Network Adiabatic Theorem: An Efficient Randomized Protocol for Contention Resolution
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Shah_Network adiabatic.pdf
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Author(s)
Rajagopalan, Shreevatsa
Shah, Devavrat
Shin, Jinwoo
Date Issued
June 2009
Journal
International Conference on Measurement and Modeling of Computer Systems (ACM SIGMETRICS). Proceedings
Publisher
Association for Computing Machinery
Citation
Rajagopalan, Shreevatsa, Devavrat Shah, and Jinwoo Shin. “Network adiabatic Theorem.” Proceedings Of the Eleventh International Joint Conference On Measurement and Modeling Of Computer Systems - SIGMETRICS ’09. Seattle, WA, USA, 2009. 133. Web. 28 Mar 2011. Copyright 2009 ACM
Version
Author's final manuscript
Abstract
The popularity of Aloha(-like) algorithms for resolution of
contention between multiple entities accessing common re-
sources is due to their extreme simplicity and distributed
nature. Example applications of such algorithms include
Ethernet and recently emerging wireless multi-access net-
works. Despite a long and exciting history of more than
four decades, the question of designing an algorithm that is
essentially as simple and distributed as Aloha while being
efficient has remained unresolved.
In this paper, we resolve this question successfully for
a network of queues where contention is modeled through
independent-set constraints over the network graph. The
work by Tassiulas and Ephremides (1992) suggests that an
algorithm that schedules queues so that the summation of
\weight" of scheduled queues is maximized, subject to con-
straints, is efficient. However, implementing such an algo-
rithm using Aloha-like mechanism has remained a mystery.
We design such an algorithm building upon a Metropolis-
Hastings sampling mechanism along with selection of\weight"
as an appropriate function of the queue-size. The key ingre-
dient in establishing the efficiency of the algorithm is a novel
adiabatic-like theorem for the underlying queueing network,
which may be of general interest in the context of dynamical
systems.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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DOI of Published Version
https://doi.org/10.1145/1555349.1555365
