Networking Working Group P. Levis
Internet-Draft Stanford University
Intended status: Standards Track T. Clausen
Expires: July 9, 2011 LIX, Ecole Polytechnique
J. Hui
Arch Rock Corporation
O. Gnawali
Stanford University
J. Ko
Johns Hopkins University
January 5, 2011
The Trickle Algorithm
draft-ietf-roll-trickle-07
Abstract
The Trickle algorithm allows nodes in a lossy shared medium (e.g.,
low power and lossy networks) 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 mechanism and transmission point
selection allows Trickle's communication rate to scale
logarithmically with density. This document describes the Trickle
algorithm and considerations in its use.
Status of this Memo
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Copyright Notice
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Trickle Algorithm Overview . . . . . . . . . . . . . . . . . . 4
4. Trickle Algorithm . . . . . . . . . . . . . . . . . . . . . . 5
4.1. Parameters and Variables . . . . . . . . . . . . . . . . . 5
4.2. Algorithm Description . . . . . . . . . . . . . . . . . . 6
5. Using Trickle . . . . . . . . . . . . . . . . . . . . . . . . 6
6. Operational Considerations . . . . . . . . . . . . . . . . . . 7
6.1. Mismatched Redundancy Constants . . . . . . . . . . . . . 7
6.2. Mismatched Imin . . . . . . . . . . . . . . . . . . . . . 7
6.3. Mismatched Imax . . . . . . . . . . . . . . . . . . . . . 8
6.4. Mismatched Definitions . . . . . . . . . . . . . . . . . . 8
6.5. Specifying the Constant k . . . . . . . . . . . . . . . . 8
6.6. Relationship Between k and Imin . . . . . . . . . . . . . 9
6.7. Tweaks and Improvements to Trickle . . . . . . . . . . . . 9
6.8. Uses of Trickle . . . . . . . . . . . . . . . . . . . . . 9
7. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 10
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10
9. Security Considerations . . . . . . . . . . . . . . . . . . . 10
10. References . . . . . . . . . . . . . . . . . . . . . . . . . . 11
10.1. Normative References . . . . . . . . . . . . . . . . . . . 11
10.2. Informative References . . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 12
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1. Introduction
The Trickle algorithm establishes a density-aware local communication
primitive with an underlying consistency model that guides when a
node transmits. When a node's data does not agree with its
neighbors, that node communicates quickly to resolve the
inconsistency (e.g., in milliseconds). When nodes agree, they slow
their communication rate exponentially, such that nodes send packets
very infrequently (e.g., 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 communication
(e.g., broadcast or local multicast), Trickle handles network re-
population, is robust to network transience, loss, and disconnection,
is simple to implement, and requires very little state. Current
implementations use 4-11 bytes of RAM and are 50-200 lines of C
code[Levis08].
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, including control traffic timing,
multicast propagation, and route discovery. This flexibility stems
from being able to define, on a case-by-case basis, what constitutes
"agreement" or an "inconsistency;" Section 6.8 presents a few
examples of how the algorithm can be used.
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].
Additionally, this document introduces the following terminology:
Trickle communication rate: the sum of the number of messages sent
or received by the Trickle algorithm in an interval.
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Trickle transmission rate: the sum of the number of messages sent by
the Trickle algorithm in an interval.
3. Trickle Algorithm Overview
Trickle's basic primitive is simple: every so often, a node transmits
data unless it hears a few other transmissions whose data suggest its
own transmission is redundant. Examples of such data include routing
state, software update versions, and the last heard multicast packet.
This primitive 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 very low maintenance overhead: one
example use, routing beacons in the CTP protocol [Gnawali09],
requires sending on the order of a few packets per hour yet can
respond in milliseconds.
Trickle sends all messages to a local communication address. The
exact address used can depend on both the underlying IP protocol as
well as how the higher layer protocol uses Trickle. In IPv6, for
example, it can be the link-local multicast address or another local
multicast address, while in IPv4 it can be the broadcast address
(255.255.255.255).
There are two possible results to a Trickle message: either every
node that hears the message finds the message data is consistent with
its own state, or a recipient detects an inconsistency. Detection
can be the result of either an out-of-date node hearing something
new, or an updated node hearing something old. As long as every node
communicates somehow - either receives or transmits - some node will
detect the need for an update.
For example, consider a simple case where "up to date" is defined by
version numbers (e.g., network configuration). If node A transmits
that it has version V, but B has version V+1, then B knows that A
needs an update. Similarly, if B transmits that it has version V+1,
A knows that it needs an update. If B broadcasts or multicasts
updates, then all of its neighbors can receive them without having to
advertise their need. Some of these recipients might not have even
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.
