Energy Efficient Partitioning and Scheduling Approach for Scientific Workflows in the Cloud

Abstract

Energy consumption is emerging as a new crucial issue of the Cloud Computing environments such as data centers. The problem of power consumption is more challenging especially in the context of scientific workflows deployment in the Cloud as they trigger intensive computational tasks and data manipulation steps which begets excessive data movement operations over communication networks. For instance, it was revealed that network devices consume up to one-third of the total energy consumption of Cloud data centers. In this paper, we propose an energy-aware approach for scientific workflows scheduling in the Cloud. In the first step, we propose a Workflow Partitioning for Energy Minimization (WPEM) algorithm that allows reducing the network energy consumption of the workflow and the total amount of data communication while achieving a high degree of parallelism. In the second step, we use the heuristic of Cat Swarm Optimization to schedule the generated partitions in order to minimize the workflow's overall energy consumption and execution time. We evaluated the proposed approach using three real cases of data intensive workflows and compare it with other algorithms from literature. The experimental results show that our proposal allows to reduce remarkably the network energy consumption of the tested workflows (up to 96% of network energy consumption saving for memory intensive workflows) and the overall energy consumption of the workflows while ensuring a reasonable execution time and using less Cloud resources.