Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

Abstract

One of the most critical concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the virtual machine placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of Internet of Things (IoT) tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into NP-hard issues. This study proposes a multi-objective virtual machine placement to jointly minimize energy costs and scheduling. After presenting a modified Memetic algorithm, we compare its performance with baseline methods and state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, service-level agreement violations, and the total response time of IoT requests.