Quality of Service for ICN in the IoT
draft-gundogan-icnrg-iotqos-00

ICN Research Group                                           C. Gundogan
Internet-Draft                                               TC. Schmidt
Intended status: Experimental                                HAW Hamburg
Expires: September 29, 2019                                 M. Waehlisch
                                                    link-lab & FU Berlin
                                                                 M. Frey
                                                       F. Shzu-Juraschek
                                                               Safety IO
                                                              J. Pfender
                                                                     VUW
                                                          March 28, 2019

                 Quality of Service for ICN in the IoT
                     draft-gundogan-icnrg-iotqos-00

Abstract

   This document describes manageable resources in ICN IoT deployments
   and a lightweight traffic classification method for mapping
   priorities to resources.  Management methods are further derived for
   controlling latency and reliability of traffic flows in constrained
   environments.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Manageable Resources in the IoT . . . . . . . . . . . . . . .   3
     3.1.  Link Layer  . . . . . . . . . . . . . . . . . . . . . . .   3
     3.2.  Pending Interest Table  . . . . . . . . . . . . . . . . .   4
     3.3.  Content Store . . . . . . . . . . . . . . . . . . . . . .   4
   4.  Traffic Flow Classification . . . . . . . . . . . . . . . . .   4
   5.  Priority Handling . . . . . . . . . . . . . . . . . . . . . .   5
     5.1.  Link Layer  . . . . . . . . . . . . . . . . . . . . . . .   5
     5.2.  Pending Interest Table  . . . . . . . . . . . . . . . . .   5
     5.3.  Content Store . . . . . . . . . . . . . . . . . . . . . .   6
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   6
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   8.  Informative References  . . . . . . . . . . . . . . . . . . .   6
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .   8
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   8

1.  Introduction

   The performance of networked systems is largely determined by the
   resources available for forwarding messages between components.  In
   addition to link capacities and buffer queues, Information-centric
   Networks rely on additional resources that shape its overall
   performance, namely Pending Interest Table space, and caching
   capacity.

   Typical IoT deployments add tight resource constraints to this
   picture [RFC7228]: Nodes have processing and memory limitations, the
   underlying link layer technologies are lossy and restricted in
   bandwidth.  Particularly in multi-hop networks, such constraints
   affect the overall performance, create bottlenecks, but may lead to
   cascading packet loss or energy depletion when PIT resources are
   independently evicted and forwarding states decorrelate
   [DECORRELATION].  Overprovisioning to counter performance flaws is
   infeasible for many IoT scenarios as it inflicts with use cases and
   increases deployment costs.  Quality of Service (QoS) is a method to
   enhance overall performance by redistributing resources to a subset
   of messages, and - in the constrained IoT use case - to coordinate
   operations under resource scarcity.

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   IoT applications follow various use cases, of which different QoS
   requirements can be derived.  While periodic sensor readings often
   comply with unmanaged performance, industrial control messaging or
   security alerts require (very) low latency, and safety-critical
   environmental recording or network management need (highly) reliable
   network services.  Both quality levels can only be assured by
   appropriate resource reservations.

   In order to achieve a QoS-aware information-centric IoT deployment,
   this document describes manageable resources in Section 3, defines a
   flow classification method in Section 4, and specifies priorities and
   their mappings in Section 5.

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].
   The use of the term, "silently ignore" is not defined in RFC 2119.
   However, the term is used in this document and can be similarly
   construed.

   This document uses the terminology of [RFC7476], [RFC7927], and
   [RFC7945] for ICN entities.

   The following terms are used in the document and defined as follows:

   Traffic Flow  A traffic flow is a sequence of messages (Interest and
                 data) that belong to one specific communication
                 context.  Due to in-network caching, ICN flows may be
                 delocalized.  A flow may also relate to several
                 requesters in the presence of Interest aggregation.

3.  Manageable Resources in the IoT

   The following resources contribute to the overall network performance
   in Information-Centric IoT Networking and need management for QoS
   control.

3.1.  Link Layer

   The link layer manages access to the media and provides space to
   buffer packets.  Low latency applications require that requests are
   prioritized compared to regular priority data.  Based on the request
   response pattern of ICN, link layer resources can be preallocated for
   data packets.

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3.2.  Pending Interest Table

   The Pending Interest Table (PIT) stores open requests at each hop.
   PIT resources are allocated when requests are forwarded, and they are
   released on returning responses.

