A reliable, TOPSIS-based multi-criteria, and hierarchical load balancing method for computational grid

  • Aref M. AbdullahEmail author
  • Hesham A. Ali
  • Amira Y. Haikal


Load balancing is a very important and complex problem in computational grids. In load balancing, jobs should be effectively distributed among resources in order to minimize the average completion time and maximize the utilization of all resources even those with low reliabilities and capacities. However, using the less reliable and slow resources implies worse completion time, whereas always selecting the powerful and reliable resources undermines the utilization of other resources. So, it is essential to develop an efficient load balancing method which makes a good tradeoff between these criteria in a way that satisfies the quality of service of jobs and fairly distributes jobs between resources based on their reliabilities and capacities. This paper proposes an efficient multicriteria load balancing method using technique for order preference by similarity to ideal solution which treats load balancing as a multi criteria decision making problem. Also, an effective weighting mechanism is proposed, which adaptively adjusts the weights of the considered criteria according to the system’s current state and jobs’ characteristics. This mechanism can make an efficient tradeoff between the considered criteria and accurately reflect the importance of each one. By simulation, the proposed method was evaluated and compared with other approaches from the literature. In the range of examined parameters’ values, the simulation results show that proposed method minimizes the average completion time by 8.7–15.7%, increases the throughput ratio up to 15.8–19.4%, and maximizes the load balancing level by 7.68–20.1%.


Computational grid Grid scheduling Load balancing Fault tolerance Distributed system 


