Skip to main content
SpringerLink
Log in

A Heterogeneous Multiprocessor Independent Task Scheduling Algorithm Based on Improved PSO

  • Conference paper
  • First Online:
Security with Intelligent Computing and Big-data Services (SICBS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 895))

Abstract

The independent task scheduling problem of heterogeneous multi-processors belongs to the NP-hard problem. The emergence of evolutionary algorithms provides a new idea for solving this problem. Particle swarm optimization (PSO) is a kind of intelligent evolutionary algorithm and it could be used to solve scheduling problem. We firstly discretized the representation of particle swarm optimization algorithm and made it suitable for the scheduling problem of heterogeneous multiprocessors. Then, the PSO algorithm was introduced into heterogeneous multiprocessors independent task scheduling problem by modeling method. In order to overcome particle swarm optimization algorithm’s problem that is easy to fall into local optimum and premature convergence. We proposed a heterogeneous multiprocessor independent task scheduling algorithm based on improved PSO by improving the update operation of particle swarm optimization algorithm and transformed it into crossover and mutation operation of genetic algorithm. The experimental results show that the improved PSO scheduling algorithm can overcome the premature defects of PSO algorithm and the makespan of proposed IPSO is smaller than PSO.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Iturriaga, S., et al.: A parallel local search in CPU/GPU for scheduling independent tasks on large heterogeneous computing systems. J. Supercomput. 71(2), 648–672 (2014)

    Article  Google Scholar 

  2. Sahni, S.K.: Algorithms for scheduling independent tasks. J. ACM 23(1), 116–127 (1976)

    Article  MathSciNet  Google Scholar 

  3. Shriya, S., et al.: Directed search-based PSO algorithm and its application to scheduling independent task in multiprocessor environment 404, 23–31 (2016)

    Google Scholar 

  4. Yi, J., et al.: Reliability-guaranteed task assignment and scheduling for heterogeneous multiprocessors considering timing constraint. J. Signal Process. Syst. 81(3), 359–375 (2014)

    Article  Google Scholar 

  5. Kumar, N., Vidyarthi, D.P.: A novel hybrid PSO–GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems. Eng. Comput. 32(1), 35–47 (2015)

    Article  Google Scholar 

  6. Xu, Y., Li, K., Hu, J., et al.: A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf. Sci. 270(6), 255–287 (2014)

    Article  MathSciNet  Google Scholar 

  7. Ayari, R., et al.: ImGA: an improved genetic algorithm for partitioned scheduling on heterogeneous multi-core systems. Des. Autom. Embed. Syst. 22(1–2), 183–197 (2018)

    Article  Google Scholar 

  8. Jiang, Y., et al.: DRSCRO: a metaheuristic algorithm for task scheduling on heterogeneous systems. Math. Probl. Eng. 2015, 1–20 (2015)

    MathSciNet  MATH  Google Scholar 

  9. Prescilla, K., Immanuel Selvakumar, A.: Modified Binary Particle Swarm optimization algorithm application to real-time task assignment in heterogeneous multiprocessor. Microprocess. Microsyst. 37(6–7), 583–589 (2013)

    Article  Google Scholar 

  10. Xie, G., et al.: Mixed real-time scheduling of multiple DAGs-based applications on heterogeneous multi-core processors. Microprocess. Microsyst. 47, 93–103 (2016)

    Article  Google Scholar 

  11. Xu, C., Li, T.: Chemical reaction optimization for task mapping in heterogeneous embedded multiprocessor systems. Adv. Mater. Res. 712–715, 2604–2610 (2013)

    Google Scholar 

  12. Xu, Y., et al.: A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization. J. Parallel Distrib. Comput. 73(9), 1306–1322 (2013)

    Article  Google Scholar 

  13. Rzadca, K., Seredynski, F.: Heterogeneous multiprocessor scheduling with differential evolution. In: IEEE Congress on Evolutionary Computation (2005)

    Google Scholar 

  14. Gogos, C., et al.: Scheduling independent tasks on heterogeneous processors using heuristics and Column Pricing. Future Gener. Comput. Syst. 60, 48–66 (2016)

    Article  Google Scholar 

  15. Braun, T.D., et al.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61(6), 810–837 (2001)

    Article  Google Scholar 

  16. Dorronsoro, B., Pinel, F.: Combining machine learning and genetic algorithms to solve the independent tasks scheduling problem. In: IEEE International Conference on Cybernetics (2017)

    Google Scholar 

  17. Zhou, Y., Jiang, C., Fang, Y.: Research on independent task scheduling algorithm in heterogeneous environment. Comput. Sci. 35(8), 90–92+97 (2008)

    Google Scholar 

  18. Omidi, A., Rahmani, A.M.: Multiprocessor independent tasks scheduling using a novel heuristic PSO algorithm. In: IEEE International Conference on Computer Science and Information Technology, pp. 369–373. IEEE (2009)

    Google Scholar 

  19. Zhang, W., et al.: Energy-aware real-time task scheduling for heterogeneous multiprocessors with particle swarm optimization algorithm. In: Mathematical Problems in Engineering, pp. 1–9 (2014)

    Google Scholar 

  20. Sarathambekai, S., Umamaheswari, K.: Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem. J. Algorithms Comput. Technol. 11(1), 58–67 (2016)

    Article  MathSciNet  Google Scholar 

  21. Chen, J., Pan, Q.: Improved particle swarm optimization algorithm for solving independent task scheduling problem. Microelectron. Comput. 34(6), 214–215 (2008)

    Google Scholar 

  22. Wang, Y., Wang, N., Yang, C., et al.: A discrete particle swarm optimization algorithm for task assignment problem. J. Cent. South Univ. (Sci. Technol.) 39(3), 571–576 (2008)

    Google Scholar 

Download references

Acknowledgments

As the research of the thesis is sponsored by National Natural Science Foundation of China (No: 61662017, No: 61262075), Key R & D projects of Guangxi Science and Technology Program (AB17195042), Guangxi Science and Technology Development Special Science and Technology Major Project (No: AA18118009), Guangxi Key Laboratory Fund of Embedded Technology and Intelligent System, we would like to extend our sincere gratitude to them.

Author information

Authors and Affiliations

  1. Guangxi Key Laboratory of Embedded Technology and Intelligent System, College of Information Science and Engineering, Guilin University of Technology, Jiangan Road No. 12, Guilin, 541000, China

    Xiaohui Cheng & Fei Dai

Authors
  1. Xiaohui Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fei Dai .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Ching-Nung Yang

  2. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Sheng-Lung Peng

  3. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cheng, X., Dai, F. (2020). A Heterogeneous Multiprocessor Independent Task Scheduling Algorithm Based on Improved PSO. In: Yang, CN., Peng, SL., Jain, L. (eds) Security with Intelligent Computing and Big-data Services. SICBS 2018. Advances in Intelligent Systems and Computing, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-16946-6_21

Download citation

Publish with us

Policies and ethics

Search

Navigation