Maximizing CPU Performance: Advancing Efficiency and Fairness through Adaptive Round Robin Scheduling with Triple Queues (ARRTQ)

In this study, we introduce the Adaptive Round Robin scheduling using triple queues (ARRTQ) in an effort to improve upon the well-known Round-Robin scheduling algorithm (RRSA). Our objective is to tackle the dynamic nature of operating systems, which necessitates continuous innovation in scheduling techniques to enhance response times and efficient use of resources. Based on process burst times, we suggest a dynamic adjustment of the time quantum. According to the average burst time of processes in the Ready Queue (RQ), this update divides processes into two separate queues: the Small Burst Time Queue (SBTQ) and the Large Burst Time Queue (LBTQ). The time quantum for each iteration is computed using the average burst time of SBTQ and LBTQ. We look at adaptability, efficiency, and fairness in a range of system tasks. Through an examination of the effects of various process parameters and system loads, we offer an understanding of the advantages and disadvantages of our suggested dynamic scheduling approach. We find that, in comparison to the conventional RRSA, the proposed ARRTQ experimental findings confirm that the ARRTQ method notably decreases TAT in contrast to the standard RRSA approach, achieving an average reduction of 42% to 45%. Moreover, the results suggest that the efficiency of ARRTQ in reducing TAT improves with an increase in the number of processes. Ultimately, proposed algorithm demonstrates the promising outcomes in enhancing scheduling efficiency and adaptability with respect to Turn around time and Total execution time of the processes.