An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing

Abstract

With the increase of mega-cities, the demand for Smart Cities is overgrowing. The mega-cities can be smarter through the Cloud of Things (CoT). Efficient energy consumption in Smart Cities has a massive impact on the environment. But, computational power is increasing rapidly in the cloud computing environment. Enormous energy consumption (EC) and Service Level Agreement violation (SLAV) becomes a key concern. The Virtual Machine (VM) consolidation approach can significantly reduce EC, SLA violation (SLAV), and increase resource utilization. However, dynamic VM consolidation may produce performance degradation of Physical Machines (PMs) and SLAV. Therefore, it becomes essential to find a trade-off between EC and SLAV. Herein, a novel New Linear Regression(NLR) prediction model, host overload/underload, and VM placement policy have been proposed to reduce EC and SLAV. The NLR model’s primary intention is to take that the model goes through a straight line and a mean point. Future CPU utilization is predicted based on the proposed NLR model. Evaluation of proposed algorithms has been accomplished by extending CloudSim Simulator. The experiment shows that proposed algorithms reduced EC and SLAV in cloud data centers and can be used to construct a smart and sustainable environment for Smart Cities.