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A Comprehensive Survey on Computational Grid Resource Management

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Proceeding of the Second International Conference on Microelectronics, Computing & Communication Systems (MCCS 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 476))

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Abstract

The paper attempts to give a complete report on different methods of resource management in grid computing. The extensive usage of internet applications and its popularity has driven an ongoing demand of increased bandwidth and high computational power. Resource management is a challenging task in grid environment as the workload is high and quick responses to the user’s query are necessary in real time. The aim of this paper is to collect various algorithms used in grid scheduling at one place so that it will help the new researchers in their course of work. So, proper resource scheduling becomes extremely important not only because resources are heterogeneous in nature, but their availability also changes with time in a grid environment. This paper will cover most of the scheduling algorithms that can be useful to any researcher and will provide substantial help to his research work.

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References

  1. Foster, I., Kesselmen, C., Tuecke, S. (2001). The Anatomy of the Grid: Enabling Scalable Virtual Organisations. International Journal of High Performance Computing Applications, pp. 200–222.

    Google Scholar 

  2. Foster, I., Kesselmen, I. (1999). The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann Publishers, pp. 1–593.

    Google Scholar 

  3. Nagariya, S., Mishra, M. (2013). Resource Scheduling in grid computing: A Survey. International Journal of Advanced Research in Computer Science and software engineering, 3(10), 735–739.

    Google Scholar 

  4. Buyya, R., Abramson, D., Giddy, J. (2000). Grid Resource Management, Scheduling and Computational Economy. In Proc. Of the 2nd International Workshop on Global and Cluster Computing, pp. 1–2.

    Google Scholar 

  5. Jiang, H., Ni, T. (2009). PB-FCFS–A Task Scheduling Algorithm Based on FCFS and Backfilling Strategy for Grid Computing, In Pervasive Computing (JCPC), pp. 507–510.

    Google Scholar 

  6. Alharbi, F. (2012). Simple Scheduling Algorithm with Load Balancing for Grid Computing. Asian Transactions on Computers, 2 (2), 8–15.

    Google Scholar 

  7. Lokhande, S.F., Chavhan S.D., Jadhao, S.R. (2015). Grid Computing Scheduling Jobs Based on Priority Using Backfilling. International Journal of Electrical, Electronics & Computer Science, Engineering, pp. 68–72.

    Google Scholar 

  8. Ghazipour, F., Mirabedini, S.J., Harounabadi, A. (2016). Proposing a new Job Scheduling Algorithm in Grid Environment Using a Combination of Ant Colony Optimization Algorithm (ACO) and Suffrage. International Journal of Computer Applications Technology and Research, 5 (1), 20–25.

    Google Scholar 

  9. Joshua, R., Raj, S., Vasudevan, V. (2011). Grid Scheduling with Smart Genetic algorithm. International Journal of Grid Computing and Multi Agent Systems, 2(1), 1–10.

    Google Scholar 

  10. Carretero, J., Xhafa, F. (2007). Genetic Algorithm based schedulers for Grid Computing Systems. International Journal of Innovative Computing, Information and Control, 3(6), 1–19.

    Google Scholar 

  11. Wei, Z., Yang-Ping, B. (2012). An Adaptive Genetic Algorithm for the Grid Scheduling Problem. In 24th Chinese Control and Decision Conference, pp. 730–734.

    Google Scholar 

  12. Russell, S. J., & Norvig, P. (2004). Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  13. Fidanova, S. (2006). Simulated Annealing for Grid Scheduling Problem. In: Modern Computing. In IEEE John Vincent Atanasoff International Symposium, pp. 41–45.

    Google Scholar 

  14. Dell’Amico, M., Trubian, M. (1993). Applying Tabu Search to a job-shop scheduling problem. In Annals of Operational Research, 41 (3), 231–252.

    Google Scholar 

  15. Krishnamoorthy, N., Asokan, R. (2014). Optimal Resource Selection to promote Grid scheduling using Hill Climbing Algorithm. International Journal of Computer Science and telecommunications, 5 (2), 14–19.

    Google Scholar 

  16. Oshin, Chhabra, A. (2016). Job Scheduling using Ant Colony Optimization in Grid environment. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), IEEE, pp. 2845–2850.

