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Link to original content: https://doi.org/10.1007/s00366-014-0364-z
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Variable threshold-based hierarchical load balancing technique in Grid

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Abstract

Load balancing is an important aspect of Grid resource scheduling. This paper attempts to address the issue of load balancing in a Grid, while maintaining the resource utilization and response time for dynamic and decentralized Grid environment. Here, to its optimum value, a hierarchical load balancing technique has been analysed based on variable threshold value. The load is divided into different categories, such as lightly loaded, under-lightly loaded, overloaded, and normally loaded. A threshold value, which can be found out using load deviation, is responsible for transferring the task and flow of workload information. In order to improve response time and to increase throughput of the Grid, a random policy has been introduced to reduce the resource allocation capacity. The proposed model has been rigorously examined over the GridSim simulator using various parameters, such as response time, resource allocation efficiency, etc. Experimental results prove the superiority of the proposed technique over existing techniques, such as without load balancing, load balancing in enhanced GridSim.

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References

  1. Rehman A, Qureshi K, Manuel P, Rashid H (2008) Resource topology aware GridSim: a step ahead. J Comput 19(2):3–22 (special issue on Grid and Cluster Computing)

    Google Scholar 

  2. Shah M, Qureshi K, Rasheed H (2010) Optimal job packing, a backfill scheduling optimization for a cluster of workstations. J Supercomput 54(3):381–399 (ISSN 0920-8542)

    Article  Google Scholar 

  3. Qureshi K, Hussain SS (2008) A comparative study of parallelization strategies for fractal image compression on a cluster of workstations. Int J Comput Methods 5(3):463–482

    Article  MATH  Google Scholar 

  4. Lin W, Shen W (2010) Tree-based task scheduling model and dynamic load-balancing algorithm for P2P computing. CIT 2903–2907

  5. Rathore NK, Chana I (2013) A sender initiate based hierarchical load balancing technique for grid using variable threshold value. In: International conference IEEE-ISPC JUIT, Waknaghat, H.P., 978-1-4673-6190

  6. Wang B, Shen Q (2011) ID management and allocation algorithm for P2P load balancing. Wuhan Univ J Nat Sci 16(4):293–300

    Article  MathSciNet  Google Scholar 

  7. Peng L, Xiao W (2011) A binary-tree based hierarchical load balancing algorithm in structured peer-to-peer systems. JCIT 6(4)

  8. Ludwig SA, Moallem A (2011) Swarm intelligence approaches for grid load balancing. J Grid Computing 9(3):279–301

  9. Yagoubi B, Slimani Y (2007) Task load balancing strategy for grid computing. J Comput Sci 3(3):186–194 (ISSN 1546-9239, Science Publications-2007)

    Article  Google Scholar 

  10. Erdil D, Lewis M (2012) Dynamic Grid load sharing with adaptive dissemination protocols. J Supercomput 59(3):1139–1166

  11. Long Q, Lin J, Sun Z (2011) Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations. Tongji University, Shanghai, China simulation modelling practice and theory. Science Direct Elsevier, pp 1021–1034

  12. Zheng G, Bhatele A, Meneses E, Kale LV (2011) Periodic hierarchical load balancing for large supercomputers. IJHPCA 25(4):371–385

  13. Nandagopal M, Uthariaraj RV (2010) Hierarchical status information exchange scheduling and load balancing for computational grid environments. Int J Comput Sci Netw Secur 10(2):177–185

    Google Scholar 

  14. De Grande RE, Boukerche A (2011) Dynamic balancing of communication and computation load for HLA-based simulations on large-scale distributed systems. J Parallel Distrib Comput 71(1):40–52

    Article  Google Scholar 

  15. Sharma D, Saxena AB (2011) Framework to solve load  balancing problem in  heterogeneous web servers. IJCSES 2(1)

  16. Nandagopal M, Gokulnath K, Rhymend Uthariaraj V (2010) Sender initiated decentralized dynamic load balancing for multi cluster computational grid environment. In: Proceedings of A2CWiC ’10. Art No. 63, ISBN: 978-1-4503-0194-7

  17. Zheng G, Bhatele A, Meneses E, Kalé LV (2011) Periodic hierarchical load balancing for large supercomputers. IJHPCA 25(4):371–385

    Google Scholar 

  18. Rathore N, Chana I (2011) A cognitive analysis of load balancing and job migration technique in grid. In: WICT-IEEE Conference, University of Mumbai, pp 77–82

  19. El-Zoghdy SF (2012) A Hierarchical load balancing policy for grid computing environment. IJCNIS 4(5):1–12

    Google Scholar 

  20. Qureshi K, Rehman A (2011) Enhanced GridSim architecture with load balancing. J Supercomput 57:265–275

    Article  Google Scholar 

  21. Rings T, Caryer G, Gallop J, Grabowski J, Kovacikova T, Schulz S, Stokes-Rees I (2009) Grid and cloud computing: opportunities for integration with the next generation network. J Grid Computing 7(3):375–393

    Article  Google Scholar 

  22. Hao Y, Liu G, Wen N (2012) An enhanced load balancing mechanism based on deadline control on GridSim. Future Gener Comp Syst 28(4):657–665

  23. Suresh P, Balasubramanie P (2013) User demand aware grid scheduling model with hierarchical load balancing. Math Prob Engg 2013, Art No. 439362. doi:10.1155/2013/439362

  24. Chen H, Wang F, Helian N, Akanmu G (2013) User-priority guided min-min scheduling algorithm for load balancing in cloud computing. National Conference on Parallel Computing Technologies (PARCOMPTECH)

  25. Abdi S, Mohamadi S (2010) The impact of data replication on job scheduling performance in hierarchical data grid. Int J Appl Graph Theory Wirel Ad hoc Netw Sens Netw 2(3):15–21

    Article  Google Scholar 

  26. Kamarunisha M, Ranichandra S, Rajagopal TKP (2011) Recitation of load balancing algorithms in grid computing environment using policies and strategies - an approach. IJSER 2(3)

  27. 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 

  28. Mandalapu S (2005) Dynamic load balancing design and modeling in MPIAB: general architecture. PDPTA, pp 710–716

Download references

Acknowledgments

This work has been carried out at Distributed and Grid Computing Lab, Department of Computer Science and Engineering at Jaypee University of Engineering and Technology, Guna, M.P. The authors acknowledge the support provided. We would like to thank all anonymous reviewers for their comments and suggestions for improving the paper. We would like to thank Mr. Punit Pandey and Dr. Ashutosh Singh for helping in improving the language and expression of a preliminary version of this paper.

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Correspondence to Neeraj Rathore.

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Rathore, N., Chana, I. Variable threshold-based hierarchical load balancing technique in Grid. Engineering with Computers 31, 597–615 (2015). https://doi.org/10.1007/s00366-014-0364-z

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