  1. 1.
    Abdullah, A.M., Ali, H.A., Haikal, A.Y.: Reliable and efficient hierarchical organization model for computational grid. J. Parallel Distrib. Comput. 104, 191–205 (2017)CrossRefGoogle Scholar
  2. 2.
    Balasangameshwara, J., Raju, N.: Performance-driven load balancing with a primary-backup approach for computational grids with low communication cost and replication cost. IEEE Trans. Comput. 62(5), 990–1003 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Banerjee, S., Kommareddy, C., Bhattacharjee, B.: Scalable peer finding on the internet. IEEE Glob. Telecommun. Conf. 3, 2205–2209 (2002)Google Scholar
  4. 4.
    Bansal, S., Hota, C.: Distributed scheduling on utility grids. Romanian J. Inf. 16(4), 373–392 (2013)Google Scholar
  5. 5.
    Behzadian, M., Khanmohammadi Otaghsara, S., Yazdani, M., Ignatius, J.: A State-of the-art survey of TOPSIS applications. Expert Syst. Appl. 39(17), 13051–13069 (2012)CrossRefGoogle Scholar
  6. 6.
    Bouguerra, M.S., Kondo, D., Martin, M.S., Trystram, D.: On the scheduling of checkpoints in desktop grids. In: 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), Newport Beach, CA, pp. 305–313 (2011)Google Scholar
  7. 7.
    Braun, T.D., Siegel, H.J., Beck, N., Boloni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D., Freund, R.F.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems (2001)Google Scholar
  8. 8.
    Czajkowski, K., Fitzgerald, S., Foster, I., Kesselman, C.: Grid information services for distributed resource sharing. In: 10th IEEE International Symposium on High-Performance Distributed Computing (HPDC-10), pp. 181–194. IEEE Press (2001)Google Scholar
  9. 9.
    Dagnew, S.A.: Optimization of periodic maintenance using condition monitoring techniques and operational data. PhD thesis, University of Stavanger, Norway (2012)Google Scholar
  10. 10.
    El-Sayed, G.A., Abdullah, A.M.: Mailbox-based non blocking minimum-process coordinated checkpointing with message logging for hierarchical computational grid (MNMCCP). In: 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 86–90 (2012)Google Scholar
  11. 11.
    El-Zoghdy, S.F.: A Two-level load balancing policy for grid computing. In: International Conference on Multimedia Computing and Systems (ICMCS), pp. 617– 622 (2012)Google Scholar
  12. 12.
    El-Zoghdy, S.F.: An intelligent AntNet-based algorithm for load balancing in grid computing. Int. J. Comput. Technol. 11(9), 2975–2986 (2013)CrossRefGoogle Scholar
  13. 13.
    Golmohammadi, R., Shahhoseini, H.S.: Load balancing in local computational grids within resource allocation process. Res. J. Appl. Sci. Eng. Technol. 4(21), 4546–4551 (2012)Google Scholar
  14. 14.
    Goswami, S., Das, A.: Resource prioritization technique in computational grid environment. In: Proceedings of the Second International Conference on Computer and Communication Technologies, Advances in Intelligent Systems and Computing, pp. 765–772 (2016)Google Scholar
  15. 15.
    Helmy, T., Al-jamimi, H., Ahmed, B., Loqman, H.: Fuzzy logic based scheme for load balancing in grid services. J. Softw. Eng. Appl. 5, 149–156 (2012)CrossRefGoogle Scholar
  16. 16.
    Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Application, vol. 186. Springer, New York (1981)zbMATHGoogle Scholar
  17. 17.
    Iosup, A., Jan, M., Sonmez, O., Epema, D.H.J.: On the dynamic resource availability in grids. In: 8th IEEE/ACM International Conference on Grid Computing, Austin, TX, pp. 26–33 (2007)Google Scholar
  18. 18.
    Javadi, B., Kondo, D., Vincent, J.M., Anderson, D.P.: Discovering statistical models of availability in large distributed systems: an empirical study of SETI@home. Measurement 22(11), 1896–1903 (2011)Google Scholar
  19. 19.
    Javadi, B., Kondo, D., Iosup, A., Epema, D.: The failure trace archive: enabling the comparison of failure measurements and models of distributed systems. J. Parallel Distrib. Comput. 73(8), 1208–1223 (2013)CrossRefGoogle Scholar
  20. 20.
    Kumar, D., Chitaranjan, P.: An improved approach for load balancing among heterogeneous resources in computational grids. Eng. Comput. 31(4), 825–839 (2014)Google Scholar
  21. 21.
    Li, K.: Optimal load distribution in nondedicated heterogeneous cluster and grid computing environments. J. Syst. Arch. 54, 111–123 (2008)CrossRefGoogle Scholar
  22. 22.
    Li, T., Ren, Y., Yu, D., Jin, S.: Resources-conscious asynchronous high-speed data transfer in multicore systems: design, optimizations, and evaluation. In: IEEE International Parallel and Distributed Processing Symposium, pp. 1097–1106 (2015)Google Scholar
  23. 23.
    Lu, K.: Decentralized load balancing in heterogeneous computational grids. PhD thesis, University of Sydney, Australia (2007)Google Scholar
