Quality of Service for ICN in the IoT

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ICN Research Group                                           C. Gundogan
Internet-Draft                                               TC. Schmidt
Intended status: Experimental                                HAW Hamburg
Expires: January 9, 2020                                    M. Waehlisch
                                                    link-lab & FU Berlin
                                                                 M. Frey
                                                       F. Shzu-Juraschek
                                                               Safety IO
                                                              J. Pfender
                                                            July 8, 2019

                 Quality of Service for ICN in the IoT


   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.  This work includes a distributed management of the
   heterogeneous resources (i) forwarding capacities, (ii) Pending
   Interest Table (PIT) space, and (iii) in-network data storage.  By
   correlating these common ICN resources, performance measures can be
   optimized without sacrificing concurrent traffic demands.  Different
   from the IP world, QoS in ICN can be benifical for all participants
   and manage 'quality instead of unfairness'.

Status of This Memo

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   This Internet-Draft will expire on January 9, 2020.

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Copyright Notice

   Copyright (c) 2019 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
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   (https://trustee.ietf.org/license-info) in effect on the date of
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Manageable Resources in the IoT . . . . . . . . . . . . . . .   3
     3.1.  Link Layer  . . . . . . . . . . . . . . . . . . . . . . .   4
     3.2.  Pending Interest Table  . . . . . . . . . . . . . . . . .   4
     3.3.  Content Store . . . . . . . . . . . . . . . . . . . . . .   4
   4.  Traffic Flow Classification . . . . . . . . . . . . . . . . .   4
   5.  Priority Handling . . . . . . . . . . . . . . . . . . . . . .   5
   6.  Distributed QoS Management  . . . . . . . . . . . . . . . . .   5
     6.1.  Locally Isolated Decisions  . . . . . . . . . . . . . . .   6
     6.2.  Local Resource Correlations . . . . . . . . . . . . . . .   6
     6.3.  Distributed Resource Coordination . . . . . . . . . . . .   7
   7.  Implementation Report and Guidance  . . . . . . . . . . . . .   7
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .   7
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   7
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .   8
     10.1.  Normative References . . . . . . . . . . . . . . . . . .   8
     10.2.  Informative References . . . . . . . . . . . . . . . . .   8
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10

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

   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

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   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.

   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",
   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

   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

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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.

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.

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    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.

   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:

      *  PROMPT

      *  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:


                  Figure 2: Schema of the priority tuple.

6.  Distributed QoS Management

   The mechanisms used to achieve QoS management is divided into three
   classes, depending on the level of interdependency exhibited between
   mechanisms on the same device or between devices.

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6.1.  Locally Isolated Decisions

   This class includes decisions that have no interaction with other
   mechanisms on the local or other devices.

   Prioritized Forwarding:
       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:



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

   Caching Decisions:
       The decisions to cache content obey the priority order "reliable"
       to "regular".  In the presence of probabilistic caching
       strategies, the weights are set accordingly.

   PIT Management:
       For saturated PITs, the management operates in favor of rapid
       packet forwarding, so "prompt" Interests replace "regular"
       Interests.  Newly arriving Interests that meet a PIT with
       saturated entries of equal or higher priority are dropped.

6.2.  Local Resource Correlations

   These are decisions that entail interaction between mechanisms on the
   same device (intra-device correlations).  This includes the
   correlation between the caching decision and cache replacement

   o  If arriving Data meets a valid PIT entry, Data is forwarded
      according to priorities.  "Reliable" Data is cached with priority.
      In the case of exhausted prioritized forwarding queues, "prompt"
      traffic is cached with the highest priority, because Interest
      retransmissions are likely to occur.

   o  If arriving Data meets no valid PIT entry, caching follows the
      order "prompt" (highest) to "regular" (lowest).  For probabilistic
      caching, weights are adjusted correspondingly.

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6.3.  Distributed Resource Coordination

   These decisions affect resources across multiple or all devices in
   the network (inter-device correlations).  These include maintaining
   PIT coherence by ensuring that all nodes apply uniform QoS mechanisms
   when replacing content of different service classes, as well as
   achieving CS diversity by introducing probabilistic caching based on
   priority classes.  In this document, distributed coordination is
   achieved as follows:

   PIT Coherence:
       Coherence is increased by applying the same PIT eviction strategy
       at all nodes.  In this case, evict "regular" before "reliable"
       before "prompt".

   Cache Efficiency:
       Efficiency increases with probabilistic caching using the
       coordination of equal cache weights.  The use of probabilistic
       caching reduces the risk of starvation for low priority content,
       even if high priority flows dominate the network.

7.  Implementation Report and Guidance

   The proposed resource management methods have been implemented as an
   extension of the NDN/CCNx software stack [CCN-LITE] in its IoT
   version on RIOT [RIOT].

   Constrained memory and cpu resources limit the use of an elaborate
   prioritized buffer queue management.  With these constraints, IoT
   nodes usually employ forwarding queues that can only hold one to two
   packets at once.  Despite these challenges, the proposed methods show
   visible improvements on forwarding delays.

   Experimental evaluations will be added in this section that show the
   implications of unmanaged PIT and CS resources for traffic forwarding
   in a resource-constrained environment.

8.  Security Considerations


9.  IANA Considerations


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10.  References

10.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

10.2.  Informative References

              "CCN-lite: A lightweight CCNx and NDN implementation",

              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.

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

              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/>.

   [RFC7228]  Bormann, C., Ersue, M., and A. Keranen, "Terminology for
              Constrained-Node Networks", RFC 7228,
              DOI 10.17487/RFC7228, May 2014,

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   [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,

   [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,

   [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,

   [RIOT]     Baccelli, E., Guenes, M., Hahm, O., Schmidt, TC., and M.
              Waehlisch, "RIOT OS: Towards an OS for the Internet of
              Things", Proc. of the 32nd IEEE INFOCOM IEEE Press, pp.
              79-80, April 2013, <http://riot-os.org/>.

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   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

Authors' Addresses

   Cenk Gundogan
   HAW Hamburg
   Berliner Tor 7
   Hamburg  D-20099

   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

   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

   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

   EMail: michael.frey@safetyio.com

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

   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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