Networking Working Group                                        P. Levis
Internet-Draft                                       Stanford University
Intended status: Informational                                T. Clausen
Expires: October 12, 2010 January 7, 2011                        LIX, Ecole Polytechnique
                                                                  J. Hui
                                                   Arch Rock Corporation
                                                              O. Gnawali
                                                     Stanford University
                                                                   J. Ko
                                                Johns Hopkins University
                                                          April 10,
                                                            July 6, 2010

                         The Trickle Algorithm
                       draft-ietf-roll-trickle-01
                       draft-ietf-roll-trickle-02

Abstract

   The Trickle algorithm allows wireless nodes to exchange information
   in a highly robust, energy efficient, simple, and scalable manner.
   Dynamically adjusting transmission windows allows Trickle to spread
   new information on the scale of link-layer transmission times while
   sending only a few messages per hour when information does not
   change.  A simple suppression nechanism mechanism and transmission point
   selection allows Trickle's communication rate to scale
   logarithmically with density.  This document describes Trickle and
   considerations in its use.

Status of this Memo

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . 3
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . 3
   3.  Trickle Algorithm Overview  . . . . . . . . . . . . . . . . . . 3
   4.  Trickle Algorithm . . . . . . . . . . . . . . . . . . . . . . . 4
     4.1.  Parameters and Variables  . . . . . . . . . . . . . . . . . 4
     4.2.  Algorithm Description . . . . . . . . . . . . . . . . . . . 5
   5.  Using Trickle . . . . . . . . . . . . . . . . . . . . . . . . . 6 5
   6.  Operational Considerations  . . . . . . . . . . . . . . . . . . 6
     6.1.  Mismatched redundancy constants . . . . . . . . . . . . . . 6
     6.2.  Mismatched Imin . . . . . . . . . . . . . . . . . . . . . . 6
     6.3.  Mismatched Imax . . . . . . . . . . . . . . . . . . . . . . 7
     6.4.  Mismatched definitions  . . . . . . . . . . . . . . . . . . 7
     6.5.  Specifying the constant k . . . . . . . . . . . . . . . . . 7
     6.6.  Relationship between k and Imin . . . . . . . . . . . . . . 7
     6.7.  Tweaks and improvements to Trickle  . . . . . . . . . . . . 8
   7.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . 7 8
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . . . 7 8
   9.  Security Considerations . . . . . . . . . . . . . . . . . . . . 7 8
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . . . 7 8
     10.1. Normative References  . . . . . . . . . . . . . . . . . . . 7 8
     10.2. Informative References  . . . . . . . . . . . . . . . . . . 8 9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . . . 8 9

1.  Introduction

   The Trickle algorithm is designed for wireless networks.  It
   establishes a density-aware local broadcast with an underlying
   consistency model that guides when a node communicates.  When a
   node's data does not agree with its neighbors, it communicates
   quickly to resolve the inconsistency.  When nodes agree, they slow
   their communicationrate communication rate exponentially, such that in a stable state nodes send at most
   a few packets per hour.  Instead of flooding a network with packets,
   the algorithm controls the send rate so each node hears a small
   trickle of packets, just enough to stay consistent.  Furthermore, by
   relying only on local broadcasts, Trickle handles network re-population, re-
   population, is robust to network transience, loss, and disconnection,
   and requires very little state (implementations use 4-11 bytes).

   While Trickle was originally designed for reprogramming protocols
   (where the data is the code of the program being updated), experience
   has shown it to be a powerful mechanism that can be applied to wide
   range of protocol design problems.  For example, routing protocols
   such as RPL use Trickle to ensure that nodes in a given neighborhood
   have consistent, loop-free routes.  When the topology is consistent,
   nodes occasionally gossip to check that they still agree, problems, including control traffic timing,
   multicast propagation, and when
   the topology changes they gossip more frequently, until they reach
   consistency again. route discovery.

