A comparison of priority-based decentralized load balancing policies

Load balancing policies in distributed systems divide jobs into two classes; those processed at their of origination (local jobs) and those processed at some other site in the system after being transfered through a communication network (remote jobs). This paper considers a class of decentralized load balancing policies that use a threshold on the local job queue length at each host in making decisions for remote processing. They differ from each other according to how they assign priorities to each of these job classes, ranging from one providing favorable treatment to local jobs to one providing favorable treatment to remote jobs. Under each policy, the optimal load balancing problem is formulated as an optimization problem with respect to the threshold parameter. The optimal threshold is obtained numerically using matrix-geometric formulation and an iteration method. Last, we consider the effects that the job arrival process can have on performance. One expects that load balancing for systems operating in an environment of bursty job arrivals should be more beneficial than for an environment with random job arrivals. This fact is observed through numerical examples.