Metahuristics based task scheduling algorithm in cloud

Cloud computing is the most trending technologies over the internet that offers numerous computing services to the users like servers, databases, software and storage that are highly scalable and cost-effective. The services provided by the cloud work on the pay-as-you-go principle which means that you can either scale up or down the resources provided by the cloud as per your requirements. Cloud computing is a highly effective and reliable technique, however, scheduling numerous tasks over cloud can result in underutilization or overutilization of resources. Thus, to map the tasks on the resources efficiently, the need to use meta-heuristic algorithms arises. The use of these algorithms provides a near-optimal or optimal solution. MEALPY is a python library which offers optimized solutions for nature-based meta-heuristics algorithms. Biological systems, Swarm intelligence, Evolutionary-based and human based meta-heuristic algorithms are used to solve real world scheduling optimization problems. In this study, we have proposed a detailed and substantiated comparison of different nature-based meta-heuristic algorithms on the basis of various variables which affects the effectiveness of execution time in scheduling tasks over cloud for achieving better optimal solution for task scheduling.