EATSVM: Energy-Aware Task Scheduling on Cloud Virtual Machines

Abstract

The pervasive adoption of cloud computing services and applications at a rapid rate makes the underlying data centers exacerbate the problems like carbon footprint and the operational cost, caused by the energy consumption. Various hardware-centric and software-centric approaches are proposed in the literature to reduce the energy consumption of the cloud data centers. Task scheduling algorithms are software-centric approaches to reduce the energy consumption in cloud computing systems. The majority of these algorithms focus on server consolidation leading to idle servers that reduce energy efficiency optimization. In this paper, we propose an Energy-Aware Task Scheduling algorithm on cloud Virtual Machines (EATSVM) that assigns a task to the VM where the increase in energy consumption is the least, considering both active and idle VMs. The algorithm also takes into consideration the increase in the energy consumption of the already running tasks on the VM due to increase in their execution time, while assigning a new task to that VM. We analyze the performance of our algorithm in a heterogeneous cloud environment with increasing number of tasks and compare the energy-savings of our algorithm with that of Energy Conscious Task Consolidation (ECTC) algorithm. Our experimental results demonstrate that EATSVM achieves energy-saving in a heterogeneous cloud-computing environment.