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A Multi-Objective Optimization Scheme for Job Scheduling in Sustainable Cloud Data Centers

For a number of years, due to an exponential increase in the demand for an eco-friendly environment, there has been a rapid increase in the green city revolution across the globe. Subsequently, load shifting of major energy consumers from conventional power grids to renewable energy sources (RES) has become inevitable. Towards this end, cloud data centers (DCs) have emerged as significant consumers of energy that solely rely on power grids to fuel their day-to-day operations. Nevertheless, their energy consumption has increased significantly which in turn has substantially raised the global carbon footprint rate. These challenges can be best addressed by the judicious utilization of RES which have well established advantages like reduced operational costs and carbon emissions. Keeping in view of the above facts, the ultimate goal of the proposed work is to design a comprehensive workload classification; and job scheduling and Vitual machine placement architecture for cloud DCs powered by RES and power grids. For this, a multi-objective optimization scheme is proposed which operates in two phases. In phase I, a random forest-based wrapper scheme known as Boruta, is used for relevant feature set selection for the incoming workload. This is followed by classification of the workload using a locality sensitive hashing-based support vector machines approach. In phase II, a multi-objective optimization problem for job scheduling and VM placement is formulated with respect to parameters such as service level agreement (SLA), energy cost, carbon footprint rate (CFR), and availability of RES. It is further solved using an enhanced heuristic approach based on a greedy strategy. Our experimental evaluations show an average improvement of approximately 31 percent in energy utilization, 28 percent in energy cost, and 36 percent in CFR, with a slight degradation in SLA assurance (about 2 percent) compared with the existing schemes.