Multi-objective Task Scheduling in Cloud Data Center using Cat Swarm Optimization Framework

Cloud computing is a burgeoning computing paradigm that offers reusability and cost-effectiveness, including a diverse array of autonomous systems. The business offers consumers customizable and expandable services on a pay-per-use basis, according to their needs and preferences. The efficacy of cloud infrastructure is contingent upon the allocation and arrangement of tasks. Efficient task scheduling plays a crucial role in minimizing power consumption within cloud infrastructures, while simultaneously maximizing the profitability of service providers through the reduction of user job processing time. The primary purpose of this study is to investigate the effectiveness of the multi-objective Cat Swarm Optimization (TA-CSO) in optimizing job scheduling for improved efficiency. The algorithm presented in this study aims to enhance the efficiency of the cloud environment by optimizing many factors such as energy consumption, cost, resource utilization, and processing time. The outcome achieved through the utilization of TA-CSO is likewise replicated by the implementation of an open-source cloud platform known as CloudSim. The findings derived from this study are contrasted with the current leading scheduling algorithms, revealing that the suggested algorithm (TA-CSO) yields an optimal equilibrium of outcomes for various objectives.