Advertisement

Engineering with Computers

, Volume 32, Issue 2, pp 173–188 | Cite as

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

  • Deepak Kumar Patel
  • Devashree Tripathy
  • Chitaranjan Tripathy
Original Article

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.

Keywords

Load balancing GridSim Gridlet Response time 

References

  1. 1.
    Berman F, Fox G, Hey AJ (2003) Grid computing: making the global infrastructure a reality. Wiley, New YorkCrossRefGoogle Scholar
  2. 2.
    Foster I, Kesselman C (eds) (1999) The grid: blueprint for a future computing infrastructure. Morgan Kaufmann Publishers, San FranciscoGoogle Scholar
  3. 3.
    Myer T (2003) Grid computing: conceptual flyover for developers. IBM’s Developers work Grid Library, IBM Corporation, New YorkGoogle Scholar
  4. 4.
    Rathore N, Channa I (2014) Load balancing and job migration techniques in grid: a survey of recent trends. Wirel Pers Commun 79:1–37CrossRefGoogle Scholar
  5. 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 77–82Google Scholar
  6. 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):1675–1686CrossRefGoogle Scholar
  7. 7.
    Hao Y, Liu G, Wen N (2012) An enhanced load balancing mechanism based on deadline control on GridSim. Future Gener Comput Syst 28:657–665CrossRefGoogle Scholar
  8. 8.
    Subrata R, Zomaya AY, Landfeldt B (2008) Game-theoretic approach for load balancing in computational grids. IEEE Trans Parallel Distrib Syst 19(1):66–76CrossRefzbMATHGoogle Scholar
  9. 9.
    Yagoubi B, Lilia HT, Maussa HS (2006) Load balancing in grid computing. Asian J Inf Technol 5(10):1095–1103Google Scholar
  10. 10.
    Murshed M, Buyya R, Abramson D (2001) GridSim: A toolkit for the modeling and simulation of global grids. Technical Report, Monash, CSSEGoogle Scholar
  11. 11.
    Qureshi K, Rehman A, Manuel P (2010) Enhanced GridSim architecture with load balancing. J Supercomput 57:1–11Google Scholar
  12. 12.
    Anand L, Ghose D, Mani V (1999) ELISA: an estimated load information scheduling algorithm for distributed computing systems. Comput Math Appl 37:57–85MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Subrata R, Zomaya AY, Landfeldt B (2007) Artificial life techniques for load balancing in computational grids. J Comput Syst Sci 73:1176–1190CrossRefzbMATHGoogle Scholar
  14. 14.
    Bharadwaj V, Ghose D, Robertazzi TG (2003) Divisible load theory: a new paradigm for load scheduling in distributed systems. Clust Comput 6:7–17CrossRefGoogle Scholar
  15. 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, PittsburghGoogle Scholar
  16. 16.
    Ludwig S, Moallem A (2011) Swarm intelligence approaches for grid load balancing. J Grid Comput 9:1–23CrossRefGoogle Scholar
  17. 17.
    Erdil D, Lewis M (2012) Dynamic grid load sharing with adaptive dissemination protocols. J Supercomput 59:1–28CrossRefGoogle Scholar
  18. 18.
    Subrata R, Zomaya AY, Landfeldt B (2008) Game-theoretic approach for load balancing in computational grids. IEEE Trans Parallel Distrib Syst 19(1):66–76CrossRefzbMATHGoogle Scholar
  19. 19.
    Zikos S, Karatza HD (2009) Communication cost effective scheduling policies of nonclairvoyant jobs with load balancing in a grid. J Syst Softw 82:2103–2116CrossRefGoogle Scholar
  20. 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 18–20Google Scholar
  21. 21.
    Balasangameshwara J, Raju N (2012) A hybrid policy for fault tolerant load balancing in grid computing environments. J Netw Comput Appl 35:412–422CrossRefGoogle Scholar
  22. 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 25:819–828CrossRefGoogle Scholar
  23. 23.
    Yan KQ, Wang SS, Wang SC, Chang CP (2009) Towards a hybrid load balancing policy in grid computing system. Expert Syst Appl 36:12054–12064CrossRefGoogle Scholar
  24. 24.
    Cao J, Spooner DP, Jarvis SA, Nudd GR (2005) Grid load balancing using intelligent agents. Future Gener Comput Syst 21:135–149CrossRefGoogle Scholar
  25. 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 27:430–439CrossRefGoogle Scholar
  26. 26.
    Zheng Q, Tham CK, Veeravalli B (2008) Dynamic load balancing and pricing in grid computing with communication delay. J Grid Comput 6:239–253CrossRefGoogle Scholar
  27. 27.
    Li K (2008) Optimal load distribution in nondedicated heterogeneous cluster and grid computing environments. J Syst Archit 54:111–123CrossRefGoogle Scholar
  28. 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 DiegoGoogle Scholar
  29. 29.
    Yagoubi B, Slimani Y (2006) Dynamic load balancing strategy for grid computing. World Acad Sci Eng Technol 13:90–95Google Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringVeer Surendra Sai University of TechnologyBurla, SambalpurIndia
  2. 2.CSIR-Central Electronics Engineering Research InstitutePilaniIndia

Personalised recommendations