Stochastic simulator for priority based task in grid environment

  • Sunita Rani
  • P. K. Suri
Original Research


Among the different types of distributed computing, grid computing is that type of computing which includes huge collection of resources and applications, which may be at the same or different locations owned by same or different governments. Availability of right resource at right time with suitable task is managed by scheduling process. Task scheduling goal in grid computing is to attain better throughput and to match the requirement of application with the available set of resources. With the increase in the size of task and grid, complexity of scheduling problem increases and it becomes a NP-complete problem to be solved. This has given birth to a new research issue. In this paper authors have given a solution to the scheduling problem by proposing a probability based scheduling approach. To represent the scheduling scenario, authors have used the concept of directed acyclic graph. Proposed approach not only handles the tasks on the basis of their priority but also handle them in parallel. Gridsim simulator has been used to compare and test the proposed approach with other existing approaches found in the literature. Parameters like makespan, cost, through put and percentage of load balancing has been considered to check the performance of probabilistic scheduling approach.


Grid computing Priority based task Probabilistic scheduling Gridsim 


  1. 1.
    Christodoulopoulos K, Sourlas V, Mpakolas I, Varvarigos E (2009) A comparison of centralized and distributed meta-scheduling architectures for computation and communication tasks in grid networks. Comput Commun 32(8):1172–1184. CrossRefGoogle Scholar
  2. 2.
    Foster I, Kesselman C (1999) The grid: blueprint for a new computing infrastructure. Morgan Kaufmann Publishers Inc., San Francisco [ISBN: 1-55860-475-8]Google Scholar
  3. 3.
    Wieczorek M, Hoheisel A, Prodan R (2009) Towards a general model of the multi-criteria workflow scheduling on the grid. Future Gener Comput Syst 25(3):237–256. CrossRefGoogle Scholar
  4. 4.
    Gary MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. W.H. Freeman and Co., San Francisco [ISBN: 0716710447]Google Scholar
  5. 5.
    Topcuoglu H, Hariri S, Wu M-Y (2002) Performance-effective and low complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274. CrossRefGoogle Scholar
  6. 6.
    Tang X, Li K, Padua D (2009) Communication contention in APN list scheduling algorithm. Sci China Ser F 52(1):59–69. MathSciNetzbMATHGoogle Scholar
  7. 7.
    Tang X, Li K, Liao G, Li R (2010) List scheduling with duplication for heterogeneous computing systems. J Parallel Distrib Comput 70(4):323–329. CrossRefzbMATHGoogle Scholar
  8. 8.
    Tang X, Li K, Li R, Veeravalli B (2010) Reliability-aware scheduling strategy for heterogeneous distributed computing systems. J Parallel Distrib Comput 70(9):941–952. CrossRefzbMATHGoogle Scholar
  9. 9.
    Sih GC, Lee EA (1993) A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures. IEEE Trans Parallel Distrib Syst 4(2):175–187. CrossRefGoogle Scholar
  10. 10.
    El-Rewini H, Lewis TG (1990) Scheduling parallel program tasks onto arbitrary target machines. J Parallel Distrib Comput 9(2):138–153. CrossRefzbMATHGoogle Scholar
  11. 11.
    Iverson MA, Ozuner F, Follen GJ (1995) Parallelizing existing applications in a distributed heterogeneous environment. In: Proceedings of Heterogeneous Computing Workshop. p 93–100Google Scholar
  12. 12.
    Kwok YK, Ahmad I (1996) Dynamic critical-path scheduling: an effective technique for allocating task graphs onto multiprocessors. IEEE Trans Parallel Distrib Syst 7(5):506–521. CrossRefGoogle Scholar
  13. 13.
    Qiu M, Sha E (2009) Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems. ACM Trans Des Autom Electr Syst 14(2):1–25. CrossRefGoogle Scholar
  14. 14.
    Skutella M, Uetz M (2005) Stochastic machine scheduling with precedence constraints. SIAM J Comput 34(4):788–802. MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Megow N, Uetz M, Vredeveld T (2006) Models and algorithms for stochastic online scheduling. Math Op Res 31(3):513–525. MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Papadimitriou CH, Tsitsiklis JN (1987) On stochastic scheduling with in-tree precedence constraints. SIAM J Comput 16(1):1–6. MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Buyya R, Murshed M (2002) Gridsim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurr Comput 14:1175–1220. CrossRefzbMATHGoogle Scholar
  18. 18.
    Buyya R, Abramson D, Giddy J, Stockinger H (2002) Economic models for resource management and scheduling in grid computing. Concurr Comput 14:1507–1542. CrossRefzbMATHGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  1. 1.Department of CSE and ITB.P.S.M.V.SonepatIndia
  2. 2.Department of Computer Science and ApplicationsKurukshetra UniversityKurukshetraIndia

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