Resource Allocation in Cloud Computing Systems Using Game Theory

Abstract

Cloud computing systems consist of multiple servers that share their resources. The way these resources are utilized to execute programs affects both execution time and energy consumption. Various methods have been proposed to address this challenge, including greedy, dynamic, and metaheuristic algorithms, with the Ant Colony Optimization (ACO) algorithm being one of the most significant among them. The ACO algorithm is specifically used to find the optimal mapping of tasks to servers. Given the characteristics of metaheuristic algorithms and the inverse relationship between the number of iterations and the proximity to the optimal solution, it is crucial to prioritize these parameters to achieve better outcomes. Consequently, reaching the optimal solution may require more time. This study aims to investigate the parameters involved in the optimization and management of virtual machines in a cloud computing environment.