Cloud Resource Allocation and Scheduling: A Comprehensive Study

Abstract: Cloud computing has now become a basic element of IT services delivery, enabling the provision of on demand, scalable computing resources over the Internet, in an efficient manner. The challenge for the efficient management of the resources in cloud computing environments, which are scalable and dynamic by nature, arises from their increasing scale. Traditional methods fail to address the issue of the efficient scheduling of tasks for the load balancing in such environments. In order to overcome this challenge, numerous scheduling methods that are based on metaheuristics have been proposed and effectively applied to various types of cloud computing environments. The aim of this paper is to present a comprehensive survey and comparison of five well known, efficient scheduling methods, which are based on metaheuristics. In the paper, five typical methods, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Artificial Bee Colony (ABC), are studied in detail. The principles, advantages, and disadvantages of the five methods are discussed in the paper, and a comparison of the methods is also presented, based on a number of important factors, such as the quality of the solutions found, the execution time, the resource utilization, the scalability of the methods, and the corresponding computational costs.

Keywords: Cloud Computing, Resource Allocation, Task Scheduling, Metaheuristic Algorithms, Optimization.