Virtualization technology has revolutionized the mobile network and widely used in fifth-generation innovation. It is a way of computing that allows dynamic leasing of server capabilities in the form of services like Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The proliferation of these services among the users led to the establishment of large-scale cloud data centers that consume an enormous amount of electrical energy and results into high metered bill cost and carbon footprint. In this paper, we propose three heuristic models, namely, median migration time, smallest void detection, and maximum fill technique, that can reduce energy consumption with minimal variation in service-level agreements negotiated. Specifically, we derive the cost of running cloud data center, cost optimization problem, and resource utilization optimization problem. Power consumption model is developed for cloud computing environment focusing on linear relationship between power consumption and resource utilization. A virtual machine (VM) migration technique is considered, focusing on synchronization-oriented shorter stop-and-copy phase. The complete operational steps as algorithms are developed for energy-aware heuristic models including median migration time, smallest void detection, and maximum fill technique. To evaluate the proposed heuristic models, we conduct experimentations using PlanetLab server data often in ten days and synthetic workload data collected randomly from the similar number of VMs employed in PlanetLab servers. Through evaluation process, we deduce that the proposed approaches can significantly reduce the energy consumption, total VM migration, and host shutdown while maintaining high system performance.
|Number of pages
|Transactions on Emerging Telecommunications Technologies
|Early online date
|29 May 2018
|Published - 1 Nov 2018