ENAGS: energy and network-aware genetic scheduling algorithm on cloud data centers

Abstract

Cloud computing plays a significant role in today's network computing by delivering virtualized resources as pay-as-you-go services over the Internet. However, the growing demand drastically increases the energy consumption of data centers, which has become a prominent problem. Hence, energy efficient solutions are required to minimize system power consumption and increase the availability of computational resources and obviously reduce the operational expenses. In this paper we present ENAGS (Energy and Network-Aware Genetic Scheduling algorithm) to minimize the energy consumption of servers and reduce the network traffic. The proposed algorithm takes into account communication dependencies among VMs and computational requirements of tasks to improve communication performance and minimize the energy consumption by maximizing the resource utilization. Our experimental results show that the proposed ENAGS algorithm can reduce data center energy consumption as well as network traffic by approximately 38% compared to other placement algorithms.