## 2 Related Work
Several variations of RR scheduling algorithms, such as shortest job first, preemptive, non-preemptive, and First Come, First Served algorithms, are analyzed in [8; 9; 10; 11] to improve performance, CPU utilization, throughput, and context switching. The Dynamic Time Slice (DTS) algorithm, designed by the authors in [12], aims to minimize time costs by prioritizing short processes and minimizing scheduling overhead. They use clustering to similar group processes and assign the average time slice of the cluster for each process. The DTS was compared with six popular scheduling algorithms using nine groups of processes, measuring waiting and turnaround times and NCS. Moreover, the RR algorithm and its variants are comprehensively reviewed in [13; 14; 15; 16]. The authors in [17] designed an MMRR (Median Mean Round Robin) method for process scheduling. This used the median and mean of the remaining burst time of the processes in each cluster to calculate an optimal dynamic time quantum. It resulted in significant enhancements in CPU overhead and utilization. A novel method presented in [18] calculates the time quantum for each cycle using the arithmetic average. The algorithm has demonstrated decreased process turnaround and waiting times through analysis and experimentation, regardless of whether the process arrival time is zero or non-zero. This algorithm is well-suited for CPU scheduling and has been shown to outperform traditional Round Robin techniques. In [19], the Dynamic Round Robin Heuristic Algorithm (DR-RHA) is introduced. This algorithm dynamically adapts the time slice by considering the task's average time quantum and the remaining burst time. Experimental results gathered using the CloudSim Plus tool demonstrate that the DRRHA algorithm outperforms other algorithms in terms of average waiting time, turnaround time, and average response time. A new method for job scheduling in cloud computing was introduced in the research article [20], offering a comprehensive framework for optimizing performance and resource management. The proposed priority-based technique addresses the challenge of fair job scheduling, ensuring equal distribution of resources and opportunities. To provide valuable insights for academics and decision makers, this research demonstrates the benefits of an efficiently optimized cloud computing ecosystem. Equitable task scheduling is critical in cloud computing, and our priority-focused method guarantees equitable resource distribution and task completion. The writer presented an online algorithm that enhances task scheduling in cloud computing systems using dynamic load management [21]. The proposed algorithm incorporates a Gaussian distribution of relevant parameters in its state space, ensuring consistency in input dimensions. Its reward function is designed to accomplish multiple objectives while adapting to varying task workloads for maximum efficiency. The work in [22] evaluates the performance of task scheduling policies in heterogeneous computing environments through simulation-based experiments. The author considered the realistic capacities of edge and cloud data centers and evaluated RR, Shortest Job First, Min-Min, and Max-Min scheduling schemes. Results showed significant performance degradation of traditional task scheduling algorithms in heterogeneous edge-cloud environments. A new task scheduling approach, multi-queue priority (MQP), is proposed to balance task allocation for latency-sensitive and delay-tolerant applications in fog computing environments. Tasks are categorized as short and long, separate queues are maintained, and the preemption time slot is dynamically updated to reduce response time and address the starvation problem [23]. A new Optimized Round Robin (ORR) CPU Scheduling Algorithm is introduced to minimize the average waiting time, average turnaround time, and number of context switch and maximize the system throughput [24]. This algorithm results from training a learning model to predict the optimum TQ value.