GEOTSA: Golden Eagle Optimization Task Scheduling Algorithm in Cloud Computing Environment

With the Internet of Things (IoT), devices, objects, and digital assets can communicate without human interaction. End-user devices generate massive amounts of data, which must be processed quickly in the cloud as part of the IoT. In contrast, traditional cloud computing approaches have difficulty analyzing such large volumes of data in a short amount of time, which negatively impacts IoT applications and overall network performance. Through cloud computing, independent and dependent tasks can be distributed to virtual resources effectively. The task scheduling process in cloud environments is critical to optimizing makepan, energy consumption, resource utilization, load balancing, and cost. Since scheduling is a complex issue, many metaheuristics have been developed. This paper presents a method for efficient task scheduling based on the Golden Eagle Optimizer (GEO). This metaheuristic algorithm can solve complex optimization problems in cloud environments, including task scheduling. We aim to improve the performance of IoT applications and the network by leveraging the capabilities of GEO to enhance task scheduling efficiency and effectiveness. A comparison of the proposed algorithm with other recent algorithms shows that it reduces overall makepan, resource utilization, and execution cost by up to ۵۰%.