Abstract
Cloud computing has spread fast because of its high performance distributed computing. It offers services and access to shared resources to internet users through service providers. Efficient performance of task scheduling in clouds is one of the most important research issues which needs to be focused on. Various task scheduling algorithms for cloud based on metaheuristic techniques have been examined and showed high performance in reasonable time such as scheduling algorithms based on Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). In this paper, we propose a new task-scheduling algorithm based on Lion Optimization Algorithm (LOA), for cloud computing. LOA is a nature-inspired population-based algorithm for obtaining global optimization over a search space. It was proposed by Maziar Yazdani and Fariborz Jolai in 2015. It is a metaheuristic algorithm inspired by the special lifestyle of lions and their cooperative characteristics. The proposed task scheduling algorithm is compared with scheduling algorithms based on Genetic Algorithm and Particle Swarm Optimization. The results demonstrate the high performance of the proposed algorithm, when compared with the other algorithms.
Highlights
Cloud computing is considered to be a distributed system that offers services to internet users through service providers such as Amazon, Google, Apple, and Microsoft
Several scheduling algorithms based on heuristic algorithms, such as Min-Min, Max-Min, and Heterogeneous Earliest Finish Time (HEFT) algorithms have been developed for cloud systems [3], [4]
Different metaheuristic task scheduling algorithms that generate optimal schedules, such as the scheduling algorithm based on Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) [3], [5] have been developed
Summary
Cloud computing is considered to be a distributed system that offers services to internet users through service providers such as Amazon, Google, Apple, and Microsoft. Cloud computing uses internet technologies to offer elastic services that support variable workloads and dynamic access to computing resources. Many of the scientific researches on cloud computing had focused on the performance efficiency of task scheduling. Several scheduling algorithms based on heuristic algorithms, such as Min-Min, Max-Min, and Heterogeneous Earliest Finish Time (HEFT) algorithms have been developed for cloud systems [3], [4]. Different metaheuristic task scheduling algorithms that generate optimal schedules, such as the scheduling algorithm based on Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) [3], [5] have been developed
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