   This document describes the Trickle algorithm and provides guidelines
   for its use.  It also states requirements for protocol specifications
   that use Trickle.  This document does not provide results on
   Trickle's performance or behavior, nor does it explain the
   algorithm's design in detail: interested readers should refer to
   [Levis04] and [Levis08].

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in RFC
   2119 [RFC2119].

3.  Trickle Algorithm Overview

   Trickle's basic primitive is simple: every so often, a mote node transmits
   code metadata if it has not heard a few other motes nodes transmit the same
   thing.  This allows Trickle to scale to thousand-fold variations in
   network density, quickly propagate updates, distribute transmission
   load evenly, be robust to transient disconnections, handle network
   repopulations, and impose a maintenance overhead on the order of a
   few packets per hour.

   Trickle sends all messages to the local broadcast address.  There are
   two possible results to a Trickle broadcast: either every mote node that
   hears the message is up to date, or a recipient detects the need for
   an update.  Detection can be the result of either an out-of-date mote node
   hearing someone has new code, or an updated mote node hearing someone has
   old code.  As long as every mote node communicates somehow - either
   receives or transmits - the need for an update will be detected.

   For example, consider a simple case where "up to date" is defined by
   version numbers (e.g., network configuration).  If node A broadcasts
   that it has version V, but B has version V+1, then B knows that A
   needs an update.  Similarly, if B broadcasts that it has V+1, A knows
   that it needs an update.  If B broadcasts updates, then all of its
   neighbors can receive them without having to advertise their need.
   Some of these recipients might not even have heard A's transmission.

   In this example, it does not matter who first transmits, A or B;
   either case will detect the inconsistency.  All that matters is that
   some nodes communicate with one another at some nonzero rate.  As
   long as the network is connected and there is some minimum
   communication rate for each node, the network will reach eventual
   consistency.

   The fact that communication can be either transmission or reception
   enables Trickle to operate in sparse as well as dense networks.  A
   single, disconnected node must transmit at the communication rate.
   In a lossless, single-hop network of size n, the sum of transmissions
   over the network is the communication rate, so for each node it is
   1/n.  Sparser networks require more transmissions per mote, node, but
   utilization of the radio channel over space will not increase.  This
   is an important property in wireless networks, where the channel is a
   valuable shared resource.  Additionally, reducing transmissions in
   dense networks conserves system energy.

4.  Trickle Algorithm

   This section describes the Trickle algorithm.

4.1.  Parameters and Variables

   A Trickle timer has three configuration parameters: the minimum
   interval size Imin, the maximum interval size Imax, and a redundancy
   constant k:

   o  The minimum interval size is defined in units of time (e.g.,
      milliseconds, seconds).  For example, a protocol might define the
      minimum interval as 100 milliseconds.

   o  The maximum interval size is described as a number of doublings of
      the minimum interval size (the base-2 log(max/min)).  For example,
      a protocol might define the maximum interval as 16.  If the
      minimum interval is 100ms, then the maximum interval is 100ms *
      65536, 6,553.6 seconds, or approximately 109 minutes.

   o  The redundancy constant is a natural number (an integer greater
      than zero).

   In addition to these three parameters, Trickle maintains three
   variables:

   o  I, the current interval size

   o  t, a time within the current interval, and

   o  c, a counter.

4.2.  Algorithm Description

   The Trickle algorithm has five rules:

   1.  When an interval begins, Trickle resets c to 0 and sets t to a
       random point in the interval, taken from the range [I/2, I).

   2.  Whenever Trickle hears a transmission that is "consistent," it
       increments counter c.

   3.  At time t, Trickle transmits if and only if counter c is less
       than the redundancy constant k.

   4.  When an interval expires, Trickle doubles the interval length.
       If this new interval length would be longer than Imax, Trickle
       sets the interval length I to be Imax.

