A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms

Abstract

In Recent days, cloud computing technology is used as a remote-based virtual computer resource utilization to offer consumers quick and accurate massive data services. In the cloud, infrastructure as services have a significant impact on computing efficiency by Infrastructure as a services (IaaS). Cloud computing uses on-demand resource provisioning. In the cloud, a variety of metaheuristic algorithms are helpful for process allocation. Large-scale datacenters utilise a lot of electricity, which has an influence on the environment and the economy. Using a Micro-Genetic Algorithm, a stable combined process workload allocation method with Cat Swarm Optimization (MG-CSO) is introduced by addressing pre-convergence problems and optimal resource management. For optimal computing efficiency, the resources are dynamically consolidated and clustered. Additionally, the MG-CSO Algorithm produces positive outcomes when compared to other well-known algorithms, such as the Genetic Algorithm (GA) and the Bat Algorithm (BA).This research's focus is to reduce cost, time, Service Level Agreement(SLA), energy usage, and afford good Quality of Service(QoS). Our final scientific results were achieved with 91%.