Skip to main content
SpringerLink
Log in

Optimized scheduling algorithm for soft Real-Time System using particle swarm optimization technique

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Scheduling of tasks in Real-Time Systems is based on static or dynamic priority like earliest deadline first (EDF) and rate monotonic, respectively. The static scheduler does not give assurance of scheduling all tasks during the underload scenario, whereas dynamic scheduler performs poorly during an overload scenario. This paper has proposed a swarm intelligence-based scheduling algorithm that can overcome both the situations. This paper has used particle swarm optimization (PSO) based swarm technique to design the new scheduling approach. It considers each task as a particle and applied modified PSO technique to identify the most critical task to execute. The efficiency of the newly proposed method has been compared with existing EDF and ACO based scheduling algorithms considering two significant parameters, the success ratio and the effective CPU utilization. All three algorithms have been tested on the simulator with a Soft Real-time periodic task set on 500 timelines. It has been observed that during the underload scenario, the proposed algorithm performs equally to EDF and ACO based algorithms. During overload and highly overload situations, the proposed algorithm performs batter compared to EDF and ACO based algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Ahmad S, Malik S, Kim DH (2018) Comparative analysis of simulation tools with visualization based on real-time task scheduling algorithms for IoT embedded applications. Int J Grid Distrib Comput. https://doi.org/10.14257/ijgdc.2018.11.2.01

    Article  Google Scholar 

  2. Chatterjee K, Pavlogiannis A, Kößler A, Schmid U (2018) Automated competitive analysis of real-time scheduling with graph games. Real-Time Syst 54(1):166–207. https://doi.org/10.1007/s11241-017-9293-4

    Article  MATH  Google Scholar 

  3. Wang X, Li Z, Wonham WM (2017) Optimal priority-free conditionally-preemptive real-time scheduling of periodic tasks based on des supervisory control. IEEE Trans Syst Man Cybern Syst 47(7):1082–1098. https://doi.org/10.1109/TSMC.2016.2531681

    Article  Google Scholar 

  4. Teraiya J, Shah A (2018) Comparative study of LST and SJF scheduling algorithm in soft real-time system with its implementation and analysis. In: 2018 international conference on advances in computing, communications and informatics, ICACCI 2018, pp 706–711. https://doi.org/10.1109/ICACCI.2018.8554483

  5. Kohutka L, Stopjakova V (2016) Improved task scheduler for dual-core real-time systems. In: Proceedings—19th Euromicro conference on digital system design, DSD 2016. Institute of Electrical and Electronics Engineers Inc., pp 471–478. https://doi.org/10.1109/DSD.2016.44

  6. Teraiya J, Shah A (2020) Analysis of dynamic and static scheduling algorithms in soft real-time system with its implementation. Adv Intell Syst Comput 1053:757–768. https://doi.org/10.1007/978-981-15-0751-9_69

    Article  Google Scholar 

  7. Thakor D, Shah A (2011) D_EDF: an efficient scheduling algorithm for real-time multiprocessor system. In: Information and communication technologies (WICT), 2011 World Congress on, pp 1044–1049. https://doi.org/10.1109/WICT.2011.6141392

  8. Teraiya J, Shah A (2019) Hybrid Scheduler (S_LST) for soft real-time system based on static and dynamic algorithm. Int J Eng Adv Technol 9(2):2885–2889. https://doi.org/10.35940/ijeat.b3837.129219

    Article  Google Scholar 

  9. Alsheikhy A, Ammar R, Elfouly R, Alharthi M, Alshegaifi A (2016) An efficient dynamic scheduling algorithm for periodic tasks in real-time systems using dynamic average estimation. In: Proceedings—IEEE symposium on computers and communications (Vol. 2016-August). https://doi.org/10.1109/ISCC.2016.7543830

  10. Yu SC (2014) Elucidating multiprocessors flow shop scheduling with dependent setup times using a twin particle swarm optimization. Appl Soft Comput J 21:578–589. https://doi.org/10.1016/j.asoc.2014.04.016

    Article  Google Scholar 

  11. Kazemi H, Zahedi ZM, Shokouhifar M (2016) Swarm intelligence scheduling of soft real-time tasks in heterogeneous multiprocessor systems. Electr Comput Eng Int J. https://doi.org/10.14810/ecij.2016.5101

    Article  Google Scholar 

  12. Shah A (2014) Adaptive scheduling for real-time distributed systems. In: Biologically-inspired techniques for knowledge discovery and data mining, pp 236–248. https://doi.org/10.4018/978-1-4666-6078-6.ch011

