Cloud Scheduling Optimization Based on Greedy Algorithm for Cloud Simulation

Abstract: Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. Scheduling in cloud is responsible for selection of best suitable resources for task execution, by considering some static and dynamic parameters and restrictions of tasks into consideration. The existing deadline constrained application, meeting the application�s deadline requirement is critical, but there is no incentive to finish the application earlier. The proposed introduce a model of task scheduling for a cloud-computing data center to energy-efficient dynamic task scheduling. Budget-constrained greedy scheduling algorithm (BCGS). As a heuristic algorithm, BCGS dynamically estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. Simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of BCGS. Simulation results show that BCGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. Finally, the simulation is carried out and its efficiency is analysed with existing scheduling algorithms.

Keywords: Cloud Simulation, Dynamic VM Allocation, Budget-constrained, Greedy Algorithm.