Skip to main content
SpringerLink
Log in

An improved load-balancing mechanism based on deadline failure recovery on GridSim

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Grid computing has emerged a new field, distinguished from conventional distributed computing. It focuses on large-scale resource sharing, innovative applications and in some cases, high performance orientation. The Grid serves as a comprehensive and complete system for organizations by which the maximum utilization of resources is achieved. The load balancing is a process which involves the resource management and an effective load distribution among the resources. Therefore, it is considered to be very important in Grid systems. For a Grid, a dynamic, distributed load balancing scheme provides deadline control for tasks. Due to the condition of deadline failure, developing, deploying, and executing long running applications over the grid remains a challenge. So, deadline failure recovery is an essential factor for Grid computing. In this paper, we propose a dynamic distributed load-balancing technique called “Enhanced GridSim with Load balancing based on Deadline Failure Recovery” (EGDFR) for computational Grids with heterogeneous resources. The proposed algorithm EGDFR is an improved version of the existing EGDC in which we perform load balancing by providing a scheduling system which includes the mechanism of recovery from deadline failure of the Gridlets. Extensive simulation experiments are conducted to quantify the performance of the proposed load-balancing strategy on the GridSim platform. Experiments have shown that the proposed system can considerably improve Grid performance in terms of total execution time, percentage gain in execution time, average response time, resubmitted time and throughput. The proposed load-balancing technique gives 7 % better performance than EGDC in case of constant number of resources, whereas in case of constant number of Gridlets, it gives 11 % better performance than EGDC.

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
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Berman F, Fox G, Hey AJ (2003) Grid computing: making the global infrastructure a reality. Wiley, New York

    Book  Google Scholar 

  2. Foster I, Kesselman C (eds) (1999) The grid: blueprint for a future computing infrastructure. Morgan Kaufmann Publishers, San Francisco

    Google Scholar 

  3. Myer T (2003) Grid computing: conceptual flyover for developers. IBM’s Developers work Grid Library, IBM Corporation, New York

    Google Scholar 

  4. Rathore N, Channa I (2014) Load balancing and job migration techniques in grid: a survey of recent trends. Wirel Pers Commun 79:1–37

    Article  Google Scholar 

  5. Rathore N, Channa I (2011) A cogitative analysis of load balancing technique with job migration in grid environment. In: IEEE proceedings of the world congress on information and communication technology (WICT), pp 77–82

  6. Shah R, Veeravalli B, Misra M (2007) On the design of adaptive and decentralized load-balancing algorithms with load estimation for computational grid environments. IEEE Trans Parallel Distrib Syst 18(12):1675–1686

    Article  Google Scholar 

  7. Hao Y, Liu G, Wen N (2012) An enhanced load balancing mechanism based on deadline control on GridSim. Future Gener Comput Syst 28:657–665

    Article  Google Scholar 

  8. Subrata R, Zomaya AY, Landfeldt B (2008) Game-theoretic approach for load balancing in computational grids. IEEE Trans Parallel Distrib Syst 19(1):66–76

    Article  MATH  Google Scholar 

  9. Yagoubi B, Lilia HT, Maussa HS (2006) Load balancing in grid computing. Asian J Inf Technol 5(10):1095–1103

    Google Scholar 

  10. Murshed M, Buyya R, Abramson D (2001) GridSim: A toolkit for the modeling and simulation of global grids. Technical Report, Monash, CSSE

    Google Scholar 

  11. Qureshi K, Rehman A, Manuel P (2010) Enhanced GridSim architecture with load balancing. J Supercomput 57:1–11

    Google Scholar 

  12. Anand L, Ghose D, Mani V (1999) ELISA: an estimated load information scheduling algorithm for distributed computing systems. Comput Math Appl 37:57–85

    Article  MathSciNet  MATH  Google Scholar 

  13. Subrata R, Zomaya AY, Landfeldt B (2007) Artificial life techniques for load balancing in computational grids. J Comput Syst Sci 73:1176–1190

