paper

Scalable load balancing in networked systems: A survey of recent advances

arXiv:1806.05444 · doi:10.1137/20M1323746

Abstract

The basic load balancing scenario involves a single dispatcher where tasks arrive that must immediately be forwarded to one of single-server queues. We discuss recent advances on scalable load balancing schemes which provide favorable delay performance when grows large, and yet only require minimal implementation overhead. Join-the-Shortest-Queue (JSQ) yields vanishing delays as grows large, as in a centralized queueing arrangement, but involves a prohibitive communication burden. In contrast, power-of- or JSQ() schemes that assign an incoming task to a server with the shortest queue among servers selected uniformly at random require little communication, but lead to constant delays. In order to examine this fundamental trade-off between delay performance and implementation overhead, we consider JSQ() schemes where the diversity parameter depends on and investigate what growth rate of is required to asymptotically match the optimal JSQ performance on fluid and diffusion scale. Stochastic coupling techniques and stochastic-process limits play an instrumental role in establishing the asymptotic optimality. We demonstrate how this methodology carries over to infinite-server settings, finite buffers, multiple dispatchers, servers arranged on graph topologies, and token-based load balancing including the popular Join-the-Idle-Queue (JIQ) scheme. In this way we provide a broad overview of the many recent advances in the field. This survey extends the short review presented at ICM 2018 (arXiv:1712.08555).

To appear in SIAM Review. arXiv admin note: substantial text overlap with arXiv:1712.08555

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