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The fact that Trickle communication can be either transmission or
reception enables the Trickle algorithm to operate in sparse as well
as dense networks. A single, disconnected node must transmit at the
Trickle communication rate. In a lossless, single-hop network of
size n, the Trickle communication rate at each node equals the sum of
the Trickle transmission rates across all nodes. The Trickle
algorithm balances the load in such a scenario, as each node's
Trickle transmission rate is 1/nth of the Trickle communication rate.
Sparser networks require more transmissions per node, but the
utilization of a given broadcast domain (e.g., radio channel over
space, shared medium) will not increase. This is an important
property in wireless networks and other shared media, 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, Imin, 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, Imax, is described as a number of
doublings of the minimum interval size (the base-2 log(max/min)).
For example, a protocol might define Imax as 16. If the minimum
interval is 100ms, then the amount of time specified by Imax 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.
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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), that
is, values greater than or equal to I/2 and less than I. The
interval ends at I.
2. Whenever Trickle hears a transmission that is "consistent," it
increments the counter c.
3. At time t, Trickle transmits if and only if the counter c is less
than the redundancy constant k.
4. When the interval I expires, Trickle doubles the interval length.
If this new interval length would be longer than the time
specified by Imax, Trickle sets the interval length I to be the
time specified by Imax.
5. If Trickle hears a transmission that is "inconsistent," the
Trickle timer resets. If I is greater than Imin when a Trickle
timer resets, Trickle sets I to Imin, resets c to 0, and sets t
to random point in the interval, taken from the range [I/2, I),
that is, values greater than or equal to I/2 and less than I. If
I is equal to Imin, resetting a Trickle timer does nothing.
Trickle can also reset the timer in response to external
"events."
The terms consistent, inconsistent and event are in quotes because
their meaning depends on how a protocol uses Trickle.
The only time the Trickle algorithm transmits is at step 3 of the
above algorithm. This means there is an inherent delay between
detecting an inconsistency (shrinking I to Imin) and responding to
that inconsistency (transmitting at time t in the new interval).
This is intentional. Immediately responding to detecting an
inconsistency can cause a broadcast storm, where many nodes respond
at once and in a synchronized fashion. By making responses follow
the Trickle algorithm (with the minimal interval size), a protocol
can benefit from Trickle's suppression mechanism and scale across a
huge range of node densities.
5. Using Trickle
A protocol specification that uses Trickle MUST specify:
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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 approximately 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 What "events," if any, besides inconsistent transmissions that
reset the Trickle timer.
o What information a node transmits in Trickle messages.
6. Operational Considerations
It is RECOMMENDED that a protocol which uses Trickle includes
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 educational 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 Trickle
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 in a low power network.
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
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after the first slow interval, when the nodes generally agree on the
interval size. Hence, mismatched Imin values are usually not a
significant concern. Note that mismatched Imin values and matching
Imax doubling constants will lead to mismatched maximum interval
lengths.
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 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 MUST 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[Levis08].
Nevertheless, there are situations where a protocol may wish to turn
off Trickle suppression. Because k is a natural number
(Section 4.1), k=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 k-1
rather than k can be confusing. Instead, it is RECOMMENDED that
protocols which require turning off suppression reserve k=0 to mean
k=infinity.
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6.6. Relationship Between k and Imin
Finally, a protocol SHOULD set k and Imin such that Imin is at least
two to three times 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 and
low-power, lossy networks, 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.
6.8. Uses of Trickle
The Trickle algorithm has been used in a variety of protocols, both
in operational as well as academic settings. Giving a brief overview
of some of these uses provides useful examples of how and when it can
be used. These examples should not be considered exhaustive.
Reliable flooding/dissemination: A protocol uses Trickle to
periodically advertise the most recent data it has received,
typically through a version number. An inconsistency is when a node
hears a newer version number or receives new data. A consistency is
when a node hears an older or equal version number. When hearing an
older version number, rather than reset its own Trickle timer, it
sends an update. Nodes with old version numbers that receive the
update will then reset their own timers, leading to fast propagation
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of the new data. Examples of this use include multicast[Hui08a],
network configuration[Lin08][Dang09], and installing new application
programs[Hui04][Levis04].
Routing control traffic: A protocol uses Trickle to control when it
sends beacons which contain routing state. An inconsistency is when
the routing topology changes in a way that could lead to loops or
significant stretch: examples include when the routing layer detects
a routing loop or when a node's routing cost changes significantly.
