Compute-intensive workflow scheduling in multi-cloud environment

Abstract

Workflow scheduling is recognized as well-known NP-complete problem in the perspective of cloud computing environment. Workflow applications always need high compute-intensive operations because of the presence of precedence-constrains. The scheduling objective is to map the workflow application to the VMs pool at available cloud datacenters such that the overall processing time (makespan) is to be minimized and average cloud utilization is maximized. In this paper, we propose a two phase workflow scheduling algorithm with a new priority scheme. It considers the ratio of average communication cost to the average computation cost of the task node as a part of prioritization process in the first phase. Prioritized tasks are mapped to suitable virtual machines in the second phase. Proposed algorithm is capable of scheduling large size workflows in heterogeneous multi-cloud environment. The proposed algorithm is simulated rigorously on standard scientific workflows and simulated results are compared with the existing dependent task scheduling algorithms as per the assumed cloud model. The results remarkably show that the proposed algorithm supercedes the existing algorithms in terms of makespan, speed-up, schedule length ratio and average cloud utilization.