The Community for Technology Leaders
RSS Icon
Issue No.09 - September (2010 vol.21)
pp: 1304-1316
Alexander Fölling , TU Dortmund University, Dortmund
Christian Grimme , TU Dortmund University, Dortmund
Joachim Lepping , TU Dortmund University, Dortmund
Alexander Papaspyrou , TU Dortmund University, Dortmund
In this paper, we address the problem of finding well-performing workload exchange policies for decentralized Computational Grids using an Evolutionary Fuzzy System. To this end, we establish a noninvasive collaboration model on the Grid layer which requires minimal information about the participating High Performance and High Throughput Computing (HPC/HTC) centers and which leaves the local resource managers completely untouched. In this environment of fully autonomous sites, independent users are assumed to submit their jobs to the Grid middleware layer of their local site, which in turn decides on the delegation and execution either on the local system or on remote sites in a situation-dependent, adaptive way. We find for different scenarios that the exchange policies show good performance characteristics not only with respect to traditional metrics such as average weighted response time and utilization, but also in terms of robustness and stability in changing environments.
Grid computing, evolutionary fuzzy systems, online grid scheduling, performance evaluation.
Alexander Fölling, Christian Grimme, Joachim Lepping, Alexander Papaspyrou, "Robust Load Delegation in Service Grid Environments", IEEE Transactions on Parallel & Distributed Systems, vol.21, no. 9, pp. 1304-1316, September 2010, doi:10.1109/TPDS.2010.16
[1] F. Gagliardi, B. Jones, F. Grey, M.-E. Begin, and M. Heikkurinen, "Building an Infrastructure for Scientific Grid Computing: Status and Goals of the Egee Project," Philosophical Trans. Series A, Math., Physical, and Eng. Sciences, vol. 363, no. 1833, pp. 1729-1742, 2005.
[2] C. Franke, F. Hoffmann, J. Lepping, and U. Schwiegelshohn, "Development of Scheduling Strategies with Genetic Fuzzy Systems," Applied Soft Computing, vol. 8, no. 1, pp. 706-721, Jan. 2008.
[3] O. Cordón, F. Herrera, F. Hoffmann, and L. Magdalena, "Evolutionary Tuning and Learning of Fuzzy Knowledge Bases," Genetic Fuzzy Systems, vol. 19, World Scientific, July 2001.
[4] D.G. Feitelson and B. Nitzberg, "Job Characteristics of a Production Parallel Scientific Workload on the NASA Ames iPSC/860," Proc. Conf. First Job Scheduling Strategies for Parallel Processing, D.G. Feitelson and L. Rudolph, eds., pp. 337-360, 1995.
[5] H.-P. Schwefel, Evolution and Optimum Seeking. John Wiley & Sons, 1995.
[6] D.C. Marinescu, L. Boloni, R. Hao, and K.K. Jun, "An Alternative Model for Scheduling on a Computational Grid," Proc. 13th Int'l Symp. Computer and Information Sciences (ISCIS '98), pp. 473-480, 1998.
[7] Y.-S. Kee, H. Casanova, and A.A. Chien, "Realistic Modeling and Synthesis of Resources for Computational Grids," Proc. Conf. High Performance Networking and Computing, pp. 54-63, 2004.
[8] U. Schwiegelshohn, A. Tchernykh, and R. Yahyapour, "Online Scheduling in Grids," Proc. 22nd IEEE Int'l Parallel and Distributed Processing Symp. (IPDPS '08), Apr. 2008.
[9] U. Schwiegelshohn and R. Yahyapour, "Fairness in Parallel Job Scheduling," J. Scheduling, vol. 3, no. 5, pp. 297-320, 2000.
[10] T. Takagi and M. Sugeno, "Fuzzy Identification of Systems and its Applications to Modeling and Control," IEEE Trans. Systems, Man, and Cybernetics, vol. SMC-15, no. 1, pp. 116-132, 1985.
[11] C.-F. Juang, J.-Y. Lin, and C.-T. Lin, "Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design," IEEE Trans. System, Man and Cybernetics, vol. 30, no. 2, pp. 290-302, Apr. 2000.
[12] Y. Jin, W. von Seelen, and B. Sendhoff, "On Generating $FC^3$ Fuzzy Rule Systems from Data Using Evolution Strategies," IEEE Trans. System, Man and Cybernetics, vol. 29, no. 6, pp. 829-845, Dec. 1999.
