DT-MG: many-to-one matching game for tasks scheduling towards resources optimization in cloud computing

Abstract

The increasing demand of cloud computing motivates researchers to make cloud environments more efficient for its users and more profitable for the providers. More and more datacenters are being built to cater customers' needs. However, datacenters consume large amounts of energy, and this draws negative attention. Therefore, cloud providers are confronted with great pressures to reduce the energy consumed by datacenters. To address this issue, efficient algorithms to reduce energy consumption and to guarantee the quality of service are needed. In this paper, we propose a load balancing algorithm named DT-MG, which aims to reduce energy consumption and maximize the efficiency of the available resources. First, we used the Matching Game Theory model for assigning tasks to datacenters. We then study the optimal operation of the resources by migrating all the tasks of the physical machine under sub-regime to other physical machine, followed by their systematic switch to standby mode. Experimental results prove that the proposed approach reduces energy consumption and the number of task migration while maintaining the service level agreement in comparison with some existing techniques.

Keywords:

• Cloud computing
• tasks scheduling
• energy consumption
• resource allocation
• matching game

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Yassir Samadi

Samadi Yassir received his M.S. in IT Telecom Networks at ENSIAS (National School of Computer Science and System Analysis) at Mohammed V University-Souissi, Rabat, Morocco, in 2015. He is currently a Ph.D. student in the Department of Computer Science, Laboratory CEDOC ST2I, ENSIAS, Rabat, Morocco. His research interests include load balancing, distributed systems, big data management in workflow systems, and cloud computing.

Mostapha Zbakh

Mostapha Zbakh received his Ph.D. in computer sciences from Polytechnic Faculty of Mons, Belgium, in 2001. He is currently a Professor at ENSIAS (National School of Computer Science and System Analysis) at Mohammed V University, Rabat, Morocco, since 2002. His research interests include load balancing, parallel and distributed systems, HPC, Big data and Cloud computing.

Claude Tadonki

Claude Tadonki currently holds a research position at Mines ParisTech/CRI, working on HPC topics and automatic code transformations. His background is a combination of mathematics and computer science. From his Ph.D. and during his different positions afterwards, he has been involved in cutting-edge researches related to high-performance computing and operation research, following the sequence model, method, and implementation. He is still interested in fundamental questions about difficult genuine problems, while striving to understand how the advances in optimization, algorithmic, programming, and supercomputers can be efficiently combined to provide the best answer.