Optimizing Task Scheduling Algorithms for Efficient CPU Utilization in Enhancing Server Availability

As modern computing continues to develop, server technology must adapt to handle the increasing demands, particularly in the context of big data processing. This paper focuses on addressing the challenges in allocating and scheduling server CPU resources for efficient big data processing. In the event that the CPU has processed large volumes of big data until the CPU usage reaches 100%, if it is interrupted by another process, such as updating the operating system software, it will be affected, leading to a potential impact on server availability. The study proposes employing Task Scheduling Algorithms such as Earliest Due Date (EDD), Longest Processing Time (LTP), and Round-Robin (RR) to optimize CPU usage. Additionally, through the execution of operating system commands, CPU cores are efficiently allocated to tasks according to their priority and processing time. An experimental application of these techniques in processing 75 jobs showed successful report generation while maintaining CPU utilization below 100%. This paper concludes that capping CPU usage at approximately 80-90% allows for enhanced system stability and an improved ability to manage interrupts, thus optimizing server performance for big data processing.