Smart Virtual Machine Placement Using Learning Automata to Reduce Power Consumption in Cloud Data Centers

Abstract

Today, cloud computing is one of the most challenging research topics in the field of information technology. It is so important for computer researchers that it was included on a list of top ten technologies in the world. Data centers include reservoirs where processing power can meet the needs of many users computing. The popularity and acceptance of cloud computing has increased the number of these centers in recent years. One of the challenging issues in cloud computing environments is high energy consumption in data centers, which has been ignored in the corporate competition to develop data centers. High energy consumption by data centers leads to increased costs, as well as CO2 emissions. Researchers are now struggling to find an effective approach to decrease energy consumption in data centers. In recent years, many attempts have been made to reduce the power consumption of data centers, and many approaches have been proposed to reduce power consumption, such as hardware and software approaches and approaches using virtualization technology. In fact, placement of a virtual machine (VM) means finding a suitable physical place for the VM. The placement goal can either maximize the usage of available resources or it can save power by being able to shut down some servers. In this paper, we present an approach based on a best-fit decreasing (BFD) algorithm, which uses learning automata to reach a compromise between decreasing energy consumption and violating service level agreements.