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Grid Multicriteria Job Scheduling with Resource Reservation and Prediction Mechanisms

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Book cover Perspectives in Modern Project Scheduling

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 92))

Abstract

Grids link together computers, data, sensors, large scale scientific instruments, visualization systems, networks and people. They can provide very large pools of computer resources, enable distributed collaborations and deliver increased efficiency and on-demand computing capabilities. The complexity of Grids on one hand and the requirements towards performance and capability on the other hand call for efficient resource management and scheduling mechanisms. Such mechanisms must take into account not only the hardware and software resources, user jobs and applications, but also policies of the resource owners. Policies usually describe cost models for the resource usage, security mechanisms, quality of service of resource provisioning etc. The problem of scheduling jobs in real Grid environments is very difficult. Due to lack of time characteristics of jobs, and difficulties in characterizing the overall system, traditional OR techniques usually fail or achieve very weak results. Usually, best effort scheduling is the best option. There are, however, some ways to deal with the problems described above.

The main goal of this paper it to present some practical issues of scheduling Grid jobs. Methods and techniques described in the paper are used in a Grid scheduling system, called GRMS (Grid Resource Management System) developed at Poznan Supercomputing and Networking Center. GRMS is widely used in many Grid infrastructures worldwide.

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References

  • Abramson, D., Buyya, R. and Giddy, J. (2002). A computational economy for Grid computing and its implementation in the Nimrod-G resource broker, Future Generation Computer Systems, 18(8): 1061–1074.

    Article  Google Scholar 

  • Agrawal, R. and Srikant, R. (1994). Fast Algorithms for Mining Association Rules, in: Proceedings of the Twentieth Intl. Conference on Very Large Databases, Morgan Kaufmann, pp. 487–499.

    Google Scholar 

  • Allen, G., Davis, K., Dolkas, K.N., Doulamis, N.D., Goodale, T., Kielmann, T., Merzky, A., Nabrzyski, J., Pukacki, J., Radke, T., Russell, M., Seidel, E., Shalf, J. and Taylor, I. (2003). Enabling Applications on the Grid-A GridLab Overview, International Journal of High Performance Computing Applications, 17(4):449–466.

    Article  Google Scholar 

  • Bode, B., Kendall, D.M. and Lei, Z. (2000). The Portable Batch Scheduler and the Maui scheduler on Linux clusters, in: Proceedings of 4th Annual Linux Showcase and Conference, October 2000.

    Google Scholar 

  • ÄŒerny, V. (1985). Thermodynamical Approach to the Traveling Salesman Problem: An Efficient Simulation Algorithm, Journal of Optimization Theory and Applications, 45:41–51.

    Article  MathSciNet  Google Scholar 

  • Cheung, L.S. (2001). A Fuzzy Approach to Load Balancing in a Distributed Object Computing Network, in: Proceedings of the First IEEE International Symposium of Cluster Computing and the Grid (CCGrid’01), pp. 694–699.

    Google Scholar 

  • Condor Group, Condor project, http://www.cs.wisc.edu/condor.

    Google Scholar 

  • Czajkowski, K., Foster, I., Kesselman, C., Martin, S., Smith, W. and Tuecke, S. (1997). A resource management architecture for metacomputing systems, JSSPP Whorskshop, Lecture Notes on Computer Science, 1459:62–68.

    Article  Google Scholar 

  • Dail, H. (2001). A Modular Framework for Adaptive Scheduling in Grid Application Development Environments, Technical report CS2002-0698, Computer Science Department, University of California, San Diego.

    Google Scholar 

  • Darken, C. and Moody, J. (1990). Fast Adaptive k-means clustering: Some empirical results, in: Proceedings of the International Joint Conference on Neural Networks, vol. II, IEEE Neural Networks Council, pp. 233–238.

    Article  Google Scholar 

  • Dinda, P. (2001). Online prediction of the running time of tasks, in: Proceedings of 10th IEEE Symp. on High Performance Distributed Computing, pp. 336–337.

    Google Scholar 

  • Downey, A. (1997). Predicting Queue Times on Space-Sharing Parallel Computers, in: 11th International Parallel Processing Symposium, pp. 209–218.

    Google Scholar 

  • Global Grid Forum DRMAA WG, DRMAA Web Site, http://www.drmaa.org.

    Google Scholar 

  • European DataGrid Project, http://www.eu-datagrid.org.

    Google Scholar 

  • El-Ghazawi, T., Gaj, K., Alexandridis, N., Vroman, F., Nguyen, N., Radzikowski, J.R., Samipagdi, P. and Suboh, S.A. (2004). A performance study of job management systems, Concurrency and Computation: Practice and Experience, 16(13): 1229–1246.

    Article  Google Scholar 

  • Feitelson, D.G. and Mu’alem Weil, A. (1998). Utilization and predictability in sche-duling the IBM SP2 with backfilling, Proceedings of 12th International Parallel Processing Symp., Orlando, pp. 542–546.

