Finding Time Quantum of Round Robin CPU Scheduling Algorithm Using Fuzzy Logic

This study proposes a new algorithm which is a logical extension of the popular Round Robin CPU scheduling algorithm. The Round Robin algorithm can be effective only if the time quantum is chosen accurately. Even by taking mean average of burst times as time quantum, the performance of the RR cannot be improved beyond a certain point. However, the novel method proposed here, suggests that a priority be assigned to each process based on balanced precedence factor. The novel method also uses mean average as a time quantum. Experiments are conducted in order to measure the effectiveness of this novel method. The results clearly showed that EPSADTQ is superior to RR and PSMTQ and its variants. EPSADTQ resulted in a significant reduction of the no. of context switches, average waiting time and average turnaround time.

In the realm of real-time and multitasking environments, the Round Robin (RR) CPU scheduling algorithm is extensively utilized. Renowned for its fairness and avoidance of process starvation, RR allocates a specific time quantum to each process. However, the frequent context switching between processes during CPU scheduling, owing to the short time quantum, can adversely impact system performance by increasing average waiting time and response waiting time. To address these concerns, this paper introduces "Improving Round Robin Scheduling Using Dynamic Time Quantum (IRRDQ)," a modified version of RR aimed at diminishing turnaround time, average waiting time, and context switching. In our proposed approach, we arrange all incoming processes based on minimum burst time and dynamically assign an optimal time quantum to each process using the Shortest Job First (SJF) algorithm. The computation of time quantum is tailored to the burst times of individual processes. Through experimental demonstrations, we showcase the significant performance improvement of our algorithm compared to RR and other existing algorithms.

Improved Performance of Round Robin CPU Scheduling Algorithm Using Non-preemptive SJF

Background: The most popular time-sharing operating systems scheduling technique, whose efficiency heavily dependent on time slice selection, is the round robin CPU scheduling algorithm. The time slice works similar to the First-Come-First-Serve (FCFS) scheduling or processor sharing algorithm if it is large or extremely too small. Some of the existing research papers have an algorithm called Improved Round Robin with Highest Response Ratio Next (IRRHRRN) which made use of response ratio with a predefined time quantum of 10ms with the major aim of avoiding starvation. However, the IRRHRRN algorithm favors processes with shorter burst time than the ones with longer burst time, and gave no regard to the process arrival time, thus leading to starvation. Aim: This study tries to improve on the IRRHRRN algorithm by proposing the Modified Round Robin with Highest Response Ratio Next (MRRHRRN) CPU Scheduling Algorithm using Dynamic Time Quantum in order to reduce the problem of starvation. Method: Dynamic method of determining the time quantum was adopted. Results: The proposed algorithm was compared with four other existing algorisms such as Standard Round Robin (RR), Improved Round Robin (IRR), An Additional Improvement in Round Robin (AAIRR), and the Improved Round Robin with Highest Response Ratio Next (IRRHRRN) and it provided some promising results in terms of the Average Waiting Time of 35407.6 ms, Average Turnaround Time of 36117.6 ms, Average Response Time of 10894.8 ms and Number of Context Switch of 301for the Non-Zero Arrival Times Processes

Branch misprediction is one of the important causes of performance degradation in superpipelined and superscalar processors. Most of the existing branch predictors, based on the exploiting of branch history, suffer from prediction accuracy decrease caused by frequent context switches. The goal of this research is to reduce misprediction rate (MPR) when the context switches are frequent, and not to increase the MPR when the context switches are relatively rare. We propose two independent, but closely related modifications of global adaptive prediction mechanisms: first, to flush only the branch history register (BHR) at context switch, instead of reinitialization of the whole predictor, and second, to use two separated BHRs, one for user and one for kernel branches, instead of one global history register. We have evaluated the ideas by measurements on real traces from IBS (Instruction Benchmark Set), and have shown that both modifications reduce MPR at negligible hardware cost.

