An efficient multi-objective task scheduling for Green cloud computing using hybrid GSCOA algorithm

Abstract

With the expansion of data centres in recent years, energy-related challenges have become worse. Green cloud computing (GCC) is a new computing paradigm designed to address cloud data centre energy consumption. Even with the advancements in GCC, large-scale green cloud data centres (GCDCs) continue to confront significant obstacles in lowering carbon emissions and energy consumption, particularly in the area of task scheduling. Ineffective task distribution can result in underutilized servers and overworked servers, wasting energy. Workload fluctuations make it difficult to manage resources effectively, which frequently results in energy spikes during periods of high demand. These dynamic demands are frequently not adequately satisfied by the current scheduling techniques since they might not take into consideration changing workload patterns. Therefore, in this work, an effective hybrid Greylag Sand Cat Swarm Optimization Algorithm (GSCOA) is introduced to schedule the task effectively in GCDC. This hybrid approach makes use of the Sand Cat Swarm Optimization Algorithm's (SCSOA) exploitation skills and the Greylag Goose Optimization algorithm's (GGOA) exploring capabilities. This combination makes it possible to schedule cloud user requirements to the cloud server efficiently by minimizing energy consumption. It helps the cloud server system emit less carbon dioxide, allowing for a more environmentally friendly atmosphere. Simulation results on two real-world workloads-NASA-IPSC and HPC2N, indicate that the proposed approach significantly outperforms existing scheduling methods by reducing energy consumption and improving overall system performance.