Skip to main content
SpringerLink
Log in

Integer-PSO: a discrete PSO algorithm for task scheduling in cloud computing systems

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

Abstract

Cloud computing is an emerging technology that changes the computing world through its power to serve the need of any user who requires better computing power over the Internet. For this environment the end user may want to have a better Quality of Service at low cost and cloud service providers have a different goal of achieving maximum profit and minimal management overhead. Task scheduling is a challenging task in this scenario to meet the requirements of both the ends. This work proposes a discrete version of the Particle Swarm Optimization (PSO) algorithm, namely Integer-PSO, for task scheduling in the cloud computing environment which can be used for optimizing a single objective function and multiple objective functions as well. Experimental studies on different types of task set characterising normal traffic and bursty traffic in the cloud computing environment shows that this approach is better, have good convergence and load balancing.

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Agrawal A, Tripathi S (2018) Particle swarm optimization with adaptive inertia weight based on cumulative binomial probability. J Evolut Intell. https://doi.org/10.1007/s12065-018-0188-7

    Article  Google Scholar 

  2. Alkayal ES, Jennings NR, Abulkhair MF (2016) Efficient task scheduling multi-objective particle swarm optimization in cloud computing. Proc. of 41st IEEE conference on local computer networks workshops, pp 17-24

  3. Au C, Leung H (2014) Cooperative coevolutionary algorithms for dynamic optimization: an experimental study. J Evolut Intell 7(4):201–218

    Article  Google Scholar 

  4. Beegom ASA, Rajasree MS (2014) A particle swarm optimization based pareto-optimal task scheduling in cloud computing. Lecture Notes Comput Sci 8795:79–86

    Article  Google Scholar 

  5. Beegom ASA, Rajasree MS (2015) Genetic algorithm framework for bi-objective task scheduling in cloud computing systems. Lecture Notes Comput Sci 8956:356–359

    Article  Google Scholar 

  6. Braun TD, Seigel HJ, Beck N, Boloni LL, Maheswaran M, Reuther AI, Robertson JP, Theys MD, Yao B, Hensgen D, Freund RF (2001) 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

    Article  MATH  Google Scholar 

  7. Chen WN, Zhang J (2012) A set-based discrete pso for cloud workflow scheduling with user-defined qos constraints. Proc. of IEEE International conference on systems, man and cybernetics, pp 773–778

  8. Duan R, Prodan R, Li X (2014) Multi-objective game theoretic scheduling of bag-of-tasks workflows on hybrid cloud. IEEE Trans Cloud Comput 2(1):29–42

    Article  Google Scholar 

  9. Elhady GF, Tawfeek MA (2015) A comparative study into swarm intelligence algorithms for dynamic task scheduling in cloud computing. Proc. of 7th IEEE International Conf. on Intelligent Computing and Information Systems, pp 362–369

  10. Feng M, Wang X, Zhang Y, Li J (Nov 2012) Multi-objective particle swarm optimization for reseource allocation in cloud computing. Proc. of 2nd International Conference on Cloud Computing and Intelligent Systems (CCIS), vol. 3, pp 1161-1165

  11. Gong DW, Zhang Y, Qi CL (2012) Localising odour source using multi-robot anemotaxis-based particle swarm optimisation. J Control Theory Appl IET 6(11):1661–1670

    Article  Google Scholar 

  12. Guo L, Shao G, Zhao S (Sept 2012) Multi-objective task assignment in cloud computing by particle swarm optimization. Proc. of 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM) pp 1–4

  13. Guo L, Zhao S, Shen S, Jiang C (2012) Task scheduling optimization in cloud computing based on heuristic algorithm. J Netw 7(3):547–553

    Google Scholar 

  14. Hussain I, Praveen A, Ahmad A, Qadri MY, Qadri NN, Ahmed J (2017) Nsga-ii based design space exploration for energy and throughput aware multicore architectures. Int J Cybern Syst 48(6):536–550

    Article  Google Scholar 

  15. Kennedy J, Eberhart RC (1995) A new optimizer using particle swarm theory. Proc. of 6th international symposium on micromachine and human science, pp 39–43

  16. Lalwani S, Sharma H (2019) Multi-objective three level parallel pso algorithm for structural alignment of complex rna sequences. J Evolut Intell. https://doi.org/10.1007/s12065-018-00198-y

    Article  Google Scholar 

  17. Langeveld J, Engelbrecht AP (2011) A generic set-based particle swarm optimization algorithm. Proc. of International conference on swarm intelligence

  18. Lee G (2012) Resource allocation and scheduling in heterogeneous cloud environments. PhD Thesis report of Department of Electrical Engineering and Computer Science, University of California, Berkeley

  19. Leena VA, Beegom ASA, Rajasree MS (2016) Genetic algorithm based bi-objective task scheduling in hybrid cloud platform. Int J Comput Theory Eng 8(1):7–13