   Placement and replacement strategies of PIT entries directly
   influence the latency and reliability properties of traffic flows and
   thus should consider prioritization schemes.  If the PIT is not
   saturated new PIT entries can be added.  If the PIT is saturated,
   requests with higher priority should replace requests with lower
   priority to prevent higher latencies due to retransmissions.

3.3.  Content Store

   Content stores (CS) enable transparent in-network caching and thus
   improve the transport in wireless and lossy environments by reducing
   hop traversals for content requests [NDN-EXP].

   Placement and replacement strategies of data in content stores can
   affect the latency and reliability properties of traffic flows.  The
   latency can be reduced by placing data closer to the consumers.
   Reliability can be improved by replicating data in multiple content
   stores to be resilient to node failures.

4.  Traffic Flow Classification

   This document defines a traffic flow classification mechanism that
   aggregates names into equivalence classes in order to apply resource
   allocation decisions on messages of particular traffic flows.

   A traffic class is a name prefix and each device maintains a list of
   valid classes.  The actual distribution of traffic classes is out of
   scope of this document.  The classes for request and response
   messages are derived by performing a longest prefix match (LPM) with
   the list of valid traffic classes and the Name TLV of the message.
   Examples are given in Figure 1.

    list =
    ["/org", "/org /Hamburg", "/org /Berlin", "/org /Berlin /sensor" ]

    LPM("/com"                      ,list) = ""
    LPM("/org /Germany"             ,list) = "/org"
    LPM("/org /Hamburg"             ,list) = "/org /Hamburg"
    LPM("/org /Berlin /sensor /temp",list) = "/org /Berlin /sensor"

               Figure 1: Example traffic flow class matches.

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   The empty traffic class "" is the default class for messages that do
   not match any valid traffic classes in the class list.

5.  Priority Handling

   We define two priority levels to set the priorities for traffic flows
   in regards to latency and reliability.

   o  Latency:

      *  EXPEDITED

      *  REGULAR

   o  Reliability:

      *  RELIABLE

      *  REGULAR

   Each list entry of the traffic class list from Section 4 has an
   associated priority tuple which distinctly specifies priority levels
   for the resources in Section 3.  The tuple is of the following form:

        priority tuple = < LATENCY_PRIORITY, RELIABILITY_PRIORITY >

                  Figure 2: Schema of the priority tuple.

5.1.  Link Layer

   As described above, the link layer provides space to buffer outgoing
   packets.  For the two latency priorities, this space can be allocated
   into the following two queues:

   o  EXPEDITED_FORWARDING_QUEUE

   o  REGULAR_FORWARDING_QUEUE

   Packets will be appended to the queue corresponding to their priority
   level.

5.2.  Pending Interest Table

   In unsatured PITs, requests are added as new entries regardless of
   the priority level.  In saturated PITs, EXPEDITED traffic replaces
   PIT entries of REGULAR traffic.  If all entries in a saturated PIT
   are of a higher priority than the incoming request, then we RECOMMEND
   to drop the incoming request.  If a saturated PIT contains entries of

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   the same priority as the incoming request, we RECOMMEND to drop the
   incoming request to avoid cancelling active but incomplete ICN
   operations.

5.3.  Content Store

   Nodes MAY implement a caching decision strategy instead of always
   caching all incoming content objects [ICN-CACHING].  If they do, the
   caching decision strategy MUST take the content object priority into
   account, such that lower priority content is not cached if the cache
   is saturated with higher priority content.

   In unsaturated content stores, all content objects are passed to the
   cache decision strategy.

   In saturated content stores, reliable traffic flows MUST be passed to
   the cache replacement strategy.  Content objects with regular
   reliability requirements MUST be evicted first to make room for
   higher reliability content objects.  Traffic flows with regular
   latency and regular reliability requirements MUST be passed to the
   cache replacement strategy.  The cache replacement strategy MUST NOT
   evict content objects of higher reliability.  Expedited traffic flows
   with regular reliability MUST be passed to the cache replacement
   strategy.  Content objects with regular latency and regular
   reliability requirements MUST be evicted first, if an open PIT state
   is available.  Otherwise, if no PIT state is available, then the
   cache replacement strategy MAY replace content objects of expedited
   or regular latency requirements and with regular reliability
   requirements.

