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CPU Scheduling Techniques: A Review on Novel Approaches Strategy and Performance Assessment

Journal of Applied Science and Technology Trends


The operating system (OS) is now being widely used in many of the various modern applications in recent years, ranging from diabetic monitoring to other strategic planning. The main function of any OS is to control and coordinate all hardware resources among the commands of the same/different user, which increases the efficiency of advanced comprehensive applications. In an autonomous computer system, the CPU is one of the important resources to manage and process all activities which require scheduling techniques on a processor. Since the early days of computing and other multi-programming OS, various studies have been assigned to CPU scheduling techniques based on processes management and performance evaluation. Thus, outlining the many issues related to scheduling methodologies and the weaknesses that need to be addressed. This review paper is organized based on two distinct perspectives: the implement strategies of CPU scheduling technique and criteria-based measures used, which assess how the strategies are analyzed and used under performance evaluation.


Operating system, CPU scheduling techniques, implemented strategy, performance assessment



  1. S. William, Operating systems internals and design principles. 2011.
  2. L. M. Haji, S. R. M. Zeebaree, K. Jacksi, and D. Q. Zeebaree, “A State of Art Survey for OS Performance Improvement,” Sci. J. Univ. Zakho, vol. 6, no. 3, pp. 118–123, 2018, doi: 10.25271/sjuoz.2018.6.3.516.
  3. A. S. W. Andrew S. Tanenbaum, Operating Systems Design and Implementation, Third Edition,Prentice Hall. 2006.
  4. M. A. Mohammed, M. Abdulmajid, B. A. Mustafa, and R. F. Ghani, “Queueing theory study of round robin versus priority dynamic quantum time round robin scheduling algorithms,” 2015 4th Int. Conf. Softw. Eng. Comput. Syst., pp. 189–194, 2015, doi: 10.1109/ICSECS.2015.7333108.
  5. J. Zhou, X. Zhou, Z. Chen, and X. Chen, “Research of real-time control algorithm for traffic lights based on CPU process scheduling,” 2011 IEEE Int. Conf. Anti-Counterfeiting, Secur. Identif., pp. 110–114, 2011, doi: 10.1109/ASID.2011.5967428.
  6. H. B. Parekh and S. Chaudhari, “Improved Round Robin CPU scheduling algorithm: Round Robin, Shortest Job First and priority algorithm coupled to increase throughput and decrease waiting time and turnaround time,” Int. Conf. Glob. Trends Signal Process. Inf. Comput. Commun., pp. 184–187, 2017, doi: 10.1109/ICGTSPICC.2016.7955294.
  7. S. S. Padmashree M G, “S2 R2: An enhanced method to improve the performance of CPU scheduling using Sort-Split Round-Robin Technique for load balancing .,” 2016 IEEE, pp. 854–857.
  8. M. U. Farooq, A. Shakoor, and A. B. Siddique, “An Efficient Dynamic Round Robin algorithm for CPU scheduling,” Proc. 2017 Int. Conf. Commun. Comput. Digit. Syst. C-CODE, pp. 244–248, 2017, doi: 10.1109/C-CODE.2017.7918936.
  9. G. C. Buttazzo, M. Bertogna, and G. Yao, “Limited preemptive scheduling for real-time systems. A survey,” IEEE Trans. Ind. Informatics, vol. 9, no. 1, pp. 3–15, 2013, doi: 10.1109/TII.2012.2188805.
  10. O. Ahmed and W. Abdullah, “A Review on Recent Steganography Techniques in Cloud Computing,” Acad. J. Nawroz Univ., vol. 6, no. 3, pp. 106–111, 2017, doi: 10.25007/ajnu.v6n3a91.
  11. H. Mora, “Modified Median Round Robin Algorithm ( MMRRA ),” 2017 IEEE.
  12. B. Dave, S. Yadav, M. Mathuria, and M. Tech, “Customary Methods for CPU Scheduling: A Review,” vol. 3, no. 8, pp. 344–348, 2017.
