Quantum-Based Fixed Priority Scheduling

Advances in Fuzzy Systems

2016

Fuzzy based CPU scheduler has become of great interest by operating system because of its ability to handle imprecise information associated with task. This paper introduces an extension to the fuzzy based round robin scheduler to a Vague Logic Based Round Robin (VBRR) scheduler. VBRR scheduler works on 2-layered framework. At the first layer, scheduler has a vague inference system which has the ability to handle the impreciseness of task using vague logic. At the second layer, Vague Logic Based Round Robin (VBRR) scheduling algorithm works to schedule the tasks. VBRR scheduler has the learning capability based on which scheduler adapts intelligently an optimum length for time quantum. An optimum time quantum reduces the overhead on scheduler by reducing the unnecessary context switches which lead to improve the overall performance of system. The work is simulated using MATLAB and compared with the conventional round robin scheduler and the other two fuzzy based approaches to CPU scheduler. Given simulation analysis and results prove the effectiveness and efficiency of VBRR scheduler.

Section: State Of the Artmentioning

confidence: 99%

Designing of Vague Logic Based 2-Layered Framework for CPU Scheduler

Advances in Fuzzy Systems

2016

“…To consider the current state of the ready queue, the dynamic priority concept over static priority has introduced that proved the better performance [4,5]. Dynamic priority means the priority of the tasks varies based on different factors, namely, CPU burst time, waiting time, and response ratio at different levels.…”

Section: Advances In Fuzzy Systemsmentioning

confidence: 99%

Designing of 2-Stage CPU Scheduler Using Vague Logic

Advances in Fuzzy Systems

Dhadich

Rajpal³

2014

In operating system the CPU scheduler is designed in such a way that all the resources are fully utilized. With static priority scheduling the scheduler ensures that CPU time will be assigned according to the highest priority but ignores other factors; hence it affects the performance. To improve the performance, we propose a new 2-stage vague logic based scheduler. In first stage, scheduler handles the uncertainty of tasks using the proposed vague inference system (VIS). In second stage, scheduler uses a vague oriented priority scheduling (VOPS) algorithm for selection of next process. The goal of this work is to handle the uncertainty as well as to optimize both the average and the amount of variation with respect to performance matrices average waiting time, average turnaround time, and average normalized turnaround time. A simulation using MATLAB is also conducted to evaluate the performance. Simulation results show that the proposed scheduler using VOPS algorithm is better than the scheduler with traditional priority scheduling algorithm. Results are based on the dual concept of fuzzy theory and its generalization, vague theory. Additionally, this work comprises the evaluation of VOPS and shortest job first algorithm. The outcome of proposed VOPS algorithm is much closer to the result obtained by traditional shortest job first.

“…Each queue has its own scheduling algorithm, because both types of tasks have their different response time requirements. Interactive tasks in Q1 are scheduled using round robin (RR) scheduling algorithm [23,24]; it means that each task is dispatched to CPU only for given time quantum. After the expiry of each time quantum, the current running task will preempt and the next task from Q1 will be scheduled with CPU.…”

Section: Vague Based Multilevel Queue Scheduling Algorithmmentioning

confidence: 99%

2-Layered Architecture of Vague Logic Based Multilevel Queue Scheduler

Applied Computational Intelligence and Soft Computing

Dadhich

Rajpal³

2014

In operating system the decisions which CPU scheduler makes regarding the sequence and length of time the task may run are not easy ones, as the scheduler has only a limited amount of information about the tasks. A good scheduler should be fair, maximizes throughput, and minimizes response time of system. A scheduler with multilevel queue scheduling partitions the ready queue into multiple queues. While assigning priorities, higher level queues always get more priorities over lower level queues. Unfortunately, sometimes lower priority tasks get starved, as the scheduler assures that the lower priority tasks may be scheduled only after the higher priority tasks. While making decisions scheduler is concerned only with one factor, that is, priority, but ignores other factors which may affect the performance of the system. With this concern, we propose a 2-layered architecture of multilevel queue scheduler based on vague set theory (VMLQ). The VMLQ scheduler handles the impreciseness of data as well as improving the starvation problem of lower priority tasks. This work also optimizes the performance metrics and improves the response time of system. The performance is evaluated through simulation using MatLab. Simulation results prove that the VMLQ scheduler performs better than the classical multilevel queue scheduler and fuzzy based multilevel queue scheduler.