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