Load balancing via random local search in closed and open systems

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 servers 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., Jointhe-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. We first analyze the natural Random Local Search (RLS) strategy. Under this strategy, after sampling a new server randomly, clients only switch to it if their service rate is improved. In closed systems, where the client population is fixed, we derive tight es...

• Type: Conference
• Year: 2010
• Where: SIGMETRICS
• Authors: Ayalvadi Ganesh, Sarah Lilienthal, D. Manjunath, Alexandre Proutiere, Florian Simatos
• Keywords: Hardware, Load Resampling, Parallel Server Systems, RLS Strategy, SIGMETRICS 2010