A Survey on Nature-Inspired Optimization Methods for Effective Task Scheduling in Cloud Computing Environment

Cloud computing is an up-and-coming technology that can meet the needs of any user, wherever in the world, by sharing its vast computer resources with them through the Internet. Cloud service providers want for maximum profit with little administration overhead, while end users require improved Quality of Service at a reasonable price. In order to satisfy all the users optimal scheduling of tasks in cloud environment is difficult. Scheduling jobs effectively means deciding which ones will be carried out next and how to divide up the available resources among them. To reduce makespan and maximize resource efficiency in cloud computing, it may be necessary to schedule many jobs on separate virtual machines. Today, most Cloud-based research focuses on metaheuristic algorithms. Metaheuristic algorithms inspired by swarm intelligence, biological schemes and chemical systems have been successfully applied to the solution of practical scheduling optimization issues. This study compared and contrasted many nature-inspired metaheuristic algorithms for scheduling jobs in a cloud context, providing a holistic perspective on the strengths and weaknesses of each. Scheduling difficulties are addressed, and the study analyses nature inspired metaheuristic approaches including the firefly algorithm, cuckoo search, bat algorithm, etc., and explores their benefits and potential applications for scheduling in the cloud-based computing arena. This review aims to improve cloud computing performance by investigating the ideas and parameters of several nature-inspired work scheduling methods.