![]() ![]() Moreover, checking the remaining burst time of the task is an essential principle applied with our proposed algorithm. The process of tuning the time quantum dynamically is repeated for each task separately and for each round. It concentrates on providing a solution for the time quantum problem by calculating the mean for all the tasks in the ready queue, which is sorted based on the SJF manner. ![]() The major contribution of this paper is enhancing the round-robin algorithm by proposing a novel technique named DRRHA. Task scheduling is one of the critical challenges that affects the overall performance of the cloud computing environment. M = The mean of burst time of all tasks in the RQ.Ĭase Study: The Impact of Integrating the Proposed Model (DRRHA) with the SJF Algorithm M = The mean of burst time of the remaining tasks in the RQ.įor (each task T i in RQ): QT ij = ( M/2) + ( M/2)/ BT ij IF ( BT ij < QT ij) : Execute ( T i) again M = The mean of burst time of the tasks that arrived in RQ. Submitted tasks in RQ based on arrival time.Īrrange all arrived tasks in RQ based on SJF QT ij: Quantum time assigned to task, T i, in the round j M: The arithmetic mean of burst time of tasks. Moreover, Figure 3 shows a flowchart describing the followed procedure of the proposed approach.īT ij: Burst time or the remaining burst time of task i in the round jĬount_Iteration: an initialized value for iteration j and Quantum Time QT ij The details of the main steps involved in the proposed approach are shown in Algorithm 2. Step 3: The task may have executed less than one time quantum. Step 2: New tasks added to the tail of the queue will be selected, set a timer to interrupt after one time slot, and dispatch the tasks. Step 1: Keep the ready queue as a FIFO queue of tasks Īlgorithm 1 The Pseudocode of the RR Algorithm in CPU Scheduling Algorithm 1 shows the pseudocode of the RR algorithm as described in. If the QT size is too large, RR tends to become an FCFS algorithm, whereas if the QT is too small, RR might perform poorly due to the context switches that cause much overhead. It is obvious from the above discussion that the efficiency of the RR algorithm depends on the QT, and therefore, the choice of the QT size is a critical issue for improving the overall performance of the RR algorithm. If the job’s execution is completed, it will be deleted directly from the ready queue. These steps are repeated for all jobs in the ready queue. If the current job is over, its execution will be paused, and it will be placed at the end of the ready queue. In the RR algorithm, the jobs share the CPU time by allocating a slice of time, usually between 10 and 100 ms for each job, called quantum time (QT). It is simple and depends on sharing CPU time. ![]() ![]() The round-robin (RR) algorithm is one of the most commonly used scheduling traditional algorithms. Round robin scheduling algorithm in java plus#The experimental results obtained using the CloudSim Plus tool showed that the DRRHA significantly outperformed the competition in terms of the average waiting time, turnaround time, and response time compared with several studied algorithms, including IRRVQ, dynamic time slice round-robin, improved RR, and SRDQ algorithms. Moreover, we applied the remaining burst time of the task as a factor to decide the continuity of executing the task during the current round. This paper introduces a novel technique called the dynamic round-robin heuristic algorithm (DRRHA) by utilizing the round-robin algorithm and tuning its time quantum in a dynamic manner based on the mean of the time quantum. Therefore, enhancing task scheduling algorithms will enhance the QoS, thus leading to more sustainability of cloud computing systems. It plays a critical role in achieving high-level performance and outstanding throughput by having the greatest benefit from the resources. One of the main issues in building cloud computing systems is task scheduling. Recently, there has been significant growth in the popularity of cloud computing systems. ![]()
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