    Google Scholar 

  • Feitelson, D.G., Parallel Workload Archive, http://www.cs.huji.ac.il/labs/parallel/work-load.

    Google Scholar 

  • Figuiera, S.M. and Bermann, F. (2001). Mapping Parallel Applications to Distributed Heterogeneous Systems, Technical report CS2002-0698, Computer Science Department, University of California, San Diego.

    Google Scholar 

  • Foster, I. and Kesselman, C. (1998). The Globus Project: A Status Report, in: Proceedings of the Seventh Heterogeneous Computing Workshop, pp. 4–18.

    Google Scholar 

  • Foster, I. and Kesselman, C. (editors) (1999). The Grid: Blueprint for a New Computing Infrastructure, Morgan Kauffmann, San Francisco, California.

    Google Scholar 

  • Foster, I. and Kesselman, C. (1999). Computational Grids, in: The Grid: Blueprint for a New Computing Infrastructure, I. Foster and C. Kesselman, eds, Morgan Kaufmann, San Francisco, California, pp. 15–52.

    Google Scholar 

  • Gibbons, R. (1997). A Historical Application Profiler for Use by Parallel Schedulers, Lecture Notes on Computer Science, 1297:58–75.

    Google Scholar 

  • Globus Team, Globus Project, http://www.globus.org.

    Google Scholar 

  • Glover, F. (1989). Tabu Search-part 1, ORSA Journal of Computing, 1(3): 190–206.

    Google Scholar 

  • Glover, F. (1990). Tabu Search-part 2, ORSA Journal of Computing, 2:4–32.

    Google Scholar 

  • Glover, F. (1986). Future Path for Integer Programming and Links to Artificial Intelligence, Computers & Operations Research, 13:533–549.

    Article  MathSciNet  Google Scholar 

  • Goldberg, D.E., (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading.

    MATH  Google Scholar 

  • Greco, S., Matarazzo, B., Slowinski, R. and Tsoukias, A. (1998). Exploitation of a rough approximation of the outranking relation in multicriteria choice and ranking, in: Trends in Multi-Criteria Decision Making, T.J Stewart and R.C van der Honert, eds, Springer Verlag, Berlin, pp. 45–60.

    Google Scholar 

  • Greco, S., Matarazzo, S. and Slowinski, R. (2001). Rough sets theory for multicriteria decision analysis, European Journal of Operational Research, 129(1): 1–47.

    Article  MathSciNet  Google Scholar 

  • Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press.

    Google Scholar 

  • Ishibushi, H. and Murata, T. (1998). A Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling, IEEE Transactions on Systems, Man and Cybernetics, 28(3):392–403.

    Article  Google Scholar 

  • Jackson, D.B., Maui Admin Guide, http://supercluster.org/maui/docs/mauiadmin.html.

    Google Scholar 

  • Jaszkiewicz, A. (1998). Genetic local search for multiple objective combinatorial optimisation, Technical Report RA014 /98, Institute of Computing Science, Poznan University of Technology.

    Google Scholar 

  • Kirkpatrick, S., Gelatt, C.D., Jr and Vecchi, M.P. (1983)., Optimization by Simulated Annealing, Science, 230:671–680.

    Article  MathSciNet  ADS  Google Scholar 

  • Knowles, J.D. and Corne, D.W. (2000). A Comparison of Diverse Approaches to Memetic Multiobjective Combinatorial Optimization, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), Workshop On Memetic Algorithms, pp. 103–108.

    Google Scholar 

  • Knowles, J.D. and Corne, D.W. (2000). M-PAES: A Memetic Algorithm for Multiobjective Optimization, in: Proceedings of the 2000 Congress on Evolutionary Computation, pp. 325–332.

    Google Scholar 

  • Kurowski, K., Nabrzyski, J. and Pukacki, J. (2000). Multicriteria Resource Management Architecture for Grid, in: Proceedings of the 4th Globus Retreat, Pittsburgh, PA, July 2000.

    Google Scholar 

  • Kurowski, K., Nabrzyski, J. and Pukacki, J. (2000). Predicting Job Execution Times in the Grid, in: Proceedings of the 1st SGI 2000 International User Conference, Krakow, pp. 272–282.

    Google Scholar 

  • Kurowski, K., Nabrzyski, J. and Pukacki, J. (2001). User preference driven multiobjective resource management in Grid environments, in: Proceedings of the First IEEE International Symposium on Cluster Computing and the Grid (CCGrid’01), pp. 114–121.

    Google Scholar 

  • Kurowski, K., Nabrzyski, J., Oleksiak, A. and WÄ™glarz, J. (2003). Multicriteria Aspects of Grid Resource Management, in: Grid Resource Management, J. Nabrzyski, J. Schopf, and J. WÄ™glarz, eds, Kluwer Academic Publishers, Boston/Dordrecht/London, pp. 271–294.

    Google Scholar 

  • Kurowski, K., Ludwiczak, B., Nabrzyski, J., Oleksiak, A. and Pukacki, J. (2004). Improving Grid Level Throughput Using Job Migration and Rescheduling Techniques in GRMS, Scientific Programming, 12:(4)263–273.