Throughput of the system in multiprogramming and time sharing systems mainly depends on the careful scheduling of the CPU and other I/O devices. CPU scheduling should control the waiting time, response time, turnaround time, and number of context switches. One of the most extensively used scheduling algorithms is shortest next remaining time first (SRTF), which gives the reduced amount of average waiting time. But this algorithm suffers from some drawbacks. One such is that, every upcoming process if selected for execution, causes a context switch even though it is slightly shorter than the currently running process. As the number of such situations increases, the number of context switches increases, causing the reduction in performance of the system. In this paper, we modify the traditional SRTF to intelligent SRTF, by changing the decision of the preemption, to decrease the number of context switches. The main idea of our proposed algorithm is to make a context switch only if the next process plus context switch over head is shorter than the currently running process. By this we can reduce the number of context switches and thereby the performance of the system is improved.

These days, the problems of scheduling and security are increasingly pressing as a result of the rapidly growing number of implementations that are operated in operating systems. Process scheduling and scheduling security is the most effective methodology of operating a system which can improve and degrade the performance of the computer system. The performance of scheduling algorithm in operating system can be measured by scheduling principles like average waiting time, average turnaround time, response time, throughput etc. Much research carried out over operating system security and round robin scheduling used in time sharing operating system in previous years to resolve the issues regarding to improvement in the performance of round robin (RR) scheduling and improving the security in the scheduling approach. This study discussed the security issues in operating systems, scheduling security and discrete structure of a new approach for RR scheduling. The researcher has suggested a list of policy for scheduling method security and proposed a new approach for RR scheduling and investigated the scheduling principles. The proposed methodology covers distinctive strategy for selection manner of process from ready queue than RR and used diverges time quantum. The proposed methodology has been validated experimentally by comparing with a numerous scheduling algorithm namely as Round Robin (RR), Adaptive Round Robin (ARR), Round Robin Remaining time (RRRT), improved Round Robin (IRR), an additional improvement on the improved Round Robin (AAAIRR), and an Enhanced Round Robin (ERR). Numerous simulations have been conducted to validate the effectiveness of the proposed algorithm to compelling the performance of system. At the conclusion the performance of computing system is dependent on security of operating system as well as secluding methods.

Round Robin (RR) is a kind of process algorithms, where the time quantum is fixed along the processes execution. In the other hand it depending on the First Come First Serve (FCFS) algorithm. Also RR performs in timesharing system by given each process static Time Quantum (TQ). In this paper, The TQ studied to improve the efficiency of RR and performs the degrades with respect to Context Switching (CS), Average Wait Time (AWT) and Average Turned Around Time (ATAT) that an overhead on the system. Thus, the new approach was proposed to calculate the TQ, known as Ascending Quantum and Minumim-Maxumum Round Robin (AQMMRR). The processes were ascending with shortest remaining burst time and calculate the TQ from multiply the summation of minimum and maximum BT by (80) percentage. The experimental result shows that AQMMRR performs better than RR and comparing with other two related works.

Scheduling is one of the most important factors used in scheduling processes insideCPU. CPU scheduling is a concept of multiprogramming, where the CPU is used to schedule the incoming processes alternately. Many algorithms can be used to schedule processesinside CPU, but not all can be real-time. Long waiting times and response times often be problems in scheduling processes in realtime. FIFO and Round Robin algorithms can be implemented to schedule the processes in realtime. In this paper, the authors schedule the process of several sensors that are used to collect landslide data. The data processing is sent by FIFO scheduling and Round Robin scheduling separately on the Internet of Things (IoT) device. The author only analyzes the performance of FIFO and Round Robin algorithms in scheduling the incoming processes in real-time on the IoT operating system by considering the waiting time and response time. The analysis is expected to create a quick response time and waiting time so that proper algorithm is decided to complement the IoT architecture in landslide detection. The FIFO and Round Robin algorithms are implementedat the Raspbian and Arch Linux operating systems in the IoT device, the Raspberry Pi 3 Model B, which uses a 64-bit 64-bit ARM Cortex-AS3 64-bit process at 1.2GHz.

