Fuzzy-Based Simple and Proficient Resource Allocation Technique for Dynamic Grid Resources

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Poonguzhali M.
Shanmugavel S

Abstract

In the grid environment, not only the submitted jobs are dynamic but also the resources are found altering dynamically. Because of the dynamic nature, efficient utilization of the grid resources remains a challenging issue. A proficient resource allocation technique is a pre-requisite to face the aforesaid grid issue. In this paper, we propose a simple and proficient fuzzy-based resource allocation technique, which effectively allocates the dynamic grid resources to the submitted jobs. The proposed technique is constituted by three different stages, namely, Classification of grid resources, Generation of fuzzy rules and Resource allocation based on the fuzzy rules. In the first stage, grid resources are classified into three categories based on their dwelling time. In the second stage, fuzzy rules are generated so as to decide whether a particular resource can be allocated to the demanded job or not. In the third and final stage of the technique, the resources are allocated to the submitted jobs based on the generated fuzzy rules. Eventually, the proposed technique is evaluated by means of three performance measures, namely, 1) Utilization, 2) Failure rate and 3) Makespan. By determining the measurements for the allocated resource to the submitted jobs, the performance of the proposed technique can be understood.

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How to Cite
Poonguzhali M., & Shanmugavel S. (2011). Fuzzy-Based Simple and Proficient Resource Allocation Technique for Dynamic Grid Resources. International Journal of Next-Generation Computing, 2(1), 58–74. https://doi.org/10.47164/ijngc.v2i1.107

