Green Energy-aware task scheduling using the DVFS technique in Cloud Computing

Abstract

Nowdays, energy consumption as a critical issue in distributed computing systems with high performance has become so green computing tries to energy consumption, carbon footprint and CO2 emissions in high performance computing systems (HPCs) such as clusters, Grid and Cloud that a large number of parallel. Reducing energy consumption for high end computing can bring various benefits such as reducing costs, increasing system reliability & availability, and environmental respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cloud data center with the Dynamic Voltage Frequency Scaling (DVFS) technique and task duplication. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS-enabled processors, and energy consumption. In this paper, we develop a new scheduling algorithm called Energy Aware Scheduling Algorithm based on DVFS technique and task duplication strategy, called EADUPDVFS. Models and scheduling heuristics are examined with a simulation study. Using simulations we show our algorithm not only maintains good performance, but also has a good improvement on energy efficiency for parallel applications.