Cloud computing has emerged as a high-performance distributed computing architecture that allows access to a pool of shared resources depending on demand through the Internet in recent years. Cloud computing is still in its infancy, and considerable study on a wide range of areas is required to reap its full benefits. Task scheduling is one of the essential aspects that should be researched in order to obtain optimal cloud performance. Due to the huge solution space and, as a result, the long time it takes to discover an optimal solution, scheduling in cloud computing is classified as an NP-hard issue. The scheduler algorithm's goal in this study is to process users' tasks in the shortest amount of time and for the least amount of money. In order to minimize overall completion time and maximize resource efficiency, all tasks should be evenly allocated across available resources. To achieve the desired goals and address the task scheduling problem, the Ant Clone Algorithm (ACO) was applied. Simulations and comparisons of the suggested method's results with those of the genetic algorithm (GA) and particle swarm optimization (PSO) reveal that the proposed methodology has been able to satisfy consumers while also maximizing resource use.
