TY  - JOUR
TT  - A Neural Net-Based Approach for CPU Utilization
AU  - Senan, Sibel
PY  - 2017
DA  - July
DO  - 10.17671/gazibtd.331037
JF  - Bilişim Teknolojileri Dergisi
PB  - Gazi Üniversitesi
WT  - DergiPark
SN  - 1307-9697
SP  - 263
EP  - 272
VL  - 10
IS  - 3
KW  - CPU Scheduling
KW  - Operating Systems
KW  - Neural Networks
N2  - CPUscheduling is an important subject to maximize CPU utilization in the contextof operating systems. Multiprogramming operating systems need CPU schedulingfor organization of processes to be executed. The order of process execution isdetermined by a CPU scheduling policy in use. The utilization of CPU depends onthe selection of scheduling algorithms. There are several scheduling policiesin the literature such as First-Come, First-Served scheduling, Shortest-Job-Firstscheduling, Last-Come, First-Served scheduling, Priority scheduling. On theother hand, there are some criteria (waiting time, throughput number,turnaround time, response time) to measure the eficiency of these policies. Itis important that we choose the scheduling policy which has the minimum waitingtime as this is crucial stage of utilizing CPU efficiently. This paper exploresan alternative, neural network approach to build a CPU scheduling model toobtain the waiting time measure.  In thispaper, we will show that neural networks can be used to model schedulingpolicies and can predict the waiting time of processes. Three learningalgorithms and three different neuron numbers in the hidden layer of thenetwork are studied to boost the eficiency of neural network model for waitingtime prediction. A comparison betweenNeural-Network Based Model and First-Come, First-Served scheduling,Shortest-Job-First scheduling, Last-Come, First-Served scheduling are provided. The results reveal theeffectiveness of neural networks in predicting waiting times, and thus suggestthat it can be useful and practical addition to the framework of operatingsystems.
CR  - [1] A. Silberschatz, P. B. Galvin, G. Gagne, Operating System Concepts, Sixth Edition, John Wiley and Sons Inc., New York, A.B.D., 2004.
[2] M. Milenkovic, Operating Systems Concepts and Design, Second Edition, McGraw-Hill, Computer Science Series, 2010.
[3] K. Sukhija, N. Aggarwal, M. Jindal, “An Optimized Approach to CPU Scheduling Algorithm:Min-Max”, Journal Of Emerging Technologies inWeb Intelligence, 6, 420-428, 2014.
[4] M. Saini, N. Kumar, “A Survey on CPU Scheduling”, International Journal Of Research In Computer Applications and Robotics, 3, 7-12, 2015.
[5] S.Jain and S.Jain, “A Review study on the CPU Scheduling Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering , 5, no.8, 2016.
[6] Imran Qureshi, “CPU Scheduling Algorithms: A Survey”, Int. J. Advanced Networking and Applications, 5, no.4, 1968-1973, 2014.
[7] H. Arora, D. Arora, B.Goel, P. Jain, “An Improved CPU Scheduling Algorithm”, International Journal of Applied Information Systems, 6, 7-9, 2013.
[8] N. Goel, R.B. Garg, “A Comparative Study of CPU Scheduling Algorithms”, International Journal of Graphics and Image Processing, 2, 245-251, 2012.
[9] S. Bibi, F. Azam, Y. Chaudhry, “Combinatory CPU Scheduling Algorithm”, International Journal of Computer Science and Information Security,8, 39-43, 2010.
 [10] S. Suranauwarat, "The Design and Development of a CPU Scheduling Algorithm Simulator", In Proc. of 12th WSEAS International Conference on Applied Computer Science, 164‐170, 2012.  
[11] M. Sindhu, R. Rajkamal, P. Vigneshwaran, “An Optimum Multilevel CPU Scheduling Algorithm”, International Conference on Advances in Computer Engineering,   90-94, 2010.
[12] Raman, P.K.Mittal, “An Efficient Dynamic Round Robin CPU Scheduling Algorithm”, International Journal of Advanced Research in Computer Science and Software Engineering, vol.4, no.5, 2014.
[13] N.Goel, R.B.Garg, “Simulation of an Optimum Multilevel Dynamic Round Robin Scheduling Algorithm”, International Journal of Computer Applications, 76, 7, 42-46, 2013.
[14] S. Almakdi, M. Aleisa, M. Alshehri, “Simulation and Performance Evaluation of CPU Scheduling Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering, 4, 2015.
[15] S. Yang, D. Wang, T. Chai, G. Kendall, “An improved constraint satisfaction adaptive neural network for job-shop scheduling”, Journal of Scheduling,  13,  17-38, 2010.
[16] M.D. Richard, R.P. Lippmann, “Neural Network Classifiers Estimate Bayesian a posteriori Probabilities”, Neural Computation, 3, 461-483, 1991.
[17] A.A. Collister, O. Lahav, “ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks”, Publications of the Astronomical Society of the Pacific, 116, 345-351, 2004.
[18] M. Kumar, N. S. Raghuwanshi, R. Singh, W. Wallender, W. Pruitt, “Estimating Evapotranspiration using Artificial Neural Network”, Journal of Irrigation And Drainage Engineering, 128, 224-233, 2002.
[19] M. Uzunoglu, C. Kocatepe, R. Yumurtaci, “An Artificial Neural Network Based Preestimation Filter For Bad Data Detection Identification and Elimination In State Estimation”, Mathematical and Computational Applications, 1, 159-164, 1996.
[20] S. Haykin, Neural Networks and Learning Machines (3rd Edition), Prentice Hall., 2009.
[21] K. Levenberg, “A method for the solution of certain problems in least squares”, Quarterly of Applied Mathematics, 5, 164-168, 1944.
[22] D. Marquardt, “An algorithm for least-squares estimation of nonlinear parameters”, SIAM Journal on Applied Mathematics, 11, 431-441, 1963.
[23] H. Yuand, B. M. Wilamowski, Intelligent Systems, Chapter 12, LevenbergMarquard Training, CRC Press., 2011. 
[24] M. Hestenes, E. Stiefel, “Methods of Conjugate Gradients for Solving Linear Systems”, Journal of Research of the National Bureau of Standards,  49,  409-436, 1952.  
[25] M. Avriel, Nonlinear Programming: Analysis and Methods, Dover Publishing., 2003.
UR  - https://doi.org/10.17671/gazibtd.331037
L1  - https://dergipark.org.tr/tr/download/article-file/328855
ER  -