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A hyper-heuristic approach for resource provisioning-based scheduling in grid environment

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Abstract

Grid computing being immensely based on the concept of resource sharing has always been closely associated with a lot many challenges. Growth of Resource provisioning-based scheduling in large-scale distributed environments like Grid computing brings in new requirement challenges that are not being considered in traditional distributed computing environments. Resources being the backbone of the system, their efficient management plays quite an important role in its execution environment. Many constraints such as heterogeneity and dynamic nature of resources need to be taken care as steps toward managing Grid resources efficiently. The most important challenge in Grids being the job–resource mapping as per the users’ requirement in the most secure way. The mapping of the jobs to appropriate resources for execution of the applications in Grid computing is found to be an NP-complete problem. Novel algorithm is required to schedule the jobs on the resources to provide reduced execution time, increased security and reliability. The main aim of this paper is to present an efficient strategy for secure scheduling of jobs on appropriate resources. A novel particle swarm optimization-based hyper-heuristic resource scheduling algorithm has been designed and used to schedule jobs effectively on available resources without violating any of the security norms. Performance of the proposed algorithm has also been evaluated through the GridSim toolkit. We have compared our resource scheduling algorithm with existing common heuristic-based scheduling algorithms experimentally. The results thus obtained have shown a better performance by our algorithm than the existing algorithms, in terms of giving more reduced cost and makespan of user’s application being submitted to the Grids.

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Notes

  1. More information about the real trace used can be obtained from the Grid Workload Archive at http://gwa.ewi.tudelft.nl/pmwiki/.

References

  1. Foster I, Kesselman C (2004) The Grid: blueprint for a future computing infrastructure. Morgan Kaufmann Publishers, USA

    Google Scholar 

  2. Aron R, Chana I (2010) Resource provisioning and scheduling in Grids: issues, challenges and future directions. In: Proceeding of IEEE International Conference on Computer and Communication Technology. MNNIT, Allahabad

  3. Aron R, Chana I (2011) Resource provisioning for Grid: a policy perspective. In: Proceeding of Springer International Conference on Contemporary Computing (IC31). JP University, Noida

  4. Khateeb AA, Abdullah R, Rashid AN (2009) Job type approach for deciding job scheduling in Grid computing systems. J Comput Sci 5(10):745–750

    Article  Google Scholar 

  5. Burke EK, Hyde M, Kendall G, Ochoa G, Ozcan E, Qu R (2009) Hyper-heuristics: a survey of the State of the Art. University of Nottingham, technical report

  6. Bhanu SMS, Gopalan NP (2008) A hyper-heuristic approach for efficient resource scheduling in Grid. Int J Comput Commun Control 3(3):249–258

    Google Scholar 

  7. Abraham A, Buyya R, Nath B (2000) Nature’s heuristics for scheduling jobs on computational Grids. In: The 8th IEEE Conference on Advanced Computing and Communications. Cochin, India

  8. Gonzalez JA, Serna M, Xhafa F (2007) A hyper-heuristic for scheduling independent jobs in computational Grids. In: International conference on software and data technologies, ICSOFT

  9. Garg S, Konugurthi P, Buyya R (2008) A linear programming driven genetic algorithm for meta-scheduling on utility Grids. In: Proceedings of the 16th International Conference on Advanced Computing and Communication (ADCOM 2008, IEEE Press, New York, USA). Chennai, India

  10. Garg SK, Buyya R, Siegel HJ (2010) Time and cost trade-off management for scheduling parallel applications on utility grids. Future Gener Comput Syst 26(8):1344–1355 (ISSN: 0167–739X, Elsevier Science, Amsterdam, The Netherlands)

    Article  Google Scholar 

  11. Braun TD, Siegel HJ, Beck N, Boloni LL, Maheswaran M, Reuther AI, Robertson JP et al (2001) A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J Parallel Distrib Comput 61(6):810837

    Article  Google Scholar 

  12. Jun L, Chunlin L, Qingqing L (2010) A research about independent tasks scheduling on tree-based grid computing platforms, 2nd International Workshop on Intelligent Systems and Applications. Institute of Computer Science. Wuhan University of Technology, Wuhan, pp 1–4

    Google Scholar 

  13. Gaoa Y, Rongb H, Huangc JZ (2005) Adaptive Grid job scheduling with genetic algorithms. J Future Gener Comput Syst 21(1)

  14. Xhafa F, Abraham A (2010) Computational models and heuristics methods for Grid scheduling problems. FGCS 26:608–621

    Article  Google Scholar 

  15. Kim S, Weissman JB (2004) A genetic algorithm based approach for scheduling decomposable data Grid applications. In: International Conference on Parallel Processing. pp 406–413

  16. Konugurthi PK, Ramakrishnan K, Buyya R (2007) A heuristic genetic algorithm based scheduler for clearing house grid broker, Technical Report, GRIDS-TR-2007-22. Grid Computing and Distributed Systems Laboratory. The University of Melbourne, Australia

    Google Scholar 

  17. Golconda K, Ozguner F (2004) A comparison of static QoS-based scheduling heuristics for a meta-task with multiple QoS dimensions in heterogeneous computing. Proceedings of 18th International Symposium on Parallel and Distributed Processing

  18. Chakhlevitch K, Cowling P (2008) Hyperheurictics: recent developments. In: Cotta C, Sevaux M, Sorensen K (eds) Adaptive and multilevel metaheuristics, studies in computational intelligence, vol 136. Springer, pp 3–29

  19. Dueck G (2002) New optimisation heuristics for the great deluge algorithm and the record-to-record travel. J Comput Phys 104:86–92 (Systems Magazine, pp 52–67)

    Article  Google Scholar 

  20. Martino VD (2004) Sub-optimal scheduling in a Grid using genetic algorithms. In: Parallel and nature-inspired computational paradigms and applications. Elsevier Science Publishers, pp 553–565

