A Powerful Load Balancing Algorithm for Cloud Performance Evaluation

The energy consumption of data centers plays a significant role in the cost estimation of maintenance, performance evaluation, and environmental pollution in cloud computing. Carbon emissions are one of the major drawback of data centers in this scenario. To manage the data center temperature as well as the necessity to reduce power consumption while satisfying customer demands. This is extremely beneficial to both the data center and the environment. The data center is a collection of servers from which consumers can benefit by using their services. In this context, cloud service providers are working hard to reduce energy usage and avoid environmental contamination. This research work describes the novel algorithm for scheduling virtual machines in servers in order to consume a minimal amount of energy. The weight approximation algorithm arranges servers and virtual machines in decreasing order depending on their configuration to allocate VMs with jobs to optimize power usage. The power consumption of the server is calculated before and after VM allocation in this algorithm. To avoid CRAC modifications, the critical threshold of each server is maintained in this instance. If it exceeds the server's critical threshold, the VM is moved to another ideal server to avoid migration. In this method of VM allocation, the ordered tasks are allocated to the server using round-robin fashion. The model is simulated using cloudsim tools, and the server's performance and energy consumption are better than the random selection algorithm.