    Google Scholar 

  17. Mathiyalagan, P., Dhepthie, U.R., Sivanandam, S.N. (2010). Grid Scheduling using enhanced ant colony algorithm. ICTACT journal on soft computing, Volume 2, 85–87.

    Google Scholar 

  18. Ruhana, K., Mahamud, K. (2010). Ant colony algorithm for job scheduling in grid computing. In: Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, pp. 40–45.

    Google Scholar 

  19. Wei, L., Zhang, X., Li, Y., Li, Y. (2012). An Improved Ant Algorithm for Grid Task Scheduling Strategy, In International Conference on Applied Physics and Industrial Engineering, Volume 24, 1974–1981.

    Google Scholar 

  20. Blum, C. (2005). Ant colony optimization: Introduction and recent trends. In Physics of Life Reviews, Elsevier, pp. 353–373.

    Google Scholar 

  21. Alyaseri, S., Ruhana, K., Mahamud, K. (2013). Bee foraging behavior techniques for Grid Scheduling. International Referred Journal of Engineering and Science, 2 (4), 39–45.

    Google Scholar 

  22. Sha, D.Y., Lin, H.H. (2010). A multi-objective PSO for job-shop scheduling problems. Expert System with Applications, 37 (2), 1065–1070.

    Google Scholar 

  23. Teodorovic, D. (2009). Bee Colony Optimization. In Innovations in Swarm intelligence 248, 39–60.

    Google Scholar 

  24. Qureshi, M.B., Dehnavi M.M., Alla, N.M., Qureshi M.S., Hussain H., Rentifis I., Tziritas N., Loukopoulos T., Khan S.U., Xu C-Z., Zomaya A Y. (2014).Survey on Grid Resource Allocation Mechanisms. Journal of Grid Computing, 12 (2), 399–441.

    Google Scholar 

  25. Kaladevi, A.C, Srinath, M.V, Prabhakar, A. (2013). Reserved Bee Colony Optimization Based Grid Scheduling. In International Conference on Computer Communication and Informatics, pp. 1–6.

    Google Scholar 

  26. Chang, R.S., Lin, C.Y, Lin, C.F. (2012). An Adaptive Scoring Job Scheduling algorithm for grid computing. In Information Sciences, Elsevier, 207, 79–89.

    Google Scholar 

  27. Wang, Q., Gao, Y., Liu, P. (2006). Hill Climbing-Based Decentralized Job Scheduling on Computational Grids. In Proc. of the First International Multi-Symp. on Computer and Computational Sciences, IEEE, pp. 705–708.

    Google Scholar 

  28. Kokilavani, T., Amalarethinam, D.I.G. (2012). Memory Constrained ant colony system for task scheduling in grid computing. International Journal of Grid Computing & Applications, 3(3), 11–20.

    Google Scholar 

  29. Kumar, E.S., Sumanthi, A., Zubar, H.A. (2015). A hybrid Ant Colony Optimization algorithm for job scheduling in Computational grids. Journal of Scientific and Industrial Research, 74(7), 377–380.

    Google Scholar 

  30. Xhafa, F., Kołodziej, J., Barolli, L., Fundo, A. (2011). A GA + TS Hybrid Algorithm for Independent Batch Scheduling in Computational Grids. In: International Conference on Network-Based Information Systems, pp. 229–235.

    Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Ankita & Sudip Kumar Sahana

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  1. Ankita
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  2. Sudip Kumar Sahana
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Correspondence to Ankita .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

    Vijay Nath

  2. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

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Ankita, Sahana, S.K. (2019). A Comprehensive Survey on Computational Grid Resource Management. In: Nath, V., Mandal, J. (eds) Proceeding of the Second International Conference on Microelectronics, Computing & Communication Systems (MCCS 2017). Lecture Notes in Electrical Engineering, vol 476. Springer, Singapore. https://doi.org/10.1007/978-981-10-8234-4_10

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  • DOI: https://doi.org/10.1007/978-981-10-8234-4_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8233-7

  • Online ISBN: 978-981-10-8234-4

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