Autonomic Resource Management for a Cluster that Executes Batch Jobs

Dynamic resource allocation in a server cluster has received much attention in recent years. We have developed heuristic algorithms for autonomic server provisioning for a cluster that executes batch jobs. These jobs have a common, or shared, completion deadline. External factors that may affect the decision to allocate servers are modeled as a time-varying cost function. The provisioning goal is to ensure that all jobs are completed on time while minimizing the total cost of server usage. Two heuristic algorithms which adapt to changing workload are presented. The merit of these algorithms is evaluated by simulation. Our results show that the proposed algorithms can successfully use fewer servers during the high cost period while ensuring that all jobs meet their deadline. They also perform far better than static server allocation