Effective load balancing approach in cloud computing using Inspired Lion Optimization Algorithm

Abstract

Load balancing problem is important in cloud computing for effectively managing the cloud resources. It entails distributing incoming network traffic or computational workload among numerous servers so that no single server is overburdened, hence improving resource utilization, increasing throughput, and decreasing response time Load balancing is critical for cloud systems to achieve high availability and fault tolerance. Even though in the recent times there are various load balancing algorithms available in balancing the load across the nodes or the virtual machine It has set backs interms of task migration, response time, throughput and fault tolerance. This research aims in eliminating such drawbacks and a Bio Inspired Improved Lion Optimization Meta-Heuristic Approach is been created for solving the load Balancing issues in the Cloud. This Meta-Heuristic approach has better exploration and exploitation rate when compared with other Bio Inspired Algorithms and it does not get struck into local optima during the search process of identifying the underutilized node. This proposed work is been implemented in Cloud Sim and its performance is going to be evaluated against the bench marks that are identified from the literature