6.  Security Considerations

   TODO

7.  IANA Considerations

   TODO

8.  Informative References

   [DECORRELATION]
              Waehlisch, M., Schmidt, TC., and M. Vahlenkamp,
              "Backscatter from the Data Plane - Threats to Stability
              and Security in Information-Centric Network
              Infrastructure", Computer Networks Vol 57, No. 16, pp.
              3192-3206, November 2013.

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   [I-D.moiseenko-icnrg-flowclass]
              Moiseenko, I. and D. Oran, "Flow Classification in
              Information Centric Networking", draft-moiseenko-icnrg-
              flowclass-03 (work in progress), January 2019.

   [ICN-CACHING]
              Chai, W., He, D., Psaras, I., and G. Pavlou, "Cache 'Less
              for More' in Information-Centric Networks (Extended
              Version)", Computer Communications 36, 7 (2013) pp.
              758-770, February 2013, <http://dx.doi.org/>.

   [NDN-EXP]  Gundogan, C., Kietzmann, P., Lenders, M., Petersen, H.,
              Schmidt, TC., and M. Waehlisch, "NDN, CoAP, and MQTT: A
              Comparative Measurement Study in the IoT", Proc. of 5th
              ACM Conf. on Information-Centric Networking (ICN-2018) ACM
              DL, pp. , September 2018, <http://dx.doi.org/>.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC7228]  Bormann, C., Ersue, M., and A. Keranen, "Terminology for
              Constrained-Node Networks", RFC 7228,
              DOI 10.17487/RFC7228, May 2014,
              <https://www.rfc-editor.org/info/rfc7228>.

   [RFC7476]  Pentikousis, K., Ed., Ohlman, B., Corujo, D., Boggia, G.,
              Tyson, G., Davies, E., Molinaro, A., and S. Eum,
              "Information-Centric Networking: Baseline Scenarios",
              RFC 7476, DOI 10.17487/RFC7476, March 2015,
              <https://www.rfc-editor.org/info/rfc7476>.

   [RFC7927]  Kutscher, D., Ed., Eum, S., Pentikousis, K., Psaras, I.,
              Corujo, D., Saucez, D., Schmidt, T., and M. Waehlisch,
              "Information-Centric Networking (ICN) Research
              Challenges", RFC 7927, DOI 10.17487/RFC7927, July 2016,
              <https://www.rfc-editor.org/info/rfc7927>.

   [RFC7945]  Pentikousis, K., Ed., Ohlman, B., Davies, E., Spirou, S.,
              and G. Boggia, "Information-Centric Networking: Evaluation
              and Security Considerations", RFC 7945,
              DOI 10.17487/RFC7945, September 2016,
              <https://www.rfc-editor.org/info/rfc7945>.

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Acknowledgments

   This work was stimulated by fruitful discussions in the ICNRG
   research group.  We would like to thank all active members for
   constructive thoughts and feedback.  In particular, the authors would
   like to thank Ilya Moiseenko and Dave Oran for their work provided in
   [I-D.moiseenko-icnrg-flowclass].  This work was supported in part by
   the German Federal Ministry of Research and Education within the I3
   project.

Authors' Addresses

   Cenk Gundogan
   HAW Hamburg
   Berliner Tor 7
   Hamburg  D-20099
   Germany

   Phone: +4940428758067
   EMail: cenk.guendogan@haw-hamburg.de
   URI:   http://inet.haw-hamburg.de/members/cenk-gundogan

   Thomas C. Schmidt
   HAW Hamburg
   Berliner Tor 7
   Hamburg  D-20099
   Germany

   EMail: t.schmidt@haw-hamburg.de
   URI:   http://inet.haw-hamburg.de/members/schmidt

   Matthias Waehlisch
   link-lab & FU Berlin
   Hoenower Str. 35
   Berlin  D-10318
   Germany

   EMail: mw@link-lab.net
   URI:   http://www.inf.fu-berlin.de/~waehl

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   Michael Frey
   Safety IO
   Franz-Ehrlich-Strasse 9
   Berlin  D-12489
   Germany

   EMail: michael.frey@safetyio.com

   Felix Shzu-Juraschek
   Safety IO
   Franz-Ehrlich-Strasse 9
   Berlin  D-12489
   Germany

   EMail: felix.juraschek@safetyio.com

   Jakob Pfender
   Victoria University of Wellington
   Kelburn Parade
   Wellington  NZ-6012
   New Zealand

   EMail: jpfender@ecs.vuw.ac.nz
   URI:   https://ecs.victoria.ac.nz/Main/GradJakobPfender

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