Feasible Load Balancing for Webserver in Cloud Environment Using Energy Efficient Maximal Support Priority Scheduling Approach

Task management and energy efficiency have grown increasingly crucial in cloud computing environments. Effective job scheduling is essential to maximizing resource usage and overall efficiency in cloud computing. However, ensuring energy efficiency is a critical challenge in task scheduling. Traditional task-scheduling methods are often costly and require more adaptability and efficiency. To solve this problem, we propose an Energy Efficient Maximal Support Priority Scheduling Approach (EEMSPS), which schedules tasks in a virtual cloud environment. Furthermore, the Task Completion Time (TCT) algorithm is utilized to calculate the task length and time. Additionally, the Scheduling Based Artificial Bee Colony (SBABC) method is employed to organize the task and reduce data processing time. Lastly, the EEMSPS algorithm assesses the load balancing in the cloud. Additionally, energy efficiency, task execution time, and energy consumption are used to evaluate the proposed method's performance compared to traditional techniques. Therefore, the energy efficiency performance is 96.4% for task scheduling in the cloud.