Research Article 2-Layered Architecture of Vague Logic Based Multilevel Queue Scheduler Supriya Raheja, 1 Reena Dadhich, 2 and Smita Rajpal 3 1 Department of Computer Science & Engineering, ITM University, Gurgaon, India 2 Department of Computer Science, University of Kota, Near Kabir Circle, MBS Marg, Swami Vivekanand Nagar, Kota, Rajasthan 324 005, India 3 Alpha Global IT, 1262 Don Mills Road, Toronto, ON, Canada M3B 2W7 Correspondence should be addressed to Supriya Raheja; supriya.raheja@gmail.com Received 28 July 2014; Revised 10 September 2014; Accepted 22 September 2014; Published 9 October 2014 Academic Editor: Baoding Liu Copyright © 2014 Supriya Raheja et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 afer the higher priority tasks. While making decisions scheduler is concerned only with one factor, that is, priority, but ignores other factors which may afect the performance of the system. With this concern, we propose a 2-layered architecture of multilevel queue scheduler based on vague set theory (VMLQ). Te VMLQ scheduler handles the impreciseness of data as well as improving the starvation problem of lower priority tasks. Tis work also optimizes the performance metrics and improves the response time of system. Te 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. Terefore, CPU scheduler plays a pivot role in operating system as it shares the CPU time among diferent 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) schedul- ing algorithm is among one of the preferable algorithms by OS designers [1, 2]. Te kernel of operating system divides the CPU time among diferent queues depending on its requirement of I/O and CPU. But this share is fxed; 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 signifcant enhancements to the approach which considers uncertainty factors [3]. Tere is one approach in literature that adapts the variations using fuzzy logic [4]. Tis paper concentrates on the dealing of uncertainty and impreciseness of task’s parameters using another approach, that is, vague logic. Vague logic is an extended formation of fuzzy logic which becomes a dedicated tool to handle the imprecise information. With this aim, a vague inference system is designed inside the scheduler that deals with the uncertainty and impreciseness of tasks. Tis work also focuses on improving the performance of MLQ scheduler. We are introducing a new vague logic based MLQ scheduler which performs two main functions. First, it distributes the CPU time among diferent queues dynamically and adapts changes with the variations in usage. Second, it resolves the starvation problem of lower priority tasks by making decisions using vague based multilevel queue scheduling Hindawi Publishing Corporation Applied Computational Intelligence and So Computing Volume 2014, Article ID 341957, 12 pages http://dx.doi.org/10.1155/2014/341957