An energy-efficient big data workflow scheduling algorithm under budget constraints for heterogeneous cloud environment

Infrastructure as a service model of cloud computing provides a tremendous amount of high-performance computing systems for the execution of scientific workflow applications. However, due to explosive growth in energy consumption and high charge cost of using these cloud systems, energy-efficient workflow scheduling under budget constraints becomes the most challenging issue. Very few research works have been done that consider the stated issue. Most of them mainly focus on the minimization of schedule length under user-specified budget constraints or energy consumption constraints. In this article, we propose an energy-efficient workflow scheduling algorithm named reducing energy consumption using fair pre-assignment of available budget (RECFPAB) that reduces energy consumption under client-specified budget constraints. The RECFPAB introduces a flexible mechanism to save energy consumption with the inclusion of energy and cost coefficient factor that enables fair distribution of available budget for unscheduled tasks of the workflow application. In order to compare the performance of the proposed algorithm, an energy-efficient version of the popular existing algorithms such as heterogeneous budget constrained scheduling and minimizing schedule length using budget level are introduced. The experimental evaluation based on Genome, LIGO, and Montage applications shows that RECFPAB gives significant results in comparison with considered algorithms.