Data-Intensive Workflow Scheduling in Cloud on Budget and Deadline Constraints

With the development of Cloud Computing, large-scale applications expressed as scientific workflows are often executed in cloud. The problems of workflow scheduling are vital for achieving high efficient and meeting the needs of users in clouds. In order to obtain more cost reduction as well as maintain the quality of service by meeting the deadlines, this paper proposed a novel heuristic, PWHEFT (Path-task Weight Heterogeneous Earliest Finish Time), based on Heterogeneous Earliest Finish Time (HEFT). The criticality of tasks in a workflow and data transmission between resources are considered in PWHEFT while ignored in some other algorithms. The heuristic is evaluated using simulation with five different real world workflow applications. The simulation results show that our proposed scheduling heuristic can significantly improve planning success rate.