An Intelligent Algorithm for Automatic Runtime Selection of Scheduling Algorithm using Pattern Recognition Techniques

This paper presents a dynamic central processing unit (CPU) scheduling algorithm selection system that utilizes machine learning to optimize the process execution within a computer system. This study evaluated six scheduling algorithms based on key performance indicators such as CPU utilization, turnaround time, waiting time, response time, and throughput. To overcome the limitations of traditional approaches, the proposed methodology integrates a neural network to identify patterns and dynamically choose the most appropriate algorithm at runtime. The paper reviews related literature, highlighting efforts to enhance scheduling algorithms through machine learning and improvements to conventional methods. The methodology includes developing a process database, applying scheduling algorithms, optimizing the outcomes, and training a neural network for dynamic algorithm selection. The results indicate that the proposed approach outperforms existing algorithms regarding waiting time, turnaround time, throughput, and execution efficiency. Additionally, the methodology offers adaptability to diverse process parameters. The study concludes by underscoring the potential for future advancements, including improvements in algorithm precision, incorporation of additional process characteristics, exploration of advanced pattern recognition techniques, and integration of security measures for real-world applications such as cloud computing, edge computing, and IoT (Internet of things) environments.