Energy Aware Scheduling and Resource Allocation for Virtual Machine
Md Mehefujur Rahman Mubin, Sajjad Ullah, James Anthony Purification, Md. Motaharul Islam · 2023
With the growing demand for high-performance computing on virtual machines and cloud servers, which consume more power to deliver optimal performance, the need for energy-aware scheduling and resource allocation of virtual machines has become crucial. This research investigates the impact and effectiveness of energy-aware scheduling in optimizing energy consumption and resource utilization. To improve energy efficiency and optimize the consumption of virtual machines, several strategies have been employed. These include allocating clients using different scheduling algorithms based on the workload status of the VMs, as well as implementing Live Migration along with algorithms to enhance network efficiency. These algorithms are established based on process scheduling algorithms used in operating systems, ensuring users experience seamless performance on virtual machines and cloud services. Perfor-mance metrics, including response time, throughput, and average turnaround time are measured in this research to evaluate the different approaches. The experimental results demonstrate the efficacy of the proposed techniques, which achieved significant energy savings compared to existing algorithms. We observed a 0.09% improvement in overall turnaround time compared to the static scheduling algorithm and a 9.96% improvement in overall throughput through our dynamic scheduling algorithm. These findings highlight the potential of energy-aware algorithms and techniques in optimizing energy consumption and improving resource utilization in virtual machine environments.