CSP Formulation for Scheduling Independent Jobs in Cloud Computing

M'hamed Mataoui, Faouzi Sebbak, Kadda Beghdad Bey, Farid Benhammadi

2015

Abstract

This paper investigates the use of Constraint Satisfaction Problem formulation to schedule independent jobs in heterogeneous cloud environment. Our formulation provides a basis for computing an optimal Makespan using job and machine reordering heuristics based on Min-min algorithm result. The combination of these heuristics with the weighted constraints allows improving the efficiency of the tree search algorithm to schedule jobs with considerable space search reduction. The proposed CSP model is validated through simulation experiments against clusters of 10 virtual machines. The results demonstrate that our model is able to efficiently allocate resources for jobs with significant performance gains between 18% - 40% compared to the Min-Min heuristic results to optimize the Makespan.

References

  1. Abirami, S. and Ramanathan, S. (2012). Linear scheduling strategy for resource allocation in cloud environment. International Journal on Cloud Computing: Services and Architecture (IJCCSA), 2(1):9-17.
  2. Barbosa, J. and Moreira, B. (2009). Dynamic job scheduling on heterogeneous clusters. In Eighth International Symposium on Parallel and Distributed Computing, 2009. ISPDC'09., pages 3-10. IEEE.
  3. Chen, C.-Y. and Tseng, H.-Y. (2012). An exploration of the optimization of excutive scheduling in the cloud computing. In Advanced Information Networking and Applications Workshops (WAINA), 2012 26th International Conference on, pages 1316-1319. IEEE.
  4. Gouda, K., Radhika, T., and Akshatha, M. (2013). Priority based resource allocation model for cloud computing. International Journal of Science, Engineering and Technology Research (IJSETR), ISSN, 2(1):2278- 7798.
  5. Goudarzi, H. and Pedram, M. (2011). Maximizing profit in cloud computing system via resource allocation. In 31st International Conference on Distributed Computing Systems Workshops (ICDCSW), 2011, pages 1- 6. IEEE.
  6. Guo, L., Zhao, S., Shen, S., and Jiang, C. (2012). Task scheduling optimization in cloud computing based on heuristic algorithm. Journal of Networks, 7(3):547- 553.
  7. Habbas, Z., Krajecki, M., and Singer, D. (2005). Decomposition techniques for parallel resolution of constraint satisfaction problems in shared memory: a comparative study. International Journal of Computational Science and Engineering, 1(2):192-206.
  8. Han, H., Deyui, Q., Zheng, W., and Bin, F. (2013). A qos guided task scheduling model in cloud computing environment. In Fourth International Conference on Emerging Intelligent Data and Web Technologies (EIDWT), 2013, pages 72-76. IEEE.
  9. Ibarra, O. H. and Kim, C. E. (1977). Heuristic algorithms for scheduling independent tasks on nonidentical processors. Journal of the ACM (JACM), 24(2):280-289.
  10. Inomata, A., Morikawa, T., Ikebe, M., Okamoto, Y., Noguchi, S., Fujikawa, K., Sunahara, H., and Rahman, M. (2011). Proposal and evaluation of a dynamic resource allocation method based on the load of vms on iaas. In 4th IFIP International Conference on New Technologies, Mobility and Security (NTMS), 2011, pages 1-6. IEEE.
  11. Irugurala, S. and Chatrapati, K. S. (2013). Various scheduling algorithms for resource allocation in cloud computing. The International Journal Of Engineering And Science (IJES), 2(5):16-24.
  12. Katyal, M. and Mishra, A. (2014). Application of selective algorithm for effective resource provisioning in cloud computing environment. International Journal on Cloud Computing: Services and Architecture (IJCCSA),, 4(1):1-10.
  13. Krishnasamy, K. and Gomathi, B. (2013). Task scheduling algorithm based on hybrid particle swarm optimization in cloud computing environment. Journal of Theoretical & Applied Information Technology, 55(1):33- 38.
  14. Kundu, A., Banerjee, C., Guha, S. K., Mitra, A., Chakraborty, S., Pal, C., and Roy, R. (2010). Memory utilization in cloud computing using transparency. In 5th International Conference on Computer Sciences and Convergence Information Technology (ICCIT), 2010, pages 22-27. IEEE.
  15. Kuribayashi, S.-i. (2011). Optimal joint multiple resource allocation method for cloud computing environments. International Journal of Research & Reviews in Computer Science, 2(1).
  16. Li, J., Qiu, M., Niu, J.-W., Chen, Y., and Ming, Z. (2010). Adaptive resource allocation for preemptable jobs in cloud systems. In 10th International Conference on Intelligent Systems Design and Applications (ISDA), 2010, pages 31-36. IEEE.
  17. Minarolli, D. and Freisleben, B. (2011). Utility-based resource allocation for virtual machines in cloud computing. In IEEE Symposium on Computers and Communications (ISCC), 2011, pages 410-417. IEEE.
  18. Santos, C., Zhu, X., and Crowder, H. (2002). A mathematical optimization approach for resource allocation in large scale data centers. Technical Report HPL-2002- 64, HP Labs, March 2002.
  19. Silva, J. N., Veiga, L., and Ferreira, P. (2008). Heuristic for resources allocation on utility computing infrastructures. In Proceedings of the 6th international workshop on Middleware for grid computing , MGC 7808, pages 1-6. ACM.
  20. Xie, W.-j., Tang, Z., Yang, L., and LI, R.-f. (2012). Research on the virtual machine placement algorithm in cloud computing based on stochastic programming. Computer Engineering & Science, 5(5):95-100.
  21. Yuan, J.-B., Lee, Y.-C., Wu, W., Young, H.-C., and Liang, K.-H. (2011). Building an intelligent provisioning engine for iaas cloud computing services. In 13th AsiaPacific Network Operations and Management Symposium (APNOMS), 2011, pages 1-6. IEEE.
  22. Zhang, L., Zhuang, Y., and Zhu, W. (2013). Constraint programming based virtual cloud resources allocation model. International Journal of Hybrid Information Technology, 6(6):333-344.
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Paper Citation


in Harvard Style

Mataoui M., Sebbak F., Beghdad Bey K. and Benhammadi F. (2015). CSP Formulation for Scheduling Independent Jobs in Cloud Computing . In Proceedings of the 5th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-104-5, pages 105-112. DOI: 10.5220/0005438801050112


in Bibtex Style

@conference{closer15,
author={M'hamed Mataoui and Faouzi Sebbak and Kadda Beghdad Bey and Farid Benhammadi},
title={CSP Formulation for Scheduling Independent Jobs in Cloud Computing},
booktitle={Proceedings of the 5th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2015},
pages={105-112},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005438801050112},
isbn={978-989-758-104-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - CSP Formulation for Scheduling Independent Jobs in Cloud Computing
SN - 978-989-758-104-5
AU - Mataoui M.
AU - Sebbak F.
AU - Beghdad Bey K.
AU - Benhammadi F.
PY - 2015
SP - 105
EP - 112
DO - 10.5220/0005438801050112