Job control in heterogeneous computing systems


A simulation model based on detailed algorithmic description of the process of collective interaction of remote resources and users via a telecommunication network is proposed. Online job stream control includes allocation of resources for their execution and choice of the volume of current job batches. The control objective is formalized in terms of system performance, number of failures, and cost factors. Adaptive control strategies optimizing limiting average characteristics are used. Results of numerical experiments are presented.

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  1. 1.

    M. G. Konovalov, “Adaptive Control of Job Allocation in the Model of Collective Use of Distributed Computing Resources”, in Proceedings of 2nd International Conference “Distributed Calculations and Grid Technologies in Science and Education”, Dubna, Russia, 2006, pp. 124–127.

  2. 2.

    S. V. Antonov, Yu. A. Dushin, M. G. Konovalov, et al., “Development of Mathematical Models and Methods for Job Distribution in a Distributed Computing System,” in Informatics Systems and Tools (Nauka, Moscow, 2006), pp. 32–45 [in Russian].

    Google Scholar 

  3. 3.

    M. Gaeta, M. Konovalov, and S. Shorgin, “Development of Mathematical Models and Methods of Task Distribution in Distributed Computing System,” Reliability: Theory and Applications 1(4), 16–21 (2006).

    Google Scholar 

  4. 4.

    S. S. Vadhiyar and J. J. Dongarra, “A Metascheduler for the Grid,” in Proceedings of 11th IEEE Symposium “High Performance Distributed Computing”, Edingurgh, UK, 2002, pp. 343–351.

  5. 5.

    C. Li and L. Li, “Utility-Based QoS Optimization Strategy for Multi-Criteria Scheduling on the Grid,” J. Parallel Distributed Comput. 67(2), 142–153 (2007).

    MATH  Article  Google Scholar 

  6. 6.

    V. Salmani, R. Ensafi, N. Khatib-Astaneh, et al., “A Fuzzy-Based Multi-Criteria Scheduler for Uniform Multiprocessor Real-Time Systems,” in Proceedings of 10th International Conference on Information Technology (ICIT 2007), Rourkela, India, 2007, pp. 179–184.

  7. 7.

    V. Salmani, M. Naghibzadeh, M. Kahani, et al., “Multi-Criteria Scheduling of Soft Real-Time Tasks on Uniform Multiprocessors Using Fuzzy Inference,” Advances and Innovations in Systems, Computing Sciences and Software Engineering. Bridgeport, 439–444 (2007).

  8. 8.

    D. Klusácek, H. Rudová, R. Baraglia, et al., “Comparison of Multi-Criteria Scheduling Techniques,” Grid Computing, 173–184 (2008).

  9. 9.

    A. Giersh, Y. Rober, and F. Vivien, “Scheduling Tasks Sharing Files on Heterogeneous Master-Slave Platforms”, in Proceedings of 12th Euromicro Workshop in Par., Dist. and Network-Based, Strasbourg, France, 2004, pp. 364–371.

  10. 10.

    H. Senger, E. R. Hruschka, F. A. B. Silva, et al., “In-Hambu: Data Mining Using Idle Cycles in Clusters of PCs,” Lect. Notes Comp. Sci. 3222, 213–220 (2004).

    Article  Google Scholar 

  11. 11.

    F. A. B. Silva, S. Carvalho, and E. R. Hruschka, “A Scheduling Algorithm for Running Bag-of-Tasks Data Mining Application on the Grid,” Lect. Notes Comp. Sci. 3419, 254–262 (2004).

    Article  Google Scholar 

  12. 12.

    F. A. B. Silva, S. Carvalho, H. Senger, et al., “Running Data Mining Applications on the Grid: a Bag-of-Tasks Approach,” Lect. Notes Comp. Sci. 3044, 168–177 (2004).

    Article  Google Scholar 

  13. 13.

    D. McLaughlin, S. Sardesai, and P. Dasgupta, “Preemptive Scheduling for Distributed Systems,” in Proceedings of 11 International Conference on Parallel and Distributed Computing Systems, Chicago, USA, 1998 (IEEE Comp Sc. Press 1998), pp. 222–227.

  14. 14.

    I. Ahmad, S. Shamala, M. Othman, et al., “Preemptive Utility Accrual Scheduling Algorithm for Adaptive Real Time System,” int. J. of Computer Science and Network Security 8(5), 57–61 (2008).

    Google Scholar 

  15. 15.

    P. Li, “Utility Accrual Real Time Scheduling: Models and Algorithms”, Ph.D. Thesis (Virginia Polytechnic Institute and State University, Blacksburg, 2004).

    Google Scholar 

  16. 16.

    D. V. Krasovskii and M. G. Furugyan, “Algorithms for Solving Minimax Scheduling Problem,” Izv. Ross. Akad. Nauk, Teor. Sist. Upr., No. 5, 65–70 (2008) [Comp. Syst. Sci. 47 (5), 732–736 (2008).

  17. 17.

    L. Marchal, Y. Yang, H. Casanova, et al., “Steady-State Scheduling of Multiple Divisible Load