A Quantitative approach to minimize energy consumption in cloud data centers using VM consolidation algorithm

Abstract

Cloud computing is an integral part of large-scale computing because it shares globally distributed resources. Cloud computing evolved due to the development of data centers and numerous servers across the globe. However, cloud information centers incur huge operational costs, consume high electricity, and emit tons of carbon dioxides. Cloud suppliers can leverage their resources and decrease the consumption of energy. This is possible through various methods such as dynamic consolidation of Virtual Machines (VMs), keeping the idle nodes in sleep mode, and the mistreatment of live migration. When VMs are harshly consolidated, performance can be negatively impacted. As a result, it is a desirable trait to be able to exchange energy and performance without compromising service quality, and improving the efficiency of power consumption. This research article details several novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to their best and reduce the energy consumption behind the SLA drawbacks relevant to CPU load, RAM capacity, and information measure. Simulations of extensive nature were used to validate the efficiency of the proposed algorithms. The output analysis depicts the projected algorithms scaling back the energy consumption up to some considerable level besides ensuring proper SLA. In the Proposed algorithms, the energy consumption was significantly reduced by 22% while there was an improvement in SLA up to 80% compared to other benchmark algorithms. newline