Allocation of energy-efficient task in cloud using DVFS. (2019)

• Record Type: Journal Article
• Authors: Mishra, Sambit Kumar; Khan, Md Akram; Sahoo, Sampa; Sahoo, Bibhudatta
• Is Part Of: International journal of computational science and engineering. Volume 18:Number 2(2019)
• Page Start: 154
• Page End: 163
• Publication Date: 2019
• Subjects: cloud computing -- big data -- dynamic voltage frequency scaling -- DVFS -- task allocation -- energy consumption -- virtual machine -- VM -- virtualisation; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Nowadays, the expanding computational capabilities of the cloud system rely on the minimisation of the consumed power to make them sustainable and economically productive. Power management of cloud data centres received a great attention from industry and academia as it consumes high energy and thus increases the operational cost. One of the core approaches for the conservation of energy in the cloud data centre is the task scheduling. This task allocation in a heterogeneous environment is a well known NP-hard problem due to which researchers pay attention for proposing various heuristic techniques for the problem. In this paper, a technique is proposed based on dynamic voltage frequency scaling (DVFS) for optimising the energy consumption in the cloud environment. The basic idea is to address the trade-off between energy consumption and makespan of the system. Here, we formally introduce a model that includes various subsystems and assess the implementation of the algorithm in the heterogeneous environment.