   5.  If Trickle hears a transmission that is "inconsistent," the
       Trickle timer resets.  If I is greater than Imin, resetting a
       Trickle timer sets I to Imin and begins a new interval.  If I is
       equal to Imin, resetting a Trickle timer does nothing.  Trickle
       may also reset the timer in response to external "events."

   The terms consistent, inconsistent and event are in quotes because
   their meaning depends on the use of Trickle.

5.  Using Trickle

   A protocol specification that uses Trickle MUST specify:

   o  Default values for Imin, Imax, and k.  Because link layers can
      vary widely in their properties, the default value of Imin should
      be specified in terms of the worst-case latency of a link layer
      transmission.  For example, a specification should say "the
      default value of Imin is 4 times the worst case link layer
      latency" and should not say "the default value of Imin is 500
      milliseconds."  Worst case latency is the time until the first
      link-layer transmission of the frame assuming an idle channel
      (does not include backoff, virtual carrier sense, etc.).

   o  What constitutes a "consistent" transmission.

   o  What constitutes an "inconsistent" transmission.

   o  Any "events"  What "events," if any, besides inconsistent transmissions that
      reset the Trickle timer.

6.  Operational Considerations

   It is RECOMMENDED that a protocol which uses Trickle include
   mechanisms to inform nodes of configuration parameters at runtime.
   However, it is not always possible to do so.  In the cases where
   different nodes have different configuration parameters, Trickle may
   have unintended behaviors.  This section outlines some of those
   behaviors and operational considerations as an educational exercise. exercises.

6.1.  Mismatched redundancy constants

   If nodes do not agree on the redundancy constant k, then nodes with
   higher values of k will transmit more often than nodes with lower
   values of k.  In some cases, this increased load can be independent
   of the density.  For example, consider a network where all nodes but
   one have k=1, and this one node has k=2.  The different node can end
   up transmitting on every interval: it is maintaining a communication
   rate of 2 with only itself.  Hence, the danger of mismatched k values
   is uneven transmission load that can deplete the energy of some
   nodes.

6.2.  Mismatched Imin

   If nodes do not agree on Imin, then some nodes, on hearing
   inconsistent messages, will transmit sooner than others.  These
   faster nodes will have their intervals grow to similar size as the
   slower nodes within a single slow interval time, but in that period
   may suppress the slower nodes.  However, such suppression will end
   after the first slow interval, when the nodes generally agree on the
   interval size.  Hence, mismatched Imin values are usually not a
   significant concern.

6.3.  Mismatched Imax

   If nodes do not agree on Imax, then this can cause long-term problems
   with transmission load.  Nodes with small Imax values will transmit
   faster, suppressing those with larger Imax values.  The nodes will with
   larger Imax values, always suppressed, will never transmit.  In the
   base case, when the network is consistent, this can cause long-term
   inequities in energy cost.

6.4.  Mismatched definitions

   If nodes do not agree on what constitutes a consistent or
   inconsistent transmission, then Trickle may fail to operate properly.
   For example, if a receiver thinks a transmission is consistent, but
   the transmitter (if in the receivers situation) would have thought it
   inconsistent, then the receiver will not respond properly and inform
   the transmitter.  This can lead the network to not reach a consistent
   state.  For this reason, unlike the configuration constants k, Imin,
   and Imax, consistency definitions should be clearly stated in the
   protocol and should not be configured at runtime.

6.5.  Specifying the constant k

   There are some edge cases where a protocol may wish to use Trickle
   with its suppression disabled (k is set to infinity).  In general,
   this approach is highly dangerous and it is NOT RECOMMENDED.
   Disabling suppression means that every node will always send on every
   interval, and can lead to congestion in dense networks.  This
   approach is especially dangerous if many nodes reset their intervals
   at the same time.  In general, it is much more desirable to set k to
   a high value (e.g., 5 or 10) than infinity.  Typical values for k are
   1-5: these achieve a good balance between redundancy and low cost.

