Abstract
•Propose HMEERA technique to allocate resources to every incoming user request in CC.•Employs feature extraction and PCA based feature reduction process for every request.•Integration of GTOA and RSO algorithms improves resource allocation among VMs.•Validate the performance of HMEERA technique using CloudSim tool.
In recent times, cloud computing (CC) has rapidly emerged as an effective framework for offering IT infrastructure, resources, and services on a pay-per-use basis. An extensive utilization of CC and virtualization technologies has resulted in the development of large-scale data centers which spend massive quantity of energy and have significant carbon footprints. Since 3% of global electricity is being consumed by the data centers in the present world, energy efficiency becomes a major issue in data centres and cloud computing. At the same time, resource allocation finds useful in CC to effectively utilize the available computing resources in the network for facilitating the processing of complex task which necessitate large-scale processing. In this view, this paper presents new hybrid metaheuristics for energy efficiency resource allocation (HMEERA) for the CCC environment. The proposed model initially performs the feature extraction process based on the task demands from many clients and feature reduction process takes place using principal component analysis (PCA). Then, the integrated features are used by the HMEERA technique for optimal resource allocation. The HMEERA model involves the hybridization of the Group Teaching Optimization Algorithm (GTOA) with rat swarm optimizer (RSO) algorithm, called GTOA-RSO for optimal resource allocation. The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in cloud datacenter. For experimental validation, a comprehensive set of simulations were performed using CloudSim tool. The experimental results showcased the superior performance of the HMEERA model interms of different aspects.