Hybrid CPU Scheduling Algorithm for Operating System to Improve User Experience

The operating system employs CPU scheduling algorithms to allocate the CPU or system resources among the jobs that the job scheduler schedules to run. The goal of the operating system is to divide the resources in a manner that takes into account system objectives such as response times, throughput, and efficiency. Through the use of CPU scheduling algorithms, tasks are distributed to the CPU, ensuring optimal resource allocation and achieving the desired system objectives. Traditional CPU scheduling methods like FCFS, SJF, and RR have limitations like starvation and suboptimal CPU utilization. Modern operating systems have adopted hybrid scheduling techniques to overcome these limitations and improve the system’s performance. These techniques combine multiple algorithms, which resulted in improved efficiency, reduced starvation, and better CPU utilization. By utilizing hybrid scheduling techniques, modern operating systems optimize resource allocation and enhance system performance. The proposed algorithm presents a hybrid scheduling technique, which exploits a Red–Black Tree structure for efficient job selection with a time complexity of (Log2n), that prioritizes jobs and executes them within a specified time period using the Incremental Time Quantum Round Robin (ITQRR) method. This approach improves user experience and CPU performance, combining the Red–Black Tree (RBT) with a multi-queue strategy. The “Time Quantum Incremental” concept of Multilevel Queue (MQ) scheduling algorithms dynamically adjusts the time quantum for subsequent processes. The algorithm outperforms previous algorithms in response time, average completion time, and resource utilization. It excels at prioritizing critical tasks, making it suitable for real-time systems. These enhancements result in notable outcomes, including a remarkable 50% reduction in CPU idle time and a significant 42% improvement in turnaround time.