  21. Carretero J, Xhafa F (2006) Use of genetic algorithms for scheduling jobs in large scale Grid applications. Technol Econ Dev Econ 12(1):11–17

    Google Scholar 

  22. Cowling P, Kendall G, Han L (2002) An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In: Proceedings of the IEEE Congress on Evolutionary Computation. pp 1185–1190

  23. Aron R, Chana I (2012) Formal QoS policy based Grid resource provisioning framework. J Grid Comput

  24. Buyya R, Murshed M (2002) GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing, concurrency and computation: practice and experience (CCPE), vol 14. Wiley Press, New York, pp 1175–1220, ISSN: 1532–0626

  25. http://www.globus.org/toolkit/docs/5.2/5.2.0/gram5/key/

  26. Cowling P, Kendall G, Soubeiga E (2001) A hyper-heuristic approach to scheduling a sales summit, selected papers of proceedings of the 3rd International Conference on the Practice and Theory of Automated Timetabling, vol. 2079. Springer LNCS, pp 176–190

  27. Burke EK, Kendall G, Landa Silva JD O’Brien R, Soubeiga E (2005) An ant algorithm hyperheuristic for the project presentation scheduling problem, proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC). Edinburgh, UK, pp 2263–2270

  28. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. IV. pp 1942–1948

  29. Tao F, Zhao D, Hu Y, Zhou Z (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing Grid system. In: IEEE Transactions on industrial informatics, vol. 4, no. 4

  30. Gkoutioudi KZ, Karatza HD (2012) Multi-criteria job scheduling in Grid using an accelerated genetic algorithm. J Grid Comput. doi:10.1007/s10723-012-9210-y

  31. Kolodziej J, Xhafa F (2012) Integration of task abortion and security requirements in GA-based meta-heuristics for independent batch grid scheduling. In: Computers and mathematics with applications, Elsevier. doi:10.1016/j.camwa.2011.07.038, 63 350364

  32. Kolodziej J, Xhafa F (2011) Meeting security and user behaviour requirements in Grid scheduling. Simul Model Pract Theory 19:213–223. doi:10.1016/j.simpat.2010.06.007

    Article  Google Scholar 

  33. Menasce DA, Casalicchio E (2004) QoS in Grid computing. IEEE Internet Comput J 8(4)

  34. Song S, Hwang K, Kwok YK (2006) Risk-resilient heuristics and genetic algorithms for security-assured grid scheduling. IEEE Trans Comput 55:703–719

    Article  Google Scholar 

  35. Ali S, Siegel HJ, Maheswaran M, Hensgen D, Ali S (2000) Representing task and machine heterogeneities for heterogeneous computing systems. Tamkang J Sci Eng 3(3):195–207

    Google Scholar 

  36. Lublin U, Feitelson D (2003) The workload on parallel supercomputers: modeling the characteristics of rigid jobs. J Parallel Distrib Comput 63(11):1105–1122

    Article  MATH  Google Scholar 

  37. Aron R, Chana I (2013) Bacterial Foraging based Hyper-heuristic for Resource Scheduling in Grid Computing. Future Gener Comput Syst 29(3):751–762. doi:10.1016/j.future.2012.09.005

    Article  Google Scholar 

  38. Chen J (2010) Economic Grid resource scheduling based on utility optimization. IITSI, pp 522–525

  39. He X, Sun X, Laszewski G (2003) A QoS Guided Min-Min Heuristic for Grid Task Scheduling. J Comput Sci Technol 18(4):442–451

    Article  MATH  Google Scholar 

  40. Izakian H et al. (2009) A novel particle swarm optimization approach for grid job scheduling. Information Systems, Technology and Management. pp 100–109

  41. Abraham A, Liu H, Zhang W, Chang TG (2006) Scheduling jobs on computational Grids using fuzzy particle swarm algorithm. In: Proceedings of 10th International Conference on Knowledge-Based & Intelligent Information & Engineering Systems. England, pp 500–507

  42. Zhou Z, Deng W, Lu L (2009) A fuzzy reputation based ant algorithm for Grid scheduling, cso. International Joint Conference on Computational Sciences and Optimization, vol. 1. pp 102–104

  43. Zhao L, Ren Y, Li M, Sakurai K (2010) SPSE: a flexible QoS-based service scheduling algorithm for service-oriented Grid. In: 24th IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010. Atlanta, Georgia, pp 1–8

  44. Kolodiej J, Khan SU, Gelenbe E, Talbi EG (2013) Scalable optimization in grid, cloud, and intelligent network computing. Concurr Comput 25(12):1719–1721

    Article  Google Scholar 

  45. Liu Z, Qu W, Liu W, Li Z, Xu Y (2014) Resource preprocessing and optimal task scheduling in cloud computing environments. In: Practice and Experience, Concurrency and Computation

  46. Buyya R, Abramson D, Giddy J, Stockinger H (2002) Economic models for resource management and scheduling in grid computing. Concurr Comput 14(13–15):1507–1542

    Article  MATH  Google Scholar 

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Acknowledgments

We would like to thank all anonymous reviewers for their comments and suggestions for improving the paper. We would like to thank Parteek Gupta for helping in improving the language and expression of a preliminary version of this paper. We are also grateful to the Grid Workloads Archive group for making the Grid workload traces available. We also thank Dr. Dror Feitelson for maintaining the Parallel Workload Archive and all organizations and researchers who made their workload logs available.

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Correspondence to Rajni Aron.

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Aron, R., Chana, I. & Abraham, A. A hyper-heuristic approach for resource provisioning-based scheduling in grid environment. J Supercomput 71, 1427–1450 (2015). https://doi.org/10.1007/s11227-014-1373-9

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