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Abstract

nCPU scheduling is an essential mechanism implemented by the operating system to determine the execution of multiple processes by the CPU. The primary objective of the scheduling algorithms is to optimize the systems’ performance efficiently. The performance of a CPU scheduling algorithm depends on various factors and can be evaluated on various criteria like average turnaround time, average waiting time, throughput, fairness etc. This paper aims to present an optimal CPU scheduling algorithm, Adaptive Quantum Round Robin (AQRR) in a uniprocessor environment. Related work has been done on increasing the performance of existing Round Robin Algorithms using dynamic time quantum approaches, but the maximum percentage gain in turnaround time and waiting time using these approaches, over the traditional Round Robin algorithm is not more than 30% and 40% respectively. The proposed algorithm in this paper outperforms these algorithms in both turnaround time and waiting time. The AQRR algorithm is based on the standard Round Robin algorithm, but it is integrated with a dynamic time quantum which is self-adaptive triggered on the event of the arrival of new processes in the ready queue or the completion of a process in the process queue. The dynamic time quantum is updated based on the remaining burst time of the running processes in the process queue at that instance as well as a weightage associated with it. Finally, a comparative study between the prevailing similar scheduling algorithms and the proposed algorithm is observed and noted, based on different scenarios. The following algorithms are compared on two criteria: average turnaround time and average waiting time.n

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Journal of Operating Systems Development & Trends

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nParallel computing has found a mainstream backing in graphics processing units (GPUs).These resources have enormous processing capacity, are energy efficient, and are broadly available, unlike grids. Since the advent of CUDA (Compute Unified Device Architecture) developed by NVIDIA that permits GPU programming in C, C++ language, GPUs are now being used in a variety of fields, including scientific computing. This thesis focuses on the optimization of the solution of a NP- complete problem called Permuted Perceptron Problem based on cryptographic identification scheme, which particularly meets the requirements for resource restricted devices like smart cards. We will study about GPU optimization techniques for parallel metaheuristics in order to achieve an efficient solution.n

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2. Essaid M, Idoumghar L, Lepagnot J, Brévilliers M. GPU parallelization strategies for metaheuristics: a survey. Int J Parallel Emergent Distrib Syst. 2019;34(5):497-522. doi: 10.1080/17445760.2018.1428969.
3. Knudsen LR, Meier W. Cryptanalysis of an identification scheme based on the permuted perceptron problem. Lecture Notes in Computer Science International Conference on the Theory and Applications of Cryptographic Techniques. 1999:363-74. doi: 10.1007/3-540-48910-X_25.
4. Clark JA, Jacob JL. Fault injection and a timing channel on an analysis technique. Lecture Notes in Computer Science International Conference on the Theory and Applications of Cryptographic Techniques. 2002:181-96. doi: 10.1007/3-540-46035-7_12.
5. Van Luong T, Melab N, Talbi E-G. Local search algorithms on graphics processing units. a case study: the permutation perceptron problem. Lecture Notes in Computer Science. 2010:264-75. doi: 10.1007/978-3-642-12139-5_23.

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