Optimizing scheduling in cloud using a meta-heuristic approach

Abstract

Cloud computing aims to optimal use of its resources by aggregating them to increase throughput and solve difficult computational problems in the most efficient way possible. Task scheduling problem is incompliant with exact solutions in cloud due to its NP-hard nature. To address this, various meta-heuristic strategies have been developed. A task scheduler should locate the optimal resources for the user’s job while taking into account specific cloud task parameter constraints. Here, a hybrid task scheduling strategy is described that incorporates deep learning and nature-inspired meta-heuristic optimization to maximize cloud throughput while minimizing completion time in an IaaS cloud. The scheduler succeeds towards cloudlet allocation resulted to shorter makespan and higher system throughput. The novel scheduling technique was evaluated against certain algorithms using the CloudSim software. When compared to existing algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), the experimental findings show that the suggested approach outperforms them.