[13] C. Franke, J. Lepping, and U. Schwiegelshohn, "Genetic Fuzzy Systems Applied to Online Job Scheduling," Proc. IEEE Int'l Conf. Fuzzy Systems, pp. 1573-1578, June 2007.
[14] C. Ernemann, V. Hamscher, and R. Yahyapour, "Benefits of Global Grid Computing for Job Scheduling," Proc. Fifth IEEE/ACM Int'l Workshop Grid Computing (GRID '04), pp. 374-379, 2004.
[15] C. Grimme, J. Lepping, and A. Papaspyrou, "Benefits of Job Exchange between Autonomous Sites in Decentralized Computational Grids," Proc. Eighth IEEE Int'l Symp. Cluster Computing and the Grid (CCGrid), pp. 25-32, May 2008.
[16] C. Ernemann, V. Hamscher, U. Schwiegelshohn, A. Streit, and R. Yahyapour, "On Advantages of Grid Computing for Parallel Job Scheduling," Proc. Second IEEE/ACM Int'l Symp. Cluster Computing and the Grid (CCGRID '02), pp. 39-46, May 2002.
[17] K. Kurowski, J. Nabrzski, A. Oleksiak, and J. Weglarz, "Scheduling Jobs on the Grid—Multicriteria Approach," Computational Methods in Science and Technology, vol. 12, no. 2, pp. 123-138, 2006.
[18] B. Hong and V.K. Prasanna, "Bandwidth-Aware Resource Allocation for Heterogeneous Computing Systems to Maximize Throughput," Proc. Int'l Conf. Parallel Processing (ICPP '03), pp. 539-546, 2003.
[19] A. Iosup, T. Tannenbaum, M. Farrellee, D. Epema, and M. Livny, "Inter-Operating Grids through Delegated Matchmaking," Scientific Programming, vol. 16, nos. 2/3, pp. 233-253, 2008.
[20] J. Carretero, F. Xhafa, and A. Abraham, "Genetic Algorithm Based Schedulers for Grid Computing Systems," Int'l J. Innovative Computing, Information and Control, vol. 3, no. 6, pp. 1-19, 2007.
[21] W. Jakob, A. Quinte, K.-U. Stucky, and W. Süss, "Optimised Scheduling of Grid Resources Using Hybrid Evolutionary Algorithms," Proc. Sixth Int'l Conf. Parallel Processing and Applied Math., pp. 406-413, 2005.
[22] D. England and J.B. Weissman, "Cost and Benefits of Load Sharing in the Computational Grid," Proc. 10th Job Scheduling Strategies for Parallel Processing, D.G. Feitelson, L. Rudolph, and U. Schwiegelshohn, eds., pp. 160-175, 2004.
[23] V. Hamscher, U. Schwiegelshohn, A. Streit, and R. Yahyapour, "Evaluation of Job-Scheduling Strategies for Grid Computing," Proc. Seventh Int'l Conf. High Performance Computing (HiPC '00), pp. 191-202, 2000.
[24] C. Grimme, J. Lepping, and A. Papaspyrou, "Prospects of Collaboration between Compute Providers by Means of Job Interchange," Proc. Conf. Job Scheduling Strategies for Parallel Processing, pp. 132-151, June 2007.
[25] C. Grimme, J. Lepping, and A. Papaspyrou, "Discovering Performance Bounds for Grid Scheduling by Using Evolutionary Multiobjective Optimization," Proc. Genetic and Evolutionary Computation Conf. (GECCO '08), pp. 1491-1498, July 2008.
[26] J. Huang, H. Jin, X. Xie, and Q. Zhang, "An Approach to Grid Scheduling Optimization Based on Fuzzy Association Rule Mining," Proc. First Int'l Conf. e-Science and Grid Computing, pp. 189-195, 2005.
[27] C. Fayad, J.M. Garibaldi, and D. Ouelhadj, "Fuzzy Grid Scheduling Using Tabu Search," Proc. Int'l Conf. Fuzzy Systems (FUZZ IEEE '07), pp. 1-6, 2007.
[28] C. Franke, J. Lepping, and U. Schwiegelshohn, "On Advantages of Scheduling Using Genetic Fuzzy Systems," Proc. 12th Workshop Job Scheduling Strategies for Parallel Processing (JSSPP), pp. 68-93, June 2006.
30 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool