2-Layered Architecture of Vague Logic Based Multilevel Queue Scheduler

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. 1. Introduction In multitasking operating systems, multiple tasks need to be executed concurrently. Therefore, CPU scheduler plays a pivot role in operating system as it shares the CPU time among different tasks. For making the decision of scheduling next task for CPU, scheduler runs scheduling algorithm. Hence, the performance of system varies very much with scheduling algorithm used. Multilevel queue (MLQ) scheduling algorithm is among one of the preferable algorithms by OS designers [1, 2]. The kernel of operating system divides the CPU time among different queues depending on its requirement of I/O and CPU. But this share is fixed; it cannot be changed dynamically with variations in usage, since kernel is not aware of the exact parameters of task, like priority of task. However, in case of MLQ, priority plays a key role in decisions of scheduler. Recent evolutions in MLQ schedulers have contributed towards improvement of MLQ approach, but no significant enhancements to the approach which considers uncertainty factors [3]. There is one approach in literature that adapts the variations using fuzzy logic [4]. This paper concentrates on the dealing of uncertainty and impreciseness of task’s