Concurrency Optimized Task Scheduling for Workflows in Cloud

Recent years, more and more enterprises migrate their applications to the cloud for cost saving. During the application migration, an application usually needs to be re-designed from a workflow to an execution plan on a cloud platform. Based on the execution plan, many scheduling optimization approaches have been proposed to improve the performance of the system. In this paper, we argue that the concurrency optimization of each task in the workflow can significantly improve the system efficiency under delay or resource constraint. To generate an efficient execution plan, we optimize the concurrency of the task based on the critical path of the workflow. Furthermore, we deal with the scheduling problem in two different scenarios respectively. To save the costs under a certain deadline, we propose a delay constrained task scheduling approach. To shorten the completion time under certain resources, we propose a resource constrained task scheduling approach. Finally, by deploying a video analysis application on a cloud platform testbed, we validate the proposed two scheduling approaches.