Multi-objective hybrid capuchin search with genetic algorithm based hierarchical resource allocation scheme with clustering model in cloud computing environment

Internet of recent decades considered cloud computing as the most effective and distributed platform. It is a comfortable and quick way to access shared resources over the Internet anytime. The major problem cloud customers face while choosing the resources for a particular application is QoS. In the cloud computing environment, various resources need to be effectively allocated on VMs by reducing makespan and synchronously increasing resource utilization. For that, a novel multi-objective hybrid capuchin search with genetic algorithm (MHCSGA) based hierarchical resource allocation is established in this work. MHCSGA optimizes the multi-objective functions like resource utilization, response time, makespan, execution time and throughput. Initially, partitioning around the K-medoids clustering method is utilized to allocate the resources optimally. During clustering, the tasks are divided into two cluster groups then, the optimization is performed to attain an optimal resource allocation process. The experimental setup is executed using the JAVA tool. For the simulation process, the proposed work uses the GWA-T-12 Bitbrains dataset. The makespan achieved by proposed algorithm for 50, 100, 150, and 200 tasks are found to be 10.45, 17.6, 25.67, and 31.34, respectively. The comparison analysis proves that the developed model attains improved performance than the state-of-the-art works.