Optimizing Task Scheduling in Cloud Computing Using Discrete Tuna Swarm Optimization

Abstract

Task scheduling in cloud computing represents a pivotal challenge, necessitating the efficient allocation of computing tasks to available resources.This challenge is crucial in diverse sectors such as e-commerce, e-learning, and e-health, and is compounded by the heterogeneity of tasks and resources, fluctuating demands, and the need to optimize multiple objectives like Makespan, resource utilization, and throughput.In the quest to resolve these complexities, meta-heuristic algorithms inspired by natural phenomena have gained prominence.Among them, the Tuna Swarm Optimization (TSO) algorithm stands out for its proficient ability to navigate and exploit the search space effectively.This paper introduces a novel algorithm, the Discrete Tuna Swarm Optimization for Task Scheduling (DTSO-TS), derived from the TSO algorithm.DTSO-TS algorithm's goal is to efficiently distribute tasks among virtual machines, balance workloads and improve resource utilization to minimize Makespan while increasing throughput.A fitness function provides optimal solutions to this goal.Creates a swarm before evaluating and refining solutions which have proven their worth.By contrasting it with well-known scheduling algorithms such as Ant-Colony-Based, Particle Swarm Optimisation, Genetic Algorithm, First Come First Serve, Round Robin, and Shortest Job First, we may evaluate DTSO-TS's effectiveness.According to the comparison results, DTSO-TS is the best option for scheduling tasks in cloud computing contexts.