References

  1. J. BLYTHE, E. DEELMAN, Y. GIL,C. KESSELMAN ,A. AGARWAL G. MEHTA and K. VAHI 2003. The Role of Planning in Grid Computing. 13th International Conference on Automated Planning and Scheduling (ICAPS), Trento, Italy, June 2003.
  2. C. CHAPMAN,M. MUSOLESI ,W. EMMERICH and C. MASCOLO, 2007. Predictive Resource Scheduling in Computational Grids. IEEE International Parallel and Distributed Processing Symposium, Long Beach, CA, 2007, 1-10.
  3. J. CHEN, J. PENG ,andX. CAO2009. A Grid Resource Scheduling Algorithm Based on the Utility Optimization. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 5(1), 1355-1362.
  4. CHEN, J., AND LU, B., 2008. Load Balancing Oriented Economic Grid Resource Scheduling. Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 19-20 Dec. 2008, 813-817.
  5. AJ. CHEN ,and, B. LU 2008. Load Balancing Oriented Economic Grid Resource Scheduling. Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 19-20 Dec. 2008, 813-817.
  6. L. CHUNLIN, 2008. Joint Application-Fabric Layer Optimization in Grid Computing. 11th IEEE International Conference on Computational Science and Engineering, Sao Paulo, 16-18 July 2008, 141 - 146.
  7. P.A, DINDA2001. Online prediction of the running time of tasks. In Proc. 10th IEEE Symp. on High Performance Distributed Computing, 2001.
  8. U. FAROOQ, S., MAJUMDARA and E.W. PARSONSA 2009. chieving efficiency, quality of service and robustness in multi-organizational Grids. Journal of Systems and Software, 82(1), 23-38.
  9. I. FOSTER and C. KESSELMAN 1986. Computational grids. The Grid: Blueprint for a New Computing Infrastructure, pages 15-52, Morgan Kaufmann, San Francisco, California, 1986.
  10. A. GALSTYAN , K. CZAJKOWSKI and K. LERMAN 2004. Resource Allocation in the Grid Using Reinforcement Learning. In Proc. Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004.
  11. J. GOMOLUCH and M. SCHROEDER 2003. Market-based Resource Allocation for Grid Computing: A Model and Simulation. Middleware Workshops, 2003, 211-218.
  12. Y. HAO , Y. XU ,G. LIU and Z. PAN 2008. An Expectation Trust Benefit Driven Algorithm for Resource Scheduling in Grid Computing. 3rd International Conference on Innovative Computing Information and Control, Dalian, Liaoning China, 18-20 June 2008, 88 - 88.
  13. W. JIANG , H. CUI, and J. CHEN 2009. A fuzzy modeling based dynamic resource allocation strategy in service grid. KAUR, G., AND CHOPRA, I., 2007. Grid Computing- Challenges Confronted and Opportunities Offered. Proceedings of COIT, 2007.
  14. R. E.,KALMAN1960. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME Journal of Basic Engineering, 82(D), 35-45.
  15. N. KIRAN , V. MAHESWARAN ,M. SHYAM and P. NARAYANASAMY 2007. A novel Task Replica based Resource Scheduling Algorithm in Grid computing. International Conference on High Performance Computing, 2007.
  16. S. KRAWCZYK , and K. BUBENDORFER 2008. Grid Resource Allocation: Allocation Mechanisms and Utilisation Patterns. Conferences in Research and Practice in Information Technology Series; In Proc. Sixth Australasian workshop on Grid computing and e-research, 2008, 73-81.
  17. K. KUROWSKI A. OLEKSIAK,J. NABRZYSKI,A. KWIECIE,M. WOJTKIEWICZ,M. DYCZKOWSKI,F. GUIM,J. CORBALAN and J. LABARTA 2007. Multi-Criteria Grid Resource Management Using Performance Prediction Techniques. Integrated Research in GRID Computing, 215-225.
  18. J. LI, W. ZIEGLER , O. WALDRICH, and , D. MALLMANN 2008. Towards SLA Based Software License Management in Grid Computing.CoreGRID Technical Report, Number TR-0136, 2008.
  19. S.S MANVI, M.N. BIRJE and B. PRASAD 2005. An Agent-based Resource Allocation Model for computational grids. Multiagent and Grid Systems - An International Journal 1(1), 17-27.
  20. A.M. MEHTA, J. SMITH ,H.J. SIEGEL ,A.A. MACIEJEWSKI and A. JAYASEELAN 2006. Dynamic Resource Management Heuristics for Minimizing Makespan while Maintaining an Acceptable Level of Robustness in an Uncertain Environment. 12th International Conference on Parallel and Distributed Systems, Minneapolis, Minnesota, July 12-15, 2006.
  21. S. MURUGANANTHAM,P.K. SRIVASTHA and KHANAA 2010. Object Based Middleware for Grid Computing. Journal of Computer Science, 6(3), 336-340.
  22. M. NAQAASH M.A. IQBAL,Q.PERVAIZ,O. SHAMIM and S. A. HUSSAIN 2010. Grid Computing Used For Next Generation High Speed Processing Technology. International Journal on Computer Science and Engineering, 02(05), 1926- 1933.
  23. PLAXTON, C.G., SUN, Y., TIWARI, M., AND VIN, H., 2006. Reconfigurable Resource Scheduling. Proceedings of the 18th Annual ACM Symposium on Parallel Algorithms and Architectures, Cambridge, Massachusetts, USA, July 30 - August 2, 2006.
  24. C.G. PLAXTON , Y. SUN,M. TIWARI and H. VIN 2006. Reconfigurable Resource Scheduling. In Proceedings of the 18th Annual ACM Symposium on Parallel Algorithms and Architectures, Cambridge, Massachusetts, USA, July 30 - August 2, 2006.
  25. L. POURMOHAMMADBAGHER2008. Intelligent Agent System Simulation Using Fear Emotion. World Academy of Science, Engineering and Technology, 48, 334-338.
  26. M. RAMIREZ-GONZALEZ and O.P. 2009. Simplified Fuzzy Logic Controller and its Application as a Power System Stabilizer. 15th International Conference on Intelligent System Applications to Power Systems, 8-12 Nov. 2009, 1-6.
  27. T. ROBLITZ,F. SCHINTKE ,and A. REINEFELD2006. Resource reservations with fuzzy requests. Concurrency and Computation: Practice and Experience, 18(13), 1681-1703.
  28. W. SILER and J.J. BUCKLEY 2005. Fuzzy expert systems and fuzzy reasoning. John Wiley and Sons, Inc., Hoboken, New Jersey, 2005.
  29. H.C. TSENG2007. Internet Applications with Fuzzy Logic and Neural Networks: A Survey. Journal of Engineering, Computing and Architecture, 1(2).
  30. V. VIJAYAKUMARand R.S.D. WAHIDA BANU2008. Security for Resource Selection in Grid Computing Based on Trust and Reputation Responsiveness. IJCSNS International Journal of Computer Science and Network Security, 8(11), 107-115.
  31. R. WANKAR2008. Grid Computing With Globus: An Overview and Research Challenges. International Journal of Computer Science and Applications, 5(3), 56 - 69.
  32. R. WOLSKI,N. SPRING ,and J. HAYES1999. Predicting the CPU availability of time-shared unix systems on the computational grid. In proceedings of High Performance Distributed Computing, Redondo Beach, CA, 1999,105 - 112.
  33. R. WOLSKI,N. SPRING ,and J. HAYES1999. The network weather service: A distributed Resource performance forecasting service for metacomputing. Future Generation Computer Systems, 15(5 -6), 757-768.
  34. F. XIA,W. ZHAO ,Y. SUN and Y.C. TIAN2007. Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks. Sensors, 7(12), 3179-3191.
  35. L.A. ZADEH2008. Is There a Need for Fuzzy Logic?. Information Sciences: an International Journal, 178(13), 2751-2779.
  36. ZHANG, J., AND PHILLIPS, C., 2009. Job-Scheduling with Resource Availability Prediction for Volunteer-Based Grid Computing. London Communications Symposium, LCS 2009, University College London, September 2009.
  37. J. ZHANG,and C. PHILLIPS2009. Job-Scheduling with Resource Availability Prediction for Volunteer-Based Grid Computing. London Communications Symposium, LCS 2009, University College London, September 2009.