Internet DRAFT - draft-gundogan-icnrg-iotqos
draft-gundogan-icnrg-iotqos
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
VUW
July 8, 2019
Quality of Service for ICN in the IoT
draft-gundogan-icnrg-iotqos-01
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. 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
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This document is subject to BCP 78 and the IETF Trust's Legal
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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
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
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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",
"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.
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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:
priority tuple = < LATENCY_PRIORITY, RELIABILITY_PRIORITY >
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:
* PROMPT_FORWARDING_QUEUE
* REGULAR_FORWARDING_QUEUE
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
strategies.
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
TODO
9. IANA Considerations
TODO
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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,
<https://www.rfc-editor.org/info/rfc2119>.
10.2. Informative References
[CCN-LITE]
"CCN-lite: A lightweight CCNx and NDN implementation",
<http://ccn-lite.net/>.
[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.
[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/>.
[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>.
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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,
<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>.
[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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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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