Adaptive Job Scheduling for a Service Grid Using a Genetic Algorithm

Abstract

This paper presents a new approach to scheduling jobs on a service Grid using a genetic algorithm (GA). A fitness function is defined to minimize the average execution time of scheduling N jobs to M(≤ N) machines on the Grid. Two models are proposed to predict the execution time of a single job or multiple jobs on each machine with varied system load. The single service type model is used to schedule jobs of one single service to a machine while the multiple service types model schedules jobs of multiple services to a machine. The predicted execution times from these models are used as input to the genetic algorithm to schedule N jobs to M machines on the Grid. Experiments on a small Grid of four machines have shown a significant reduction of the average execution time by the new job scheduling approach.