Skip to main content
Log in

Combining Range-Suffrage and Sort-Mid Algorithms for Improving Grid Scheduling

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

A Correction to this article was published on 16 September 2021

This article has been updated

Abstract

Grid scheduling is one of the most known NP-complete problems. Heterogeneity of machines causes mapping of tasks to be a challenging problem. Several meta-heuristic algorithms have been designed to reach optimality as possible. Sort-Mid is a recent efficient scheduler of excellent resource utilization. Its strategy was based on computing the mean of two consecutive middle values in the sorted list of completion time. Thereafter, it maps the task with the maximum mean to the machine having the minimum completion time. However, Range-Suffrage scheduler obtains a better makespan. It was built on searching for the maximum average value of completion times among certain tasks. These tasks were selected according to their suffrage values under specified constraint. This paper proposes RSSM as highly efficient scheduler. It combines Sort-Mid and Range-Suffrage algorithms to achieve the maximum resource utilization and the minimum makespan. RSSM methodology is based on sorting only the completion time corresponding to tasks satisfying Range-Suffrage constraint. Experimental tests manifest the superiority of proposed algorithm over the two original meta- heuristics and other promising algorithms such as Min-Min.

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

Access this article

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

Similar content being viewed by others

Change history

References

  1. Braun TD, Siegel HJ, Beck N et al (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  Google Scholar 

  2. Kołodziej J, Khan SU, Wang L, Kisiel-Dorohinicki M, Madani SA et al (2014) Security, energy, and performance-aware resource allocation mechanisms for computational grids. Future Gener Comput Syst 31(1):77–92

    Article  Google Scholar 

  3. Reda NM, Tawfik A, Marzok MA, Khamis SM (2015) Sort-mid tasks scheduling algorithm in grid computing. J Adv Res 6(1):987–993

    Article  Google Scholar 

  4. Reda NM, Tawfik A, Marzok MA, Khamis SM (2015) Range-Suffrage algorithm for grid task scheduling. Int J Appl Phys Sci 1(2):42–50

    Article  Google Scholar 

  5. Maheswaran M, Ali S, Siegel HJ, Hensgen D, Freund RF (1999) Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. J Parallel Distrib Comput 59(2):107–131

    Article  Google Scholar 

  6. Anousha S, Ahmadi M (2013) An improved min-min task scheduling algorithm in grid computing. Lecture Notes in Computer Science, vol 786. Springer, Berlin, Heidelberg, pp 103–113

    Google Scholar 

  7. George Amalarethina DI, Vaaheedha Kfatheen S (2012) Max-Min average algorithm for scheduling tasks in grid computing systems. Int J Comput Sci Inf Technol 3(2):3659–3663

    Google Scholar 

  8. Chaturvedi AK, Sahu R (2011) new heuristic for scheduling of independent tasks in computational grid. Int J Grid Distrib Comput 4(3):25–36

    Google Scholar 

  9. Freund RF, Gherrity M et al (1998) Scheduling resources in multi-user heterogeneous computing environment with smart net. Proceedings of the 7th IEEE Heterogeneous Computing Workshop, IEEE Computer Society Washington, DC, USA, 1998

  10. Kumar MA, Srinivas TS, Pandey R (2021) Comparative study of job scheduling algorithms in grid computing, advances in power systems and energy management, Lecture Notes in Electrical Engineering, vol 690, pp 1–7.https://doi.org/10.1007/978-981-15-7504-4_1

  11. Abdulal W, Ramachandram S (2012) Reliability-aware scheduling based on a novel simulated annealing in grid. Proceedings of the 4th International Conference on Computational Intelligence and Communication Networks, Phuket, Thailand, 2012

  12. AlZubi AA (2017) Modified hierarchical method for task scheduling in grid systems. Int J Adv Comput Sci Appl 8(3):67–75

    Google Scholar 

  13. Entezari-Maleki R, Bagheri M, Mehri S, Movaghar A (2017) Performance aware scheduling considering resource availability in grid computing. Eng Comput 33(2):191–206

