Research Article Designing of Vague Logic Based 2-Layered Framework for CPU Scheduler Supriya Raheja School of Engineering & Technology, Department of CSE & IT, Northcap University (Formerly ITM University), Gurgaon 122017, India Correspondence should be addressed to Supriya Raheja; supriya.raheja@gmail.com Received 30 November 2015; Accepted 20 March 2016 Academic Editor: Mehmet Onder Efe Copyright © 2016 Supriya Raheja. 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. Fuzzy based CPU scheduler has become of great interest by operating system because of its ability to handle imprecise information associated with task. Tis 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 frst 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. Te 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 efectiveness and efciency of VBRR scheduler. 1. Introduction Multitasking environment of a computer system defnes the role of CPU scheduler. Te scheduler uses a scheduling algorithm to decide when to schedule the task and for how long. Te goal of system designer is to design the CPU scheduler in such a way that it gives users more efective and efcient throughput [1–3]. In this paper, the author discusses the round robin (RR) CPU scheduler. Traditional RR CPU scheduler is not able enough to know the exact attributes of task like burst time, length of time quantum, arrival time, and so forth which afect the perfor- mance of system. Recent research works handle the uncer- tainty of attributes using fuzzy logic [4]. Tese developments undoubtedly improve the performance of system. Te pro- posed work extends the fuzzy based RR scheduler to vague logic based RR scheduler. Te author calls it VBRR CPU scheduler. Vague set theory over fuzzy set theory improves the modelling of real world and becomes a promising tool to handle the impreciseness [5]. VBRR scheduler functions are fourfold. First, it addresses the impreciseness and uncertainty using vague logic. Second, it dynamically provides an optimum length of time quantum. Tird, it reduces the unnecessary context switches which further reduce the scheduler overhead. Fourth, and the last, it improves the overall performance of system in terms of average response time, average waiting time, average turnaround time, and average normalized turnaround time. VBRR scheduler works on 2-layered framework. First layer is of vague inference system which itself contains four units: Vague Logic Unit, Grade Function Unit, Data Base Unit, and -Vague Logic Unit. First two functions of VBRR are performed at this layer only. Second layer runs scheduling algorithm to schedule the tasks. Te latter two functions are performed at this particular layer. Te rest of paper is organized as follows. Section 2 outlines the related work with RR scheduler. Section 3 briefy describes the vague set theory and how it is a better tool over fuzzy set theory. Section 4 introduces the 2-layered framework for RR CPU scheduler. Section 5 presents the simulation and results to analyze the performance of VBRR scheduler with the traditional RR scheduler and two other fuzzy based CPU schedulers. Finally, Section 6 concludes the proposed work. Hindawi Publishing Corporation Advances in Fuzzy Systems Volume 2016, Article ID 2784067, 11 pages http://dx.doi.org/10.1155/2016/2784067