: Peer-to-Peer

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 not just 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

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   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.  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), an 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.

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5.  A Distributed Autonomic Solution

   The use of Autonomic Networking (AN) can help such detection through
   an efficient activation of measurement sessions [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.

   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.  This requires the use of a
   P2P overlay.  A P2P overlay is important for several reasons:

   o  It makes it possible for 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

   o  It facilitates the exchange local data between different devices
      that is used to coordinate measurements and to share measurement
      results to refine measurement strategy.

   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's underlay.

   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

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   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
   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 and Administrator Experience

   The autonomic solution should not require human intervention in the
   distributed detection of SLA violations.  This also enables SLA
   monitoring of a network by less experienced human administrators.
   However, some information may be provided from the human
   administrator.  For example, the human administrator may set a policy
   regarding the resource budget that is assigned to network devices for
   measurement operations, or set a target for the number or percentage
   of SLO violations that must be detected allowing the solution to
   minimize the resources required to achieve that target.

7.  Analysis of Parameters and Information Involved

   The active measurement model assumes that a typical infrastructure
   will have multiple network segments and Autonomous Systems (ASs), and
   a reasonably large number of several of routers and hosts.  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
   for the implementation of distributed detection of SLA violations as
   a whole.

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

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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
   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.

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
   administrator's actions.  There some proposals for passive probe
   activation (e.g., DECON and CSAMP), but without the focus on
   autonomic features.  It is also mentioning a proposal from Barford et
   al. to detect and localize links which cause anomalies along a
   network path.

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

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   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

10.  Acknowledgements

   We wish to acknowledge the helpful contributions, comments, and
   suggestions that were received from Mohamed Boucadair, Bruno Klauser,
   Eric Voit, and Hanlin Fang.

11.  IANA Considerations

   This memo includes no request to IANA.

12.  Security Considerations

   The bootstrapping of a new device follows the approach proposed on
   anima wg [draft-anima-boot], thus in order to exchange data a device
   should register first.  This registration could be performed by a
   "Registrar" device or a cloud service provided by the organization to
   facilitate autonomic mechanisms.  The new device sends its own
   credentials to the Registrar, and after successful authentication,
   receives domain information, to enable subsequent enrollment to the
   domain.  The Registrar sends all required information: a device name,
   domain name, plus some parameters for the operation.  Measurement
   data should be exchanged signed and encrypted among devices since
   these data could carry sensible information about network
   infrastructures.  Some attacks should be considering when analyzing
   the security of the autonomic solution.  Denial of service (DoS)
   attacks could be performed if the solution be tempered to active more
   local probe than the available resources allow.  Besides that,
   results could be forged by a device (attacker) in order to this
   device be considered peer of a specific device (target).  This could
   be done to gain information about a network.

13.  Informative References

              Pritikin, M., Richardson, M., Behringer, M., and S.
              Bjarnason, "draft-ietf-anima-bootstrapping-keyinfra",
              draft-ietf-anima-bootstrapping-keyinfra-04 (work in
              progress), January 2017.

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              Elkins, N., Hamilton, R., and M. Ackermann, "draft-ietf-
              ippm-6man-pdm-option", draft-ietf-ippm-6man-pdm-option-08
              (work in progress), February 2017.

              Behringer, M., Eckert, T., and S. Bjarnason, "An Autonomic
              Control Plane", draft-ietf-anima-autonomic-control-
              plane-05 (work in progress), January 2017.

              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,

   [RFC3954]  Claise, B., Ed., "Cisco Systems NetFlow Services Export
              Version 9", RFC 3954, DOI 10.17487/RFC3954, October 2004,

   [RFC4148]  Stephan, E., "IP Performance Metrics (IPPM) Metrics
              Registry", BCP 108, RFC 4148, DOI 10.17487/RFC4148, August
              2005, <http://www.rfc-editor.org/info/rfc4148>.

   [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,

   [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,

   [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,

   [RFC7297]  Boucadair, M., Jacquenet, C., and N. Wang, "IP
              Connectivity Provisioning Profile (CPP)", RFC 7297,
              DOI 10.17487/RFC7297, July 2014,

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Authors' Addresses

   Jeferson Campos Nobre
   University of Vale do Rio dos Sinos
   Porto Alegre

   Email: jcnobre@unisinos.br

   Lisandro Zambenedetti Granvile
   Federal University of Rio Grande do Sul
   Porto Alegre

   Email: granville@inf.ufrgs.br

   Alexander Clemm
   Santa Clara, California

   Email: ludwig@clemm.org

   Alberto Gonzalez Prieto
   Cisco Systems
   San Jose

   Email: albertgo@cisco.com

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