  13. A. Silberschatz, P. B. Galvin, and G. Gagne, “Operating System Concepts,” Ninth Ed. Addison Wesley, Boston, 2012.
  14. Y. H. Jbara, “A new improved round robin-based scheduling algorithm-a comparative analysis,” 2019 Int. Conf. Comput. Inf. Sci. ICCIS 2019, pp. 1–6, 2019, doi: 10.1109/ICCISci.2019.8716476.
  15. N. Almansour and N. M. Allah, “A survey of scheduling algorithms in cloud computing,” 2019 Int. Conf. Comput. Inf. Sci. ICCIS 2019, pp. 1–6, 2019, doi: 10.1109/ICCISci.2019.8716448.
  16. M. Akhtar, B. Hamid, and M. Humayun, “An Optimized Shortest job first Scheduling Algorithm for CPU Scheduling,” J. Appl. Environ. Biol. Sci., vol. 5, no. 12, pp. 42–46, 2015.
  17. F. Ahmad, I. Khan, S. A. Mahmud, G. M. Khan, and F. Z. Yousaf, “Real time Evaluation of Shortest Remaining Processing Time based Schedulers for Traffic Congestion Control using Wireless Sensor Networks,” 2013 Int. Conf. Connect. Veh. Expo, pp. 381–387, 2013, doi: 10.1109/ICCVE.2013.90.
  18. R. I. Davis and M. Bertogna, “Optimal fixed priority scheduling with deferred pre-emption,” Proc. - Real-Time Syst. Symp., pp. 39–50, 2012, doi: 10.1109/RTSS.2012.57.
  19. J. Chen, W. Huang, Z. Dong, and C. Liu, “Fixed-Priority Scheduling of Mixed Soft and Hard Real-Time Tasks on Multiprocessors,” 2017 IEEE.
  20. M. A. Shah, M. B. Shahid, S. Zhang, S. Mustafa, and M. Hussain, “Organization Based Intelligent Process Scheduling Algorithm (OIPSA),” Proc. 21st Int. Conf. Autom. Comput. Univ. Strat. Glas. UK, 11-12 Sept. 2015, doi: 10.1109/IConAC.2015.7313978.
  21. A. Noon, A. Kalakech, and S. Kadry, “A New Round Robin Based Scheduling Algorithm for Operating Systems: Dynamic Quantum Using the Mean Average,” J. Comput. Sci. Issues, vol. 8, no. 3, pp. 224–229, 2011.
  22. A. Alsheikhy, R. Ammar, and R. Elfouly, “An improved dynamic Round Robin scheduling algorithm based on a variant quantum time,” 2015 11th Int. Comput. Eng. Conf. Today Inf. Soc. ICENCO, pp. 98–104, 2015, doi: 10.1109/ICENCO.2015.7416332.
  23. S. Ullah, “Improved Optimum Dynamic Time Slicing Round Robin Algorithm,” 2017 3rd Int. Conf. Electr. Inf. Commun. Technol., no. December, pp. 7–9, 2017.
  24. L. Luo, W. Wu, D. Di, F. Zhang, Y. Yan, and Y. Mao, “A resource scheduling algorithm of cloud computing based on energy efficient optimization methods,” in 2015 IEEE 12th International Conference on e-Business Engineering 23-25 Oct. 2015, no. June, doi: 10.1109/ICEBE.2015.68.
  25. P. Sangwan, M. Sharma, and A. Kumar, “Improved Round Robin Scheduling in Cloud Computing,” Adv. Comput. Sci. Technol., vol. 10, no. 4, pp. 639–644, 2017.
  26. P. Patil and C. Borse, “Fair resource allocation to MIMO wireless system using Opportunistic Round Robin scheduling algorithm,” 2015 Int. Conf. Pervasive Comput. Adv. Commun. Technol. Appl. Soc., pp. 1–3, doi: 10.1109/PERVASIVE.2015.7087050.
  27. V. Chahar and S. Raheja, “Fuzzy based Multilevel Queue Scheduling Algorithm,” Proc. 2013 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI, pp. 115–120, 2013, doi: 10.1109/ICACCI.2013.6637156.
  28. A. Rezaee, A. M. Rahmani, S. Adabi, and S. Adabi, “A fuzzy algorithm for adaptive multilevel queue management with QoS feedback,” Proc. 2011 Int. Conf. High Perform. Comput. Simulation, HPCS 2011, pp. 121–127, 2011, doi: 10.1109/HPCSim.2011.5999815.
  29. S. Raheja, R. Dadhich, and S. Rajpal, “2-Layered Architecture of Vague Logic Based Multilevel Queue Scheduler,” Appl. Comput. Intell. Soft Comput., vol. 2014, 2014.
  30. S. M. F, “Broadcast Network ( IRBN ) Over Satellite,” Int. Conf. Intell. Comput. Control Syst. ICICCS 2017- IEEE, pp. 145–148, 2017.