    Article  Google Scholar 

  20. Li K, Xu G, Zhao G, Dong Y, Wang D (Aug 2011) Cloud task scheduling based on load balancing ant colony optimization. Proc. of Sixth IEEE Annual ChinaGrid Conference, pp 3–9

  21. Manasrah AM, Ali HB (2018) Workflow scheduling using hybrid ga-pso algorithm in cloud computing. J Wireless Commun Mob Comput 2018:16

  22. Marler RT, Arora JS (2010) The weighted sum method for multi-objective optimization: new insights. Struct Multidiscip Opt 41(6):853–862

    Article  MathSciNet  MATH  Google Scholar 

  23. Mezmaz M, Melab N, Kessaci Y, Lee YC, Talbi EG, Zomaya AY, Tuyttens D (2011) A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J Parallel Distrib Comput 71(11):1497–1508

    Article  Google Scholar 

  24. Murtza SA, Ahmad A, Qadri MY, Qadri NN, Ahmed J (2018) Optimizing energy and throughput for mpsocs: an integer particle swarm optimization approach. J Comput 100(3):227–244

    Article  MathSciNet  Google Scholar 

  25. Pandey S, Wu L, Guru SM, Buyya R (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. Proc. of 24th IEEE Conf. on Advanced Information Networking and Applications, pp 400–407

  26. Rodriguez MA, Buyya R (2014) Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans Cloud Comput 2(2):222–235

    Article  Google Scholar 

  27. Shahid A, Qadri MY, Fleury M, Waris H, Ahmad A, Qadri NN (2018) Ac-dse: approximate computing for the design space exploration of reconfigurable mpsocs. J Circuits Syst Comput 27(9):25

  28. Sidhu MS, Thulasiraman P, Thulasiram RK (2013) A load-rebalance pso heuristic for task matching in heterogeneous computing systems. IEEE Symposium on Swarm Intelligence (SIS), pp 180–187

  29. Stanimirovic IP, Zlatanovic ML, Petkovic MD (2011) On the linear weighted sum method for multi-objective optimization. Facta Universitatis, Series 26:49–63

    MathSciNet  MATH  Google Scholar 

  30. Szabo C, Kroeger T (June 2012) Evolving multi-objective strategies for task allocation of scientific workflows on public clouds. Proc. of IEEE Congress on Evolutionary Computation (CEC), pp 1–8

  31. Thant PT, Powell C, Schlueter M, Munetomo M (2017) Multiobjective level-wise scientific workflow optimization in iaas public cloud environment. J Sci Program 2017:17

  32. Tsai C, Huang W, Chiang MH, Chiang MC, Yang C (2014) A hyper-heuristic scheduling algorithm for cloud. IEEE Trans Cloud Comput 2(2):236–250

    Article  Google Scholar 

  33. Wang X, Wang Y (2012) An energy and data locality aware bi-level multiobjective task scheduling model based on mapreduce for cloud computing. Proc. of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp 648–655

  34. Yang X (2014) Swarm intelligence based algorithms: a critical analysis. J Evolut Intell 7(1):17–28

    Article  Google Scholar 

  35. Zhan S, Huo H (2012) Improved pso-based task scheduling algorithm in cloud computing. J Inf Comput Sci 9(13):3821–3829

    Google Scholar 

  36. Zhan Z, Liu X, Gong Y, Zhang J, Chung HS, Li Y (2015) Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput Surveys 47(4):33 Article 63

    Article  Google Scholar 

  37. Zhang L, Chen Y, Sun R, Jing S, Yang B (2008) A task scheduling algorithm based on pso for grid computing. Int J Comput Intell Res 4(1):37–43

    Article  Google Scholar 

  38. Zhang Y, Gong D, Cheng J (2017) Multi-objective particle swarm optimization approach for cost based feature selection in classification. IEEE/ACM Trans Comput Biol Bioinf 14(1):64–75

    Article  Google Scholar 

  39. Zhang Y, Gong D, Ding Z (2011) Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer. J Exp Syst Appl 38(11):13933–13941

    Google Scholar 

  40. Zhang Y, Gong D, Ding Z (2012) A bare-bones multi-objective particle swarm algorithm for environmental/economic dispatch. J Inf Sci 192:213–227

    Article  Google Scholar 

  41. Zhang Y, Gong DW, Sun XY, Geng N (2014) Adaptive bare-bones particle swarm algorithm and its convergence analysis. J Soft Comput 18(7):1337–1352

    Article  MATH  Google Scholar 

  42. Zuo X, Zhang G, Tan W (2014) Self-adaptive learning pso based deadline constrained task scheduling for hybrid iaas cloud. IEEE Trans Autom Sci Eng 11(2):564–573

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering, Trivandrum, India

    A. S. Ajeena Beegom

  2. Government Engineering College, Barton Hill, Trivandrum, India

    M. S. Rajasree

Authors
  1. A. S. Ajeena Beegom
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. S. Rajasree
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. S. Ajeena Beegom.

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

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-019-00216-7

Keywords

Search

Navigation