    Article  Google Scholar 

  14. Sheikh S, Nagaraju A (2020) Dynamic task scheduling with advance reservation of resources to minimize turnaround time for computational grid. Int J Inf Tecnol 12:625–633. https://doi.org/10.1007/s41870-020-00448-2

    Article  Google Scholar 

  15. Ananthi Lakshmi R, Rahul Ravichandran R (2019) Reputation based dead line scheduling in grid computing environment. Int J Adv Res Comput Commun Eng 8:261–265

    Article  Google Scholar 

  16. Naik KJ, Jagan A, Narayana NS (2015) A novel algorithm for fault tolerant job Scheduling and load balancing in grid computing environment. Proceedings of the 2015 International Conference on Green Computing and Internet of Things. https://doi.org/10.1109/ICGCIoT.2015.7380629

  17. Yousif A, Abdullah A, Nor S, Abdelaziz A (2011) Scheduling jobs on grid computing using firefly algorithm. J Theor Appl Inf Technol 33:155–164

    Google Scholar 

  18. SarathChandar AP, Priyesh V, Doreen Hephzibah Miriam D (2012) Grid scheduling using improved particle swarm optimization with digital pheromones. Int J Sci Eng Res 3(6):106–111

    Google Scholar 

  19. Rao CS, Babu DBR (2014) A fuzzy differential evolution algorithm for job scheduling on computational grids. Int J Computer Trends Technol 13(2):72–77

    Article  Google Scholar 

  20. Jiang YS, Chen WM (2015) Task scheduling for grid computing systems using a genetic algorithm. J Supercomput 71(4):1357–1377

    Article  Google Scholar 

  21. Chhabra OA (2016) Job scheduling using ant colony optimization in grid environment. Proceedings of the International Conference on Electrical Electronics and Optimization Techniques, Chennai, India, 2016

  22. Dibbur Byrappa S, Hegde SN, Rajan MA, Krishnappa HK (2018) A novel task scheduling scheme for computational grids - greedy approach. 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), Krakow, 2018, pp 1026–1033. https://doi.org/10.1109/AINA.2018.00149

  23. Mahato DP (2020) Reliability maximization of grid transaction processing system using Cuckoo search-ant colony optimization. ICDCN 2020: Proceedings of the 21st International Conference on Distributed Computing and Networking, pp 1–6

  24. Naghshnejad, Singhal M (2020) A hybrid scheduling platform: a runtime prediction reliability aware scheduling platform to improve HPC scheduling performance 76:122–149

  25. Sulaiman M, Halim Z, Lebbah M et al (2021) An evolutionary computing-based efficient hybrid task scheduling approach for heterogeneous computing environment. J Grid Comput 19(11):1–31. https://doi.org/10.1007/s10723-021-09552-4

    Article  Google Scholar 

  26. Izakian H, Abraham A, Snasel V (2009) Comparison of heuristics for scheduling independent tasks on heterogeneous distributed environments. Proceedings of the International Joint Conference on Computational Sciences and Optimization, Sanya, Hainan, China 2009

  27. Ritchie G, Levine J (2004) A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments. Proceedings of the 23rd Workshop of the UK Planning and Scheduling Special Interest Group, Glasgow

  28. https://github.com/dapurv5/distributedscheduling#readme

  29. Sharma G, Banga P (2013) Task aware switcher scheduling for batch mode mapping in computational grid environment. Int J Adv Res Comput Sci Softw Eng 3(6):1292–1299

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wael Zakaria.

Additional information

Publisher's Note

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

The original online version of this article was revised: In this article the title was incorrectly given.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khamis, S.M., Reda, N.M. & Zakaria, W. Combining Range-Suffrage and Sort-Mid Algorithms for Improving Grid Scheduling. J Supercomput 78, 3072–3090 (2022). https://doi.org/10.1007/s11227-021-03984-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03984-1

Keywords

Mathematics Subject Classification

Navigation