LPOD: A Local Path Based Optimized Scheduling Algorithm for Deadline-Constrained Big Data Workflows in the Cloud

Abstract

List based scheduling algorithms have been proven an optimistic strategy with a shorter response time to generate feasible solutions for the workflow scheduling problem. Data-intensive and computation-intensive workflow applications have different characteristics in terms of the ratio between data transfer time and task execution time. Workflow scheduling algorithms in a cloud-based environment should adequately consider the characteristics of the underlying cloud platform such as the on-demand resource provisioning strategy, the practically unlimited compute capacities, the booting times of virtual machines, the homogeneous network and the pay-as-you-go price model to produce an optimal scheduling solution within the deadline constraint of a given workflow. In this paper, a path based scheduling algorithm, named LPOD, is proposed to find the best workflow schedule solution with minimum monetary cost in a cloud computing environment. A series of case studies have been carefully conducted using synthetic workflows based on DATAVIEW, which is a popular open-source big data workflow management system. The experimental results show that the proposed algorithm is efficient and can generate better workflow schedules than the state-of-the-art algorithms such as IC-PCP and SGX-E2C2D.