Relative Time Quantum-based Enhancements in Round Robin Scheduling

Abstract

Modern human life is heavily dependent on computing systems and one of the core components affecting the performance of these systems is underlying operating system. Operating systems need to be upgraded to match the needs of modern-day systems relying on Internet of Things, Fog computing and Mobile based applications. The scheduling algorithm of the operating system dictates that how the resources will be allocated to the processes and the Round Robin algorithm (RR) has been widely used for it. The intent of this study is to ameliorate RR scheduling algorithm to optimize task scheduling. We have carried out an experimental study where we have developed four variations of RR, each algorithm considers three-time quanta and the performance of these variations was compared with the RR algorithm, and results highlighted that these variations performed better than conventional RR algorithm. In the future, we intend to develop an automated scheduler that can determine optimal algorithm based on the current set of processes and will allocate time quantum to the processes intelligently at the run time. This way the task performance of modern-day systems can be improved to make them more efficient.

An ameliorated Round Robin algorithm in the cloud computing for task scheduling

“…The first process in the queue is selected, and the CPU is allocated for the chosen TQ. Then the remaining burst times are moved to the tail of the ready queue [21]. Sometimes the first process is too large and the rest processes are too small, so the response time decreases which causes an increase in waiting time and turnaround time.…”

“…For interactive users, these environments must ensure reasonable response times. In the RR scheduling algorithm, every process in the ready queue receives a time slice (time quantum (TQ)) [21]. The current process is placed at the end of this ready queue when the TQ expires.…”

Big Data Analytics in Healthcare: Exploring the Role of Machine Learning in Predicting Patient Outcomes and Improving Healthcare Delivery

“…The authors suggested that machine learning could be used to improve AKI diagnosis and ultimately improve patient outcomes (Gluck and Gostin, 2023). (Iqbal et al, 2022) highlights the potential benefits of time-based enhancements in scheduling algorithms, which can be relevant for optimizing the execution of machine learning algorithms in healthcare analytics. A systematic review published in the Journal of Medical Internet Research examined the use of machine learning in predicting patient outcomes in cancer care (Wolinetz and Tabak, 2023).…”

Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling

“…In this section, we will review the relevant literature on scheduling algorithms, explicitly focusing on the approaches of dynamic time quantum and priority scheduling. For example, the authors in [1] proposed the design of four versions of the Round Robin scheduling algorithm that allows for the dynamic determination of the time quantum based on the candidate process's overall execution time. The main goal of these variations was to achieve a fair distribution of time quantum among the processes to increase the system's overall performance.…”

“…The process is then allocated memory and system resources and added to the scheduler's queue. The scheduler decides which method to execute next based on the scheduling algorithm [1][2][3][4]. Therefore, various scheduling algorithms are proposed to address this problem, each with its trade-offs and advantages.…”