Local Divergence of Markov Chains and the Analysis of Iterative Load-Balancing Schemes
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Fast convergence of selfish rerouting
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Distributed Selfish Load Balancing
SIAM Journal on Computing
Discrete load balancing is (almost) as easy as continuous load balancing
Proceedings of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing
Distributed selfish load balancing on networks
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
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We study probabilistic protocols for concurrent threshold-based load balancing in networks. There are n resources or machines represented by nodes in an undirected graph and m n users that try to find an acceptable resource by moving along the edges of the graph. Users accept a resource if the load is below a threshold. Such thresholds have an intuitive meaning, e.g., as deadlines in a machine scheduling scenario, and they allow the design of protocols under strong locality constraints. When migration is partly controlled by resources and partly by users, our protocols obtain rapid convergence to a balanced state, in which all users are satisfied. We show that convergence is achieved in a number of rounds that is only logarithmic in m and polynomial in structural properties of the graph. Even when migration is fully controlled by users, we obtain similar results for convergence to approximately balanced states.