Load Balancing in Cloud Computing Environment using Butterfly Optimization Algorithm

Load balancing is the process of distributing the incoming traffic of the network among various resources and servers. When one of the servers fails, the load balancer transfers the network traffic to the other servers across the group. As soon as a server is joined to the group, the load balancer begins delivering traffic to that server as part of its balancing mechanism. It is a challenging effort to use distributed storage systems to manage a big amount of data and the synchronisation issue between many consumers or producers. In order to address this issue, this research proposal offers payload benchmarking with dynamic load balancing in distributed streaming storage systems using Butterfly Optimization Algorithm (BOA). The main objective of the optimization technique is to analyze the benchmarking data and find the failure prediction of Kafka benchmarking. The BOA is used to solve optimization problems, particularly when there is insufficient or faulty data. To achieve the highest throughput from the streaming storage system, the performance of the benchmarking tool for distribution has been enhanced. The performance of the Kafka and DevOps systems is validated by the parameters of network latency as 3800ms, Execution time as 970ms, Memory size as 385kb, and Bandwidth as 380kb. The performance of the benchmarking tool for distributing the streaming storage system has been improved to achieve the maximum possible throughput from the streaming storage system.