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Approximating Wardrop equilibria with finitely many agents

Approximating Wardrop equilibria with finitely many agents We present efficient algorithms for computing approximate Wardrop equilibria in a distributed and concurrent fashion. Our algorithms are exexuted by a finite number of agents each of which controls the flow of one commodity striving to balance the induced latency over all utilised paths. The set of allowed paths is represented by a DAG. Our algorithms are based on previous work on policies for infinite populations of agents. These policies achieve a convergence time which is independent of the underlying network and depends mildly on the latency functions. These policies can neither be applied to a finite set of agents nor can they be simulated directly due to the exponential number of paths. Our algorithms circumvent these problems by computing a randomised path decomposition in every communication round. Based on this decomposition, flow is shifted from overloaded to underloaded paths. This way, our algorithm can handle exponentially large path collections in polynomial time. Our algorithms are stateless, and the number of communication rounds depends polynomially on the approximation quality and is independent of the topology and size of the network. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Distributed Computing Springer Journals

Approximating Wardrop equilibria with finitely many agents

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References (23)

Publisher
Springer Journals
Copyright
Copyright © 2008 by Springer-Verlag
Subject
Computer Science; Theory of Computation ; Software Engineering/Programming and Operating Systems ; Computer Systems Organization and Communication Networks; Computer Hardware ; Computer Communication Networks
ISSN
0178-2770
eISSN
1432-0452
DOI
10.1007/s00446-008-0057-1
Publisher site
See Article on Publisher Site

Abstract

We present efficient algorithms for computing approximate Wardrop equilibria in a distributed and concurrent fashion. Our algorithms are exexuted by a finite number of agents each of which controls the flow of one commodity striving to balance the induced latency over all utilised paths. The set of allowed paths is represented by a DAG. Our algorithms are based on previous work on policies for infinite populations of agents. These policies achieve a convergence time which is independent of the underlying network and depends mildly on the latency functions. These policies can neither be applied to a finite set of agents nor can they be simulated directly due to the exponential number of paths. Our algorithms circumvent these problems by computing a randomised path decomposition in every communication round. Based on this decomposition, flow is shifted from overloaded to underloaded paths. This way, our algorithm can handle exponentially large path collections in polynomial time. Our algorithms are stateless, and the number of communication rounds depends polynomially on the approximation quality and is independent of the topology and size of the network.

Journal

Distributed ComputingSpringer Journals

Published: Mar 13, 2008

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