Temperature-Aware Virtual Machine Scheduling in Green Clouds

Abstract

Rapid growth of the demand for computational power by business, scientific and web-applications has led to the creation of large-scale data centers consuming enormous amounts of electrical power leads to increase in size of electrical bill and heat dissipation. The increased power consumption and performance of data center increases the operating temperature of computing facility. High temperature gradients degrade reliability and performance therefore it requires vigorous cooling in order to keep the equipment and the software stable. Moreover, high energy consumption not only increases operational cost, which reduces the profit margin of Cloud providers, but also leads to high carbon emissions which is not friendly for environment. Hence energy-efficient solutions are required to minimize the impact of cloud computing on both cost of operation and environment. In this thesis, a new approach for scheduling of virtual machines (VMs) in Cloud environment is presented that provides efficient green enhancements within a scalable Cloud Computing architecture. Proposed thesis “Temperature-Aware Virtual Machine Scheduling in Green Clouds” aims to maintain the temperature of Virtualized Cloud system below critical temperature threshold by scheduling VMs according to temperature of node and insures reliable quality of service (QoS). Thus apart from saving energy and money by avoiding huge investment on cooling, it also reduces carbon footprints. To demonstrate feasibility of our approach from a performance perspective, quantitative results have also shown.