منهجية محسنة لجدولة المهام في منصات الحوسبة السحابية

Abstract

Cloud computing is a model that provides easy and flexible access to computing resources and services over the Internet. Task scheduling in cloud computing significantly and directly affects resource utilization and the operational costs of the cloud system.

There are many studies that have addressed the issue of task scheduling in cloud computing, some of which proposed new methodologies, while others combined algorithms to leverage the advantages of each algorithm to improve performance. Recently, intelligent algorithms have been used to enhance the efficiency of task execution in cloud computing, including metaheuristic algorithms that have proven effective in finding optimal scheduling solutions, such as Ant Colony Optimization, Bee Algorithm, and Crow Search Algorithm. In this research, the latest metaheuristic algorithms for task scheduling in cloud computing will be applied, specifically the Whale Optimization Algorithm (WOA). This algorithm utilizes a multi-objective optimization model aimed at improving the performance of the cloud system. Additionally, this research proposes an enhancement to the Whale Algorithm to improve the search capability for optimal scheduling solutions. The results obtained from this research show a clear improvement in reducing the load cost on the system, minimizing time cost, and consequently lowering the total cost of the entire cloud system.

This research can be applied in cloud systems and platforms to enhance performance and reduce overall costs in these systems.