  24. 24.
    Lu, K., Subrata, R., Zomaya, A.Y.: An efficient load balancing algorithm for heterogeneous grid systems considering desirability of grid sites. In: The 25th IEEE International Performance, Computing, and Communications Conference (IPCCC), pp. 311–320 (2006)Google Scholar
  25. 25.
    Lu, K., Subrata, R., Zomaya, A.Y.: Towards decentralized load balancing in a computational grid environment. In: GPC’06 Proceedings of the First International Conference on Advances in Grid and Pervasive Computing, vol. 3947, pp. 466–477 (2006)Google Scholar
  26. 26.
    Lu, K., Subrata, R., Zomaya, A.Y.: On the performance-driven load distribution for heterogeneous computational grids. J. Comput. Syst. Sci. 73, 1191–1206 (2007)CrossRefzbMATHGoogle Scholar
  27. 27.
    Mohanty, D.R., Mishra, S.K.: A data-driven approach for option pricing algorithm. In: Proceedings of the Second International Conference on Computer and Communication Technologies, Advances in Intelligent Systems and Computing, vol. 380, pp. 163–170 (2016)Google Scholar
  28. 28.
    Onder, E., Dag, S.: Combining analytical hierarchy process and TOPSIS approaches for supplier selection in a cable company. J. Bus. Econ. Finance 2(2), 56–74 (2013)Google Scholar
  29. 29.
    Patel, D.K., Tripathy, D., Tripathy, C.: An improved load-balancing mechanism based on deadline failure recovery on gridsim. Eng. Comput. 32(2), 173–188 (2016)CrossRefGoogle Scholar
  30. 30.
    Patel, D.K., Tripathy, D., Tripathy, C.: Survey of load balancing techniques for grid. J. Netw. Comput. Appl. 65(C), 103–119 (2016)Google Scholar
  31. 31.
    Pérez-Miguel, C., Mendiburu, A., Miguel-Alonso, J.: Competition-based failure-aware scheduling for high-throughput computing systems on peer-to-peer networks. Clust. Comput. 18(3), 1229–1249 (2015)CrossRefGoogle Scholar
  32. 32.
    Righi, R.D.R.: MigBSP: a new approach for processes rescheduling management on bulk synchronous parallel applications. PhD thesis, Universidade Federal Do Rio Grande Do Sul (2009)Google Scholar
  33. 33.
    Rood, B.: Grid resource availability prediction-based scheduling and task replication. PhD thesis, State University of New York at Binghamton, Binghamton (2011)Google Scholar
  34. 34.
    Santiago, A.J.S., Yuste, A.J., Expósito, J.E.M., Galán, S.G., Prado, R.P.D.: A multi-criteria meta-fuzzy-scheduler for independent tasks in grid computing. Comput. Inform. 30, 1201–1223 (2011)Google Scholar
  35. 35.
    Schroeder, B., Gibson, G.A.: A large-scale study of failures in high-performance computing systems. IEEE Trans. Dependable Secure Comput. 7(4), 337–350 (2010)CrossRefGoogle Scholar
  36. 36.
    Singh, S., Bawa, R.K.: Proactive fault tolerance algorithm for job scheduling in computational grid. Int. J. Grid Distrib. Comput. 9(3), 135–144 (2016)CrossRefGoogle Scholar
  37. 37.
    Snchez, J.M.: Global behavior modeling: a new approach to grid autonomic management. PhD thesis, Boston, MA (2010)Google Scholar
  38. 38.
    Soundarabai, P.B., A, S.R., Sahai, R.K., J, T., Venugopal, K.R., Patnaik, L.M.: Comparative study on load balancing techniques in distributed systems. Int. J. Inf. Technol. Knowl. Manag. 6(1), 53–60 (2012)Google Scholar
  39. 39.
    Subrata, R., Zomaya, A.Y., Landfeldt, B.: Artificial life techniques for load balancing in computational grids. Comput. Syst. Sci. 23(8), 1176–1190 (2007)CrossRefzbMATHGoogle Scholar
  40. 40.
    Suresh, P., Balasubramanie, P.: User demand aware grid scheduling model with hierarchical load balancing. Math. Probl. Eng. 2013, 8 (2013)CrossRefGoogle Scholar
  41. 41.
    Wolski, R., Spring, T., Hayes, J.: The network weather service: a distributed resource performance forecasting service for metacomputing. Future Gener. Comput. Syst. 15(5–6), 757–768 (1999)CrossRefGoogle Scholar
  42. 42.
    Yagoubi, B., Slimani, Y.: Dynamic load balancing strategy for grid computing. World Acad. Sci. Eng. Technol. 19, 90–95 (2006)Google Scholar
  43. 43.
    Yagoubi, B., Slimani, Y.: Task load balancing strategy for grid computing. J. Comput. Sci. 3(3), 186–194 (2007)CrossRefGoogle Scholar
  44. 44.
    Zhang, Y., Mandal, A., Koelbel, C., Cooper, K., Hill, C.: Combined fault tolerance and scheduling techniques for workflow applications on computational grids. In: 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, Shanghai, pp. 244–251 (2009)Google Scholar
  45. 45.
    Zhu, Y., Ni, L.M.: A survey on grid scheduling systems (2013)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Aref M. Abdullah
    • 1
    Email author
  • Hesham A. Ali
    • 2
  • Amira Y. Haikal
    • 2
  1. 1.Taiz UniversityTaizYemen
  2. 2.Mansoura UniversityAl-DaqhaliyaEgypt

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