Load balancing in cloud environment using enhanced migration and adjustment operator based monarch butterfly optimization

In the decades before the advent of computers, humans tend to make mistakes while calculating and remembering tasks. Distributed computing helped to reduce the workload of each computer by distributing the workload evenly among computers connected in the network. Cloud computing have eradicated most of the problems that occurred in distributed computing but were also prone to different types of issues. Major issues in cloud computing relate to security and load balancing. Load balance of a node relates to two important parameters namely request time and response time. Meta heuristics algorithms can be used to provide proper load balancing techniques in cloud. This paper provides a mechanism namely EMAMBO to ensure that each node is properly load-balanced in cloud. Based on different metrics considered, it could be inferred that the proposed system fares better when compared to different benchmarked existing systems.