    Google Scholar 

  • Kurowski, K., Oleksiak, A., Nabrzyski, J., Guim, F., Corbalan, J., Labarta, J., Kwiecien, A., Wojtkiewicz, M. and Dyczkowski, M. (2005). Multicriteria Grid Resource Management using Performance Prediction Techniques, in: Proceedings of the 2nd CoreGrid Workshop, Springer Verlag (to appear).

    Google Scholar 

  • Langley, P., Iba, W. and Thompson, K. (1992). in: An Analysis of Bayesian Classifiers, Proceedings of AAAI-92, pp. 223–228.

    Google Scholar 

  • Lifka, D. (1995). The ANL/IBM SP scheduling system, in: Job Scheduling Strategies for Parallel Processing, D.G. Feitelson and L. Rudolph, eds, Springer-Verlag, Lecture Notes of Computer Science, 949:295–303.

    Google Scholar 

  • Liu, C., Yang, L., Foster, I. and Angulo, D. (2002). Design and evaluation of a resource selection framework for Grid applications, in: Proceedings if the Eleventh IEEE International Symposium on High-Performance Distributed Computing (HPDC-II), pp. 63–72.

    Google Scholar 

  • Nabrzyski, J., Schopf, J. and Weglarz, J., editors, (2003). Grid Resource Management-State of the Art and Future Trends, Kluwer Academic Publishers.

    Google Scholar 

  • Nabrzyski, J. (2000). User Preference Driven Expert System for Solving Multi-objective Project Scheduling Problems, Ph.D. Thesis, Poznan University of Technology.

    Google Scholar 

  • Pawlak, Z. (1982). Rough Sets, International Journal of Information & Computer Sciences, 11(5):341–356.

    Article  MathSciNet  Google Scholar 

  • Platform Computing Technical Docs, http://www.platform.com/services/support /docs/LSFDoc51.asp.

    Google Scholar 

  • Quinlan, J.R. (1986), Induction of Decision Trees, Machine Learning, 1:81–106.

    Google Scholar 

  • Rumelhart, D.E., Hinton, G.E. and Williams, RJ. (1986). Learning Representations by Back Propagating Errors, Nature, 323:533–536.

    Article  ADS  Google Scholar 

  • Sandholm, T.W. (1999). Distributed Rational Decision Making, in: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, G. Weiss, ed, MIT Press, pp. 201–258.

    Google Scholar 

  • Schopf, J. and Berman, F. (1998). Performance prediction in production environments, in: Proceedings of IPPS/SPDP, pp. 647–653.

    Google Scholar 

  • Shirazi, B.A., Husson, A.R. and Kavi, K.M. (1995). Scheduling and Load Balancing in Parallel and Distributed Systems, IEEE Computer Society Press.

    Google Scholar 

  • Smith, W., Taylor, V. and Foster, I. (1999), Using Run-Time Predictions to Estimate Queue Wait Times and Improve Scheduler Performance, Proceedings of the IPPS/SPDP’ 99 Workshop on Job Scheduling Strategies for Parallel Processing, pp. 202–219.

    Google Scholar 

  • Taylor V., Wu, X., Geisler, J., Li, X., Lan, Z., Hereld, M., Judson, R. and Stevens, R. (2001). Prophesy: Automating the modeling process, in: Proceedings Of the Third International Workshop on Active Middleware Services.

    Google Scholar 

  • Veridian Inc. PBS: The Portable Batch System. http://www.openpbs.org/

    Google Scholar 

  • Vazhkudai, S. and Schopf, J. (2003). Using Regression Techniques to Predict Large Data Transfers, Journal of High Performance Computing Applications-Special Issue on Grid Computing: Infrastructure and Application, 17: 249–268.

    Google Scholar 

  • WÄ™glarz, J., editor (1999). Project Scheduling-Recent Models, Algorithms and Applications, Kluwer Academic Publishers.

    Google Scholar 

  • Wolski, R., Spring, N. and Hayes, J. (1999). The Network Weather Service: a distributed resource performance forecasting service for metacomputing, Future Generation Computer Systems, 15(5–6): 757–768.

    Article  Google Scholar 

  • Wolski, R. (1997). Dynamically Forecasting Network Performance to Support Dynamic Scheduling Using the Network Weather Service, Cluster Computing, 1(1): 119–132.

    Article  Google Scholar 

  • Zadeh, L.A. (1965), Fuzzy Sets, Information and Control, 8(3):338–353.

    Article  MathSciNet  Google Scholar 

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Kurowski, K., Nabrzyski, J., Oleksiak, A., Weglarz, J. (2006). Grid Multicriteria Job Scheduling with Resource Reservation and Prediction Mechanisms. In: Józefowska, J., Weglarz, J. (eds) Perspectives in Modern Project Scheduling. International Series in Operations Research & Management Science, vol 92. Springer, Boston, MA . https://doi.org/10.1007/978-0-387-33768-5_14

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