A Statistical Mechanical Load Balancer for the Web

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dc.creatorBridgewater, Jesse S. A.
dc.creatorBoykin, P. Oscar
dc.creatorRoychowdhury, Vwani P.
dc.date2004-10-06
dc.date2005-01-06
dc.date.accessioned2026-07-07T03:01:18Z
dc.date.available2026-07-07T03:01:18Z
dc.descriptionThe maximum entropy principle from statistical mechanics states that a closed system attains an equilibrium distribution that maximizes its entropy. We first show that for graphs with fixed number of edges one can define a stochastic edge dynamic that can serve as an effective thermalization scheme, and hence, the underlying graphs are expected to attain their maximum-entropy states, which turn out to be Erdos-Renyi (ER) random graphs. We next show that (i) a rate-equation based analysis of node degree distribution does indeed confirm the maximum-entropy principle, and (ii) the edge dynamic can be effectively implemented using short random walks on the underlying graphs, leading to a local algorithm for the generation of ER random graphs. The resulting statistical mechanical system can be adapted to provide a distributed and local (i.e., without any centralized monitoring) mechanism for load balancing, which can have a significant impact in increasing the efficiency and utilization of both the Internet (e.g., efficient web mirroring), and large-scale computing infrastructure (e.g., cluster and grid computing).
dc.description11 Pages, 5 Postscript figures; added references, expanded on protocol discussion
dc.identifierhttps://arxiv.org/abs/cond-mat/0410136
dc.identifierhttp://arxiv.org/abs/cond-mat/0410136
dc.identifierPhysical Review E 71, 046133, 2005
dc.identifierdoi:10.1103/PhysRevE.71.046133
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/25012
dc.subjectDisordered Systems and Neural Networks
dc.titleA Statistical Mechanical Load Balancer for the Web
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