Task Scheduling in Cloud Computing Using Fossa Optimization Algorithm

Cloud computing has gained popularity due to its high-performance distributed computation and its ability to be accessed from anywhere in the world through online resources. Through cloud service providers and the Internet, it gives users access to shared computing resources. The efficient functioning of task scheduling in clouds is one of the most important research topics that have to be addressed. First, traditional scheduling methods have been used to assess the effectiveness of task scheduling algorithms for cloud computing. Traditional scheduling algorithms perform poorly as a result of the growing number of activities generated. In this study, we suggest a newly created Fossa Optimization method for job scheduling in cloud computing environments. When compared to other meta-heuristic algorithms such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Lion Optimization algorithm, the Fossa Optimization method’s advantage is its high rate of convergence. In this paper, we suggest a novel task-scheduling algorithm for cloud computing that is based on the Fossa Optimization Algorithm (FOA). The Fossa Optimization Algorithm (FOA) is a meta-heuristic optimization method inspired by nature that imitates the Fossa’s hunting and movement patterns. When compared to other scheduling algorithms, it performed exceptionally well in terms of minimizing the wait time and costs, as well as resource use and ensuring that workloads are distributed in a balanced manner.