Experimental Comparison of Three Scheduling Algorithms for Energy Efficiency in Cloud Computing

Nowadays, with the increased deployment of servers to facilitate high performance computing (HPC) for scientific and engineering applications lead to large consumption of energy. Cloud computing is a cost-effective solution, as it allows to host storage, computational and supported network services on a shared infrastructure of physical servers. However, the growing demand of cloud infrastructure among the IT companies is drastically increasing, by which data centers are drawing more energy. Energy efficient scheduling is one effective solution to streamline the resource usage as well as reduce the energy consumption. The proposed work in this paper demonstrates the resource allocation and makes an energy consumption analysis of Greedy, Round Robin and Power Aware Best Fit Decreasing scheduling algorithms on a private academic cloud. This paper provides an insight into the working of different scheduling scenarios for cloud computing and demonstrates the potential for the improvement of energy efficiency of PABFD algorithm under academic workload.