The Shortest Job First (SJF) algorithm reduces the average waiting time of processes in a CPU environment; however, it may lead to the starvation of long processes. The Round Robin (RR) algorithm solves starvation by equally distributing processing time. This paper explores user-friendly improvements by allowing users to specify which processes should be finished first. The SJF, RR, and the user-priority SJFRR were compared using four test cases in the following criteria: context switching, average waiting time, and average turnaround time. The user-priority SJFRR was found to have reduced average waiting time and average turnaround time in all test cases except for the first. Further research is also recommended in order to find out whether the algorithm's performance is attributed to the randomized assignment of priority and to find a proper basis for user designation of priority tasks.

The design of advanced controllers for many industrial processes is heavily dependent on the availability of a model for the process. Construction of appropriate models is often not possible due to the complexity and nonlinearity of the process. Fuzzy logic can be used for different tasks within intelligent control systems because they represent general, nonlinear relationships that can be initialized using expert knowledge. In adaptive control the key problem is system identification and fuzzy logic is used as process identification and acts as an on-line adaptive part of the control system. The presented work in this paper devoted to the development and the implementation of a smooth and robust fuzzy logic based PID controller targeting a real process represented by a DC servomotor. The aim is to overcome the need for an on-line tuning problem associated with the parameters of classical PID approach and enhances the performance. The adopted self-tuning strategy improves the performance automatically until it converges to a predetermined optimal global criterion. The experimental results and the performance of a developed fuzzy logic controller is compared with other methods such as, model reference adaptive controller and other fuzzy logic based controllers using the same process in terms of steady state error, settling time, and response time. The developed controller shows better performance.

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time.
 Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done.
 Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm.
 Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity.
 Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources.
 Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

CPU scheduling is the basis of multi-programmed operating systems. The scheduler is accountable for multiplexing processes on the CPU. By switching the CPU among processes, the operating system results in the computer more prolific. Various CPU scheduling algorithms exist for a multi-programmed operating system like First Come First Served (FCFS), Shortest Job First (SJF), Shortest Remaining Time Next (SRTN) and Round Robin (RR) scheduling algorithm. This paper has threefold objective. Firstly, it throws light on a new-fangled CPU scheduling algorithm called MIN-MAX whichbehaves as both preemptive and non-preemptive algorithm basis on the burst time. Secondly, it would helps to understand how MIN-MAXalgorithm improves the CPU efficiency in multi programming operating system and also trims down the starvation problem amongprocesses. Thirdly, it focuses on the comparative study of the existing algorithms on basis of various scheduling parameters with the proposed algorithm MIN-MAX.

Cloud computing is one of the most emerging technologies which has created a revolution in the High performance Computing (HPC) domain. The term Quality of Service (QoS) plays a vital role in the formation of more flexible integration of various technologies. The Waiting Time (WT), Turnaround Time (TAT), Context Switching (CS) and Makespan (MS) are the primary parameter that has great impact on the scheduling of cloudlets. The Proposed algorithm has improved the resource utilization system of the existing Round Robin Algorithm (RRA) and Improved Round Robin Cloudlet Scheduling Algorithm (IRRCSA) by introducing the concept of dynamically calculated Time Quantum (TQ) for each virtual machine (VM) according to the allocated cloudlets. This new approach in cloudlet scheduling drastically reduced average WT, average TAT and Number of CS of the VMs, which further enhanced the capability of cloud service providers (CSPs) to provide better QoS.

The performance of the multiprocessor system and time-sharing system rely upon CPU scheduling algorithm. Some of the well known efficient CPU scheduling algorithms are Round Robin scheduling (RR), Shortest job first(SJF), Preemptive version of SJF(SRTF), First Come First Serve (FCFS) etc. As of now, the Round Robin scheduling algorithm is considered as the efficient process scheduling algorithm among all the existing CPU scheduling algorithms. However, in RR the shortest one have to wait for a longer time and in SRTF longer process behaves as a suspended process as short tasks keep on executing. This paper proposes a new scheduling process algorithm ESRR (Efficient Shortest Remaining Time Round Robin) that consolidate two of the preemptive version of existing scheduling algorithms namely RR and SRTF. ESRR reduces total waiting time and turn around time in compare to RR and reduces the waiting time of the shorter process and it also provides a longer process to execute faster than SRTF.