A Statistical Mechanical Load Balancer for the Web
Abstract
The 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).
Keywords
Cite
@article{arxiv.cond-mat/0410136,
title = {A Statistical Mechanical Load Balancer for the Web},
author = {Jesse S. A. Bridgewater and P. Oscar Boykin and Vwani P. Roychowdhury},
journal= {arXiv preprint arXiv:cond-mat/0410136},
year = {2007}
}
Comments
11 Pages, 5 Postscript figures; added references, expanded on protocol discussion