  31. S. Kadry and A. Bagdasaryan, “New MLFQ Scheduling Algorithm for Operating Systems Using Dynamic quantum,” Stat. Optim. Inf. Comput., vol. 3, no. 2, pp. 189–196, 2015, doi: 10.19139/soic.v3i2.58.
  32. M. Thombare, R. Sukhwani, P. Shah, S. Chaudhari, and P. Raundale, “Efficient implementation of Multilevel Feedback Queue Scheduling,” Proc. 2016 IEEE Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2016, pp. 1950–1954, 2016, doi: 10.1109/WiSPNET.2016.7566483.
  33. S. K. Dwivedi and R. Gupta, “A simulator based performance analysis of multilevel feedback queue scheduling,” Proc. - 5th IEEE Int. Conf. Comput. Commun. Technol. ICCCT 2014, pp. 341–346, 2015, doi: 10.1109/ICCCT.2014.7001516.
  34. K. E. Hoganson and D. Ph, “Multi-Core Real-Time Scheduling in Multilevel Feedback Queue with Starvation Mitigation ( MLFQ-RT ),” ACM SE ’18, March 29–31, 2018, Richmond, KY, USA.
  35. S. Killen, E. Giese, and H. Huynh, “Marble MLFQ: An Educational Visualization Tool for the Multilevel Feedback Queue Algorithm,” 2017 IEEE, pp. 663–669.
  36. D. S. J. Shweta Jain, “Analysis of Multi Level Feedback Queue Scheduling Using Markov Chain Model with Data Model Approach,” Int. J. Adv. Netw. Appl., vol. 07, no. 06, pp. 2915–2924, 2016.
  37. A. Patil and R. V. Biradar, “Scheduling techniques for TinyOS: A review,” 2016 Int. Conf. Comput. Syst. Inf. Technol. Sustain. Solut. CSITSS, pp. 188–193, 2016, doi: 10.1109/CSITSS.2016.7779420.
  38. A. O. Chhabra, “Analytical Study of Job Scheduling using Variants of Ant Colony Optimization Technique in Grid,” 2nd IEEE Int. Conf. Eng. Technol., no. March, pp. 16–21, 2016.
  39. T. A. Maktum, R. A. Dhumal, and L. Ragha, “A genetic approach for processor scheduling,” Int. Conf. Recent Adv. Innov. Eng. ICRAIE, pp. 9–12, 2014, doi: 10.1109/ICRAIE.2014.6909108.
  40. S. Jawad, “Design and evaluation of a neurofuzzy CPU scheduling algorithm,” Proc. 11th IEEE Int. Conf. Networking, Sens. Control. ICNSC, pp. 445–450, 2014, doi: 10.1109/ICNSC.2014.6819667.
  41. T. Helmy, S. Al-Azani, and O. Bin-Obaidellah, “A machine learning-based approach to estimate the CPU-burst time for processes in the computational grids,” 3rd Int. Conf. Artif. Intell. Model. Simul., 2015, doi: 10.1109/AIMS.2015.11.
  42. K. S. Umadevi, “Multilevel Queue Scheduling in Software Defined Networks,” Int. Conf. Innov. Power Adv. Comput. Technol.
  43. S. Kumarsaroj, A. K. Sharma, and S. K. Chauhan, “A novel CPU scheduling with variable time quantum based on mean difference of burst time,” Proceeding - IEEE Int. Conf. Comput. Commun. Autom. ICCCA 2016, no. April, pp. 1342–1347, 2017, doi: 10.1109/CCAA.2016.7813986.
  44. J. Lee and K. G. Shin, “Preempt a job or not in EDF scheduling of uniprocessor systems,” IEEE Trans. Comput., vol. 63, no. 5, pp. 1197–1206, 2014, doi: 10.1109/TC.2012.279.
  45. Y. H. Jbara, “A new visual tool to improve the effectiveness of teaching and learning CPU scheduling algorithms,” 2017 IEEE Jordan Conf. Appl. Electr. Eng. Comput. Technol. AEECT 2017, vol. 2018-Janua, pp. 1–6, 2017, doi: 10.1109/AEECT.2017.8257759.
  46. O. Ahmed and A. Brifcani, “Gene Expression Classification Based on Deep Learning,” 4th Sci. Int. Conf. Najaf, SICN 2019, pp. 145–149, 2019, doi: 10.1109/SICN47020.2019.9019357.
  47. A. R. Arunarani, D. Manjula, and V. Sugumaran, “Task scheduling techniques in cloud computing: A literature survey,” Futur. Gener. Comput. Syst., vol. 91, pp. 407–415, 2018, doi: 10.1016/j.future.2018.09.014.
  48. D. Q. Zeebaree, H. Haron, A. M. Abdulazeez, and S. R. M. Zeebaree, “Combination of k-means clustering with genetic algorithm: A review,” Int. J. Appl. Eng. Res., vol. 12, no. 24, pp. 14238–14245, 2017.


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