Distributed Selfish Load Balancing

TL;DR

This paper presents a distributed, game-theoretic protocol for load balancing among selfish agents, achieving near-perfect equilibrium efficiently with convergence times depending logarithmically on the number of tasks.

Contribution

It introduces a natural, decentralized protocol with proven convergence properties and bounds for load balancing in large-scale systems with selfish agents.

Findings

Expected convergence to approximate balance in O(log log m) time.

Expected convergence to perfect balance in O(log log m + n^4) time.

Lower bound of Ω(max{log log m, n}) for convergence time.

Abstract

Suppose that a set of $m$ tasks are to be shared as equally as possible amongst 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 to 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.