Consistency is when the routing topology is operating well and is
delivering packets successfully. Using the Trickle algorithm in this
way allows a routing protocol to react very quickly to problems (Imin
is small) but send very few beacons when the topology is stable.
Examples of this use include RPL[I-D.ietf-roll-rpl], CTP[Gnawali09],
and some current commericial IPv6 routing layers[Hui08b].
7. Acknowledgements
The authors would like to acknowledge the guidance and input provided
by the ROLL chairs, David Culler and JP Vasseur.
The authors would also like to acknowledge the helpful comments of
Yoav Ben-Yehezkel, Alexandru Petrescu, and Ulrich Herberg, which
greatly improved the document.
8. IANA Considerations
This document has no IANA considerations.
9. Security Considerations
As it is an algorithm, Trickle itself does not have any specific
security considerations. However, two security concerns can arise
when Trickle is used in a protocol. The first is that an adversary
can force nodes to send many more packets than needed by forcing
Trickle timer resets. In low power networks this increase in traffic
can harm system lifetime. The second concern is that an adversary
can prevent nodes from reaching consistency.
Protocols can prevent adversarial Trickle resets by carefully
selecting what can cause a reset and protecting these events and
messages with proper security mechanisms. For example, if a node can
reset nearby Trickle timers by sending a certain packet, this packet
should be authenticated such that an adversary cannot forge one.
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An adversary can possibly prevent nodes from reaching consistency by
suppressing transmissions with "consistent" messages. For example,
imagine node A detects an inconsistency and resets its Trickle timer.
If an adversary can prevent A from sending messages that inform
nearby nodes of the inconsistency in order to repair it, then A may
remain inconsistent indefinitely. Depending on the security model of
the network, authenticated messages, or a transitive notion of
consistency can prevent this problem. E.g., if messages that are
consistent with A and so suppress its transmissions are by definition
inconsistent with what A heard, then an adversary cannot
simultaneously prevent A from notifying neighbors and not notify the
neighbors itself (recall Trickle operates on shared, broadcast
media). Note that this means Trickle should filter unicast messages.
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
[Dang09] Dang, T., Bulusu, N., Feng, W., and S. Park, "DHV: A Code
Consistency Maintenance Protocol for Multi-hop Wireless
Networks", Wireless Sensor Networks: 6th European
Conference Proceedings EWSN 2009 Cork, February 2009,
<http://books.google.com/books?id=3fb5eePdkBg>.
[Gnawali09]
Gnawali, O., Fonseca, R., Jamieson, K., Moss, D., and P.
Levis, "Collection Tree Protocol", Proceedings of the 7th
ACM Conference on Embedded Networked Systems SenSys 2009,
November 2009,
<http://portal.acm.org/citation.cfm?id=1644038.1644040>.
[Hui04] Hui, J. and D. Culler, "The dynamic behavior of a data
dissemination protocol for network programming at scale",
Proceedings of the 2nd ACM Conference on Embedded
Networked Systems SenSys 2004, November 2004,
<http://portal.acm.org/citation.cfm?id=1031506>.
[Hui08a] Hui, J., "An Extended Internet Architecture for Low-Power
Wireless Networks - Design and Implementation", UC
Berkeley Technical Report EECS-2008-116, September 2008,
<http://portal.acm.org/citation.cfm?id=1460412.1460415>.
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[Hui08b] Hui, J. and D. Culler, "IP is dead, long live IP for
wireless sensor networks", Proceedings of the 6th ACM
Conference on Embedded Networked Systems SenSys 2008,
November 2008,
<http://portal.acm.org/citation.cfm?id=1460412.1460415>.
[I-D.ietf-roll-rpl]
Winter, T., Thubert, P., Brandt, A., Clausen, T., Hui, J.,
Kelsey, R., Levis, P., Pister, K., Struik, R., and J.
Vasseur, "RPL: IPv6 Routing Protocol for Low power and
Lossy Networks", draft-ietf-roll-rpl-17 (work in
progress), December 2010.
[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>.
[Lin08] Lin, K. and P. Levis, "Data Discovery and Dissemination
with DIP", Proceedings of the 7th international conference
on Information processing in sensor networks IPSN 2008,
April 2008,
<http://portal.acm.org/citation.cfm?id=1371607.1372753>.
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
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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
3400 N. Charles St., 224 New Engineering Building
Baltimore, MD 21218
USA
Phone: +1 410 516 4312
Email: jgko@cs.jhu.edu
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