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A Hybrid Algorithm for DAG Application Scheduling on Computational Grids

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Mobile, Secure, and Programmable Networking (MSPN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 9395))

Abstract

In the late three decades, grid computing has emerged as a new field providing a high computing performance to solve larger scale computational demands. Because Directed Acyclic Graph (DAG) application scheduling in a distributed environment is a NP-Complete problem, meta-heuristics are introduced to solve this issue. In this paper, we propose to hybridize two well-known heuristics. The first one is the Heterogeneous Earliest Finish Time (HEFT) heuristic which determines a static scheduling for a DAG in a heterogeneous environment. The second one is Particle Swarm Optimization (PSO) which is a stochastic meta-heuristic used to solve optimization problems. This hybridization aims to minimize the makespan (i.e., overall completion time) of all the tasks within the DAG. The experimental results that have been conducted under hybridization show that this approach improves the scheduling in terms of completion time compared to existing algorithms such as HEFT.

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References

  1. Cafaro, M., Aloisio, G.: Grids, Clouds, and Virtualization. 1st edn., Spring (2011). ISBN 978-0-85729-049-6

    Google Scholar 

  2. Dong, F., Akl, S.G.: Scheduling Algorithms for Grid Computing: State of the Art and Open Problems. Technical report No. 2006-504. School of Computing, Queen’s University, Kingston, Ontario

    Google Scholar 

  3. Casavant, T., Kuhl, J.: A taxonomie of scheduling in general-purpose distributed computing systems. IEEE Trans. Softw. Eng. 14(2), 141–154 (1988)

    Article  Google Scholar 

  4. Braun, R., Siegel, H., Beck, N., Boloni, L., Maheswaran, M., Reuther, A., Robertson, J., Theys, M., Yao, B., Hensgen, D., Freund, R.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61(6), 810–837 (2001)

    Article  MATH  Google Scholar 

  5. Kwok, Y.K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. 31(4), 406–471 (1999)

    Article  Google Scholar 

  6. Yu, J., Buyya, R., Ramamohanarao, K.: Workflow scheduling algorithms for grid computing. In: Xhafa, F., Abraham, A. (eds.) Metaheuristics for Scheduling in Distributed Computing Environments. Studies in Computational Intelligence, vol. 146, pp. 173–214. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  7. Topcuoglu, H., Hariri, S., Wu, M.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)

    Article  Google Scholar 

  8. Radulescu, A., van Gemund, A.J.C.: On the complexity of list scheduling algorithms for distributed-memory systems. In: Technical report No. 1-68340-44(1999)02, January 1999

    Google Scholar 

  9. Kwok, Y., Ahmad, I.: Dynamic critical-path scheduling: an effective technique for allocating task graphs to muliprocessors. IEEE Trans. Parallel Distrib. Syst. 7(5), 506–521 (1996)

    Article  Google Scholar 

  10. Sih, G.C., Lee, E.A.: A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures. IEEE Trans. Parallel Distrib. Syst. 4(2), 75–87 (1993)

    Article  Google Scholar 

  11. Ma, T., Buyya, R.: Critical-path and priority based algorithms for scheduling workflows with parameter sweep tasks on global grids. In: IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2005) (2005)

    Google Scholar 

  12. Yang, T., Gerasoulis, A.: DSC: scheduling parallel tasks on an unbounded number of processors. IEEE Trans. Parallel Distrib. Syst. 5(9), 951–967 (1994)

    Article  Google Scholar 

  13. Liou, J., Palis, M.A.: An efficient clustering heuristic for scheduling DAGs on multiprocessors. In: Proceedings of the Symposium Parallel and Distributed Processing (1996)

    Google Scholar 

  14. Boeres, C., Filho, J.V., Rebello, V.E.F: A cluster-based strategy for scheduling task on heterogeneous processors. In: IEEE Symposium on Computer Architecture and High Performance Computing, pp. 214–221, October 2004

    Google Scholar 

  15. Kruatrachue, B., Lewis, T.: Grain size determination for parallel processing. IEEE Softw. 5, 23–32 (1988)

    Article  Google Scholar 

  16. Ahmad, I., Kwok, Y.-K.: A new approach to scheduling parallel programs using task duplication. In: IEEE International Conference on Parallel Processing, vol. 2 (1994)

