Improving performance of a dynamic load balancing system by using number of effective tasks

Efficient resource usage is a key to achieving better performance in cluster systems. Previously, most research in this area has focused on balancing the load if each node to use the resources of an entire system more effectively. However, we can achieve further improvement in performance when the load balancing system considers the resource requirement according to the task being assigned. This kind of load balancing system, known as an initial job placement system, requires knowledge of the resource usage of a task in order to fit the job to the most suitable node. Since the initial placement requires that the tasks be scheduled before execution, all resource usage must be provided in terms of the prediction. This approach can severely affect the execution time when it uses an inaccurate prediction. We propose a novel load metric termed number of effective tasks in order to resolve the problem arising from inaccurate predictions. Thus, the initial job placement system can work without knowing job resource usage in priori. Simulation results show that the system incurs 11% shorter execution time that the conventional approach using historical behavior-based estimates.