Concurrent imitation dynamics in congestion games
Proceedings of the 28th ACM symposium on Principles of distributed computing
Distributed algorithms for QoS load balancing
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Tight bounds for parallel randomized load balancing: extended abstract
Proceedings of the forty-third annual ACM symposium on Theory of computing
Distributed selfish load balancing on networks
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Distributed selfish load balancing with weights and speeds
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
Learning equilibria of games via payoff queries
Proceedings of the fourteenth ACM conference on Electronic commerce
Brief announcement: threshold load balancing in networks
Proceedings of the 2013 ACM symposium on Principles of distributed computing
Virtual Machine Coscheduling: A Game Theoretic Approach
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Suppose that a set of $m$ tasks are to be shared as equally as possible among a set of $n$ resources. A game-theoretic mechanism to find a suitable allocation is to associate each task with a “selfish agent” and require each agent to select a resource, with the cost of a resource being the number of agents that select it. Agents would then be expected to migrate from overloaded to underloaded resources, until the allocation becomes balanced. Recent work has studied the question of how this can take place within a distributed setting in which agents migrate selfishly without any centralized control. In this paper we discuss a natural protocol for the agents which combines the following desirable features: It can be implemented in a strongly distributed setting, uses no central control, and has good convergence properties. For $m \gg n$, the system becomes approximately balanced (an $\epsilon$-Nash equilibrium) in expected time $O(\log \log m)$. We show using a martingale technique that the process converges to a perfectly balanced allocation in expected time $O(\log \log m + n^4)$. We also give a lower bound of $\Omega(\max\{\log \log m, n\})$ for the convergence time.