Improvement of the HEFT Algorithm by Lookahead Technique for Heterogeneous Resource Environments

Abstract

Task scheduling is a major issue in heterogeneous resource environments and particularly in multiprocessor system and Cloud. It means selection of the best suitable resources for task execution. Among the scheduling heuristics proposed in the literature, the Heterogeneous Earliest Finish Time (HEFT) heuristic is known by giving a good schedules in short time. In this paper, we propose three improvement versions of HEFT, where generally if certain conditions were verified, decisions made by the heuristic do not accounting a task being in scheduling only, but also take into account the impact of this decision to the children of the task. Thus, the heuristic can allocate this task and one of its children on the processor that can minimize execution time of the two tasks at the same time. Simulation results indicate that the new versions of HEFT can effectively reduce the schedule length in most cases without making the execution time prohibitively high otherwise.