  13. Konar D, Bhattacharyya S, Sharma K, Sharma S, Pradhan SR (2017) An improved Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) for scheduling of real-time task in multiprocessor system. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2016.12.051

    Article  Google Scholar 

  14. Beegom ASA, Rajasree MS (2019) Integer-PSO: a discrete PSO algorithm for task scheduling in cloud computing systems. Evol Intel 12(2):227–239. https://doi.org/10.1007/s12065-019-00216-7

    Article  Google Scholar 

  15. Zarrouk R, Bennour IE, Jemai A (2019) A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem. Swarm Intell 13(2):145–168. https://doi.org/10.1007/s11721-019-00167-w

    Article  Google Scholar 

  16. Pandey S, Wu L, Guru SM, Buyya R (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: Proceedings—international conference on advanced information networking and applications, AINA, pp 400–407. https://doi.org/10.1109/AINA.2010.31

  17. Guo P, Xue Z (2018) An adaptive PSO-based real-time workflow scheduling algorithm in cloud systems. In: International conference on communication technology proceedings, ICCT, 2017-October, pp 1932–1936. https://doi.org/10.1109/ICCT.2017.8359966

  18. Awadalla M, Elewi A (2016) Enhanced PSO approach for real time systems scheduling. Int J Comput Theory Eng 8(4):285–289. https://doi.org/10.7763/ijcte.2016.v8.1059

    Article  Google Scholar 

  19. Rahman HF, Janardhanan MN, Nielsen IE (2019) Real-time order acceptance and scheduling problems in a flow shop environment using hybrid Ga-PSO algorithm. IEEE Access 7:112742–112755. https://doi.org/10.1109/ACCESS.2019.2935375

    Article  Google Scholar 

  20. Eberhart R, Kennedy J (1995) New optimizer using particle swarm theory. In: Proceedings of the international symposium on micro machine and human science. https://doi.org/10.1109/mhs.1995.494215

  21. Brownlee J (2011) Clever algorithms. Search. https://doi.org/10.1017/CBO9781107415324.004

    Article  Google Scholar 

  22. Elbes M, Alzubi S, Kanan T, Al-Fuqaha A, Hawashin B (2019) A survey on particle swarm optimization with emphasis on engineering and network applications. Evol Intell 12(2):113–129. https://doi.org/10.1007/s12065-019-00210-z

    Article  Google Scholar 

  23. Dixit A, Mani A, Bansal R (2021) An adaptive mutation strategy for differential evolution algorithm based on particle swarm optimization. Evol Intell. https://doi.org/10.1007/s12065-021-00568-z

    Article  Google Scholar 

  24. Li YL, Shao W, You L, Wang BZ (2013) An improved PSO algorithm and its application to UWB antenna design. IEEE Antennas Wirel Propag Lett 12(3):1236–1239. https://doi.org/10.1109/LAWP.2013.2283375

    Article  Google Scholar 

  25. Erskine A, Joyce T, Herrmann JM (2017) Stochastic stability of particle swarm optimisation. Swarm Intell 11(3–4):295–315. https://doi.org/10.1007/s11721-017-0144-7

    Article  Google Scholar 

  26. Teraiya J, Shah A, Kotecha K (2019) ACO based scheduling method for soft RTOS with simulation and mathematical proofs. Int J Innov Technol Explor Eng 8(12):4736–4740. https://doi.org/10.35940/ijitee.L3606.1081219

    Article  Google Scholar 

  27. Shah A, Kotecha K (2010) Scheduling algorithm for real-time operating systems using ACO. In: Proceedings—2010 international conference on computational intelligence and communication networks, CICN 2010. https://doi.org/10.1109/CICN.2010.122

  28. Lindh F, Otnes T, Wennerström J (2010) Scheduling algorithms for real-time systems. Department of Computer Engineering, Malardalens University, Sweden. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Scheduling+algorithms+for+real-time+systems#0

  29. Yang K, Anderson JH (2015) On the soft real-time optimality of global EDF on multiprocessors: from identical to uniform heterogeneous. In: Proceedings—IEEE 21st international conference on embedded and real-time computing systems and applications, RTCSA 2015, pp 1–10. https://doi.org/10.1109/RTCSA.2015.14

Download references

Author information

Authors and Affiliations

  1. Marwadi University, Rajkot, Gujarat, India

    Jay Teraiya

  2. The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India

    Apurva Shah

Authors
  1. Jay Teraiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Apurva Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jay Teraiya.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Teraiya, J., Shah, A. Optimized scheduling algorithm for soft Real-Time System using particle swarm optimization technique. Evol. Intel. 15, 1935–1945 (2022). https://doi.org/10.1007/s12065-021-00599-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-021-00599-6

Keywords

Search

Navigation