Optimizing the Cloud Computing Resources via Virtual Machine Consolidation: Current Solutions and Challenges

An explosion of various cloud computing services has occurred during the last decade on a global scale. Many people and companies now use cloud services to back up and transfer massive volumes of data. These kinds of records are kept in data centers located in the cloud, which use a lot of power to run. Carbon dioxide (CO2) emissions and increased electricity costs are two of the many negative outcomes of excessive power consumption. Consequently, there is a great deal of effort going into the scientific problem of designing and building cloud data centers that minimize energy consumption without sacrificing service performance. There is evidence that virtual machine consolidation strategies can help cloud data centers become more energy efficient. Finding the sweet spot between energy economy, minimizing Service Level Agreement Violations (SLAVs), and optimizing Quality of Service (QoS) is the biggest obstacle when it comes to virtual machine consolidation solutions. Academics have recently shown a strong interest in using AI techniques to consolidate virtual machines (VMs) to increase the efficiency of cloud resources. The purpose of this research is to examine the recently proposed strategies for making better use of cloud resources by merging virtual machines. Several criteria proposed throughout the last five years have been used to assess the ideas. An advanced method for doing research gap analysis is detailed in this work. The current methods for consolidating virtual machines, the challenges they pose, and the possible future advancements in this area are the focus of this research.