    Google Scholar 

  17. Darbha, S., Agrawal, D.P.: Optimal scheduling algorithm for distributed-memory machines. IEEE Trans. Parallel Distrib. Syst. 9(1), 87–95 (1998)

    Article  Google Scholar 

  18. Chung, Y.-C., Ranka, S.: Application and performance analysis of a compile-time optimization approach for list scheduling algorithms on distributed-memory multiprocessors. In: Proceedings of the Supercomputing, pp. 512–521 (1992)

    Google Scholar 

  19. Bajaj, R., Agrawal, D.P.: Improving scheduling of tasks in a heterogeneous environment. IEEE Trans. Parallel Distrib. Syst. 15(2), 107–118 (2004)

    Article  Google Scholar 

  20. Wang, L., Siegel, H.J., Roychowdhury, V.P., Maciejewski, A.A.: Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach. J. Parallel Distrib. Comput. 47(1), 8–22 (1997)

    Article  Google Scholar 

  21. Martino, V.D., Mililotti, M.: Sub optimal scheduling in a grid using genetic algorithms. Parallel Comput. 30, 553–565 (2004)

    Article  Google Scholar 

  22. Gao, Y., Rong, H., Huang, J.Z.: Adaptive grid job scheduling with genetic algorithms. Future Gener. Comput. Syst. 2, 151–161 (2005)

    Article  Google Scholar 

  23. Aggarwal, M., Kent, R.D., Ngom, A.: Genetic algorithm based scheduler for computational grids. In: Proceedings of the 19th Annual International Symposium on High Performance Computing Systems and Applications (HPCS 2005), May 2005

    Google Scholar 

  24. Song, S., Kwok, Y., Hwang, K.: Security-driven heuristics and a fast genetic algorithm for trusted grid job scheduling. In: Proceedings of 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2005), April 2005

    Google Scholar 

  25. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ (1995 in press)

    Google Scholar 

  26. Zhang, L., Chen, Y., Sun, R., Jing, S., Yang, B.: A task scheduling algorithm based on PSO for grid computing. Int. J. Comput. Intell. Res. 4(1), 37–43 (2008)

    Article  Google Scholar 

  27. Liu, H., Abraham, A., Hassanien, A.E.: Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm. Future Gener. Comput. Syst. 26, 1336–1343 (2010)

    Article  Google Scholar 

  28. Izakian, H., Ladani, B.T., Abraham, A., Snasel, V.: A discrete particle swarm optimization approach for grid job scheduling. Int. J. Innovative Comput. Inf. Control 6(9), 4219–4233 (2010)

    Google Scholar 

  29. Zhang, Y.-Y., Inoguchi, Y., Shen, H.: A dynamic task scheduling algorithm for grid computing system. In: Cao, J., Yang, L.T., Guo, M., Lau, F. (eds.) ISPA 2004. LNCS, vol. 3358, pp. 578–583. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  30. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 5 (1997)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LARI Laboratory, UMMTO, Tizi Ouzou, Algeria

    Lyes Bouali

  2. Compute Science Department, UMMTO, Tizi Ouzou, Algeria

    Karima Oukfif

  3. LRPE Laboratory, USTHB, Bab Ezzouar, Algeria

    Karima Oukfif & Fatima Oulebsir-Boumghar

  4. CEDRIC Laboratory, CNAM, Paris, France

    Samia Bouzefrane

Authors
  1. Lyes Bouali
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  2. Karima Oukfif
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  3. Samia Bouzefrane
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  4. Fatima Oulebsir-Boumghar
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Corresponding author

Correspondence to Samia Bouzefrane .

Editor information

Editors and Affiliations

  1. CNAM/CEDRIC, Paris, France

    Selma Boumerdassi

  2. CNAM/CEDRIC, Paris, France

    Samia Bouzefrane

  3. Institut Mines-Télécom -Télécom SudParis, Evry, France

    Éric Renault

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© 2015 Springer International Publishing Switzerland

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Bouali, L., Oukfif, K., Bouzefrane, S., Oulebsir-Boumghar, F. (2015). A Hybrid Algorithm for DAG Application Scheduling on Computational Grids. In: Boumerdassi, S., Bouzefrane, S., Renault, É. (eds) Mobile, Secure, and Programmable Networking. MSPN 2015. Lecture Notes in Computer Science(), vol 9395. Springer, Cham. https://doi.org/10.1007/978-3-319-25744-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-25744-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25743-3

  • Online ISBN: 978-3-319-25744-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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