Load Balancing via Random Local Search in Closed and Open systems

TL;DR

This paper studies the effectiveness of random load resampling and migration strategies in parallel server systems, especially in scenarios where clients lack load prediction capabilities, such as wireless spectrum sharing.

Contribution

It introduces and analyzes a novel load balancing approach based on random client migration, contrasting with traditional smart server selection policies.

Findings

Random migration strategies can improve load distribution.

Performance depends on migration frequency and system parameters.

Applicable to wireless spectrum sharing scenarios.

Abstract

In this paper, we analyze the performance of random load resampling and migration strategies in parallel server systems. Clients initially attach to an arbitrary server, but may switch server independently at random instants of time in an attempt to improve their service rate. This approach to load balancing contrasts with traditional approaches where clients make smart server selections upon arrival (e.g., Join-the-Shortest-Queue policy and variants thereof). Load resampling is particularly relevant in scenarios where clients cannot predict the load of a server before being actually attached to it. An important example is in wireless spectrum sharing where clients try to share a set of frequency bands in a distributed manner.