The Experiential Heterogeneous Earliest Finish Time Algorithm for Task Scheduling in Clouds

Task scheduling in cloud environments is the problem of assigning and executing computational tasks on the available cloud resources. Effective task scheduling approaches reduce the task completion time, increase the efficiency of resource utilization, and improve the quality of service and the overall performance of the system. In this paper, we present a novel task scheduling algorithm for cloud environments based on the Heterogeneous Earliest Finish Time (HEFT) algorithm, called experiential HEFT. It considers experiences with previous executions of tasks to determine the workload of resources. To realize the experiential HEFT algorithm, we propose a novel way of HEFT rank calculation to specify the minimum average execution time of previous runs of a task on all relevant resources. Experimental results indicate that the proposed experiential HEFT algorithm performs better than HEFT and the popular Critical-Path-on-a-Processor (CPOP) algorithm considered in our comparison.