   Nevertheless, there are situations where a protocol may wish to turn
   off Trickle suppression.  Because k is a natural number
   (Section 4.1), c=0 has no useful meaning.  If a protocol allows k to
   be dynamically configured, a value of 0 remains unused.  For ease of
   debugging and packet inspection, having the parameter describe (c-1)
   can be counter-productive.  Instead, it is RECOMMENDED that protocols
   which require turning off suppression reserve c=0 to mean c=infinity.

6.6.  Relationship between k and Imin

   Finally, a protocol SHOULD set k and Imin such that Imin is at least
   two to three as long as it takes to transmit k packets.  Otherwise,
   if more than k nodes reset their intervals to Imin, the resulting
   communication will lead to congestion and significant packet loss.
   Experimental results have shown that packet losses from congestion
   reduce Trickle's efficiency [Levis04].

6.7.  Tweaks and improvements to Trickle

   Trickle is based on a small number of simple, tightly integrated
   mechanisms that are highly robust to challenging network
   environments.  In our experiences using Trickle, attempts to tweak
   its behavior are typically not worth the cost.  As written, the
   algorithm is already highly efficient: further reductions in
   transmissions or response time come at the cost of failures in edge
   cases.  Based on our experiences, we urge protocol designers to
   suppress the instinct to tweak or improve Trickle without a great
   deal of experimental evidence that the change does not violate its
   assumptions and break the algorithm in edge cases.

   This warning in mind, Trickle is far from perfect.  For example,
   Trickle suppression typically leads sparser nodes to transmit more
   than denser ones; it is far from the optimal computation of a minimum
   cover.  However, in dynamic network environments such as wireless,
   the coordination needed to compute the optimal set of transmissions
   is typically much greater than the benefits it provides.  One of the
   benefits of Trickle is that it is so simple to implement and requires
   so little state yet operates so efficiently.  Efforts to improve it
   should be weighed against the cost of increased complexity.

7.  Acknowledgements

8.  IANA Considerations

   This document has no IANA considerations.. considerations.

9.  Security Considerations

   This document has no security considerations.

10.  References

10.1.  Normative References

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

10.2.  Informative References

   [Levis04]  Levis, P., Patel, N., Culler, D., and S. Shenker,
              "Trickle: A Self-Regulating Algorithm for Code Propagation
              and Maintenance in Wireless Sensor Networks"", Proceedings
              of the First USENIX/ACM Symposium on Networked Systems
              Design and Implementation NSDI 2004, March 2004,
              <http://portal.acm.org/citation.cfm?id=1251177>.

   [Levis08]  Levis, P., Brewer, E., Culler, D., Gay, D., Madden, S.,
              Patel, N., Polastre, J., Shenker, S., Szewczyk, R., and A.
              Woo, "The Emergence of a Networking Primitive in Wireless
              Sensor Networks", Communications of the ACM, v.51 n.7,
              July 2008,
              <http://portal.acm.org/citation.cfm?id=1364804>.

Authors' Addresses

   Philip Levis
   Stanford University
   358 Gates Hall, Stanford University
   Stanford, CA  94305
   USA

   Phone: +1 650 725 9064
   Email: pal@cs.stanford.edu

   Thomas Heide Clausen
   LIX, Ecole Polytechnique

   Phone: +33 6 6058 9349
   Email: T.Clausen@computer.org

   Jonathan Hui
   Arch Rock Corporation
   501 Snd St., Suite 410
   San Francisco, CA  94107
   USA

   Email: jhui@archrock.com
   Omprakash Gnawali
   Stanford University
   S255 Clark Center, 318 Campus Drive
   Stanford, CA  94305
   USA

   Phone: +1 650 725 6086
   Email: gnawali@cs.stanford.edu

   JeongGil Ko
   Johns Hopkins University
   3100 Wyman Park Dr., Room 414
   3400 N. Charles St., 224 New Engineering Building
   Baltimore, MD  21211  21218
   USA

   Phone: +1 410 516 4312
   Email: jgko@cs.jhu.edu