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Cloud datacenters consume enormous energy and generate heat, which affects the environment. Hence, there must be  proper management of resources in the datacenter for optimziation of energy and service quality. Virtualization-enabled computing improves the performance of the datacenters in terms of these parameters. Therefore, virtual machines (VMs) management is a required activity in the datacenter, which selects the VMs from the overloaded host for migration, VM migration from the underutilized host, and VM placement in the suitable host. In this paper, a method (SMA-LinR) has been developed using the simple moving average (SMA) integrated with linear regression (LinR) that predicts the CPU utilization and determines the overloading of the host. Further, this predicted value is used to place the VMs in the appropriate PM. The main aim of this research is to reduce energy consumption (EC) and service level agreement violations (SLAV). Extensive simulations have been performed on real workload data, and simulation results indicate that SMA-LinR provides better EC and service quality improvements.

SMA-LinR: An Energy and SLA-Aware Autonomous Management of Virtual Machines