    Article  MATH  Google Scholar 

  14. Bharadwaj V, Ghose D, Robertazzi TG (2003) Divisible load theory: a new paradigm for load scheduling in distributed systems. Clust Comput 6:7–17

    Article  Google Scholar 

  15. Cao J (2004) Self-organizing agents for grid load balancing. In: Proceedings of the fifth IEEE/ACM international workshop on grid computing, GRID’04, Pittsburgh

  16. Ludwig S, Moallem A (2011) Swarm intelligence approaches for grid load balancing. J Grid Comput 9:1–23

    Article  Google Scholar 

  17. Erdil D, Lewis M (2012) Dynamic grid load sharing with adaptive dissemination protocols. J Supercomput 59:1–28

    Article  Google Scholar 

  18. Subrata R, Zomaya AY, Landfeldt B (2008) Game-theoretic approach for load balancing in computational grids. IEEE Trans Parallel Distrib Syst 19(1):66–76

    Article  MATH  Google Scholar 

  19. Zikos S, Karatza HD (2009) Communication cost effective scheduling policies of nonclairvoyant jobs with load balancing in a grid. J Syst Softw 82:2103–2116

    Article  Google Scholar 

  20. Fernandes de Mello R, Senger LJ, Yang LT (2006) A routing load balancing policy for grid computing environments. In: Proceedings of the 20th international conference on advanced information networking and applications, Aina’06, vol. 1, pp 18–20

  21. Balasangameshwara J, Raju N (2012) A hybrid policy for fault tolerant load balancing in grid computing environments. J Netw Comput Appl 35:412–422

    Article  Google Scholar 

  22. Li Y, Yang Y, Ma M, Jhou L (2009) A hybrid load balancing strategy of sequential tasks for grid computing environments. Future Gener Comput Syst 25:819–828

    Article  Google Scholar 

  23. Yan KQ, Wang SS, Wang SC, Chang CP (2009) Towards a hybrid load balancing policy in grid computing system. Expert Syst Appl 36:12054–12064

    Article  Google Scholar 

  24. Cao J, Spooner DP, Jarvis SA, Nudd GR (2005) Grid load balancing using intelligent agents. Future Gener Comput Syst 21:135–149

    Article  Google Scholar 

  25. Wu J, Xu X, Zhang P, Liu C (2011) A novel multi-agent reinforcement learning approach for job scheduling in grid computing. Future Gener Comput Syst 27:430–439

    Article  Google Scholar 

  26. Zheng Q, Tham CK, Veeravalli B (2008) Dynamic load balancing and pricing in grid computing with communication delay. J Grid Comput 6:239–253

    Article  Google Scholar 

  27. Li K (2008) Optimal load distribution in nondedicated heterogeneous cluster and grid computing environments. J Syst Archit 54:111–123

    Article  Google Scholar 

  28. Howell F, McNab R (1998) SimJava: a discrete event simulation package for Java with applications in computer systems modeling. In: Proceedings of the 1st international conference on web-based modelling and simulation, society for computer simulation, San Diego

  29. Yagoubi B, Slimani Y (2006) Dynamic load balancing strategy for grid computing. World Acad Sci Eng Technol 13:90–95

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, Odisha, India

    Deepak Kumar Patel & Chitaranjan Tripathy

  2. CSIR-Central Electronics Engineering Research Institute, Pilani, 333031, Rajasthan, India

    Devashree Tripathy

Authors
  1. Deepak Kumar Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Devashree Tripathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chitaranjan Tripathy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deepak Kumar Patel.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patel, D.K., Tripathy, D. & Tripathy, C. An improved load-balancing mechanism based on deadline failure recovery on GridSim. Engineering with Computers 32, 173–188 (2016). https://doi.org/10.1007/s00366-015-0409-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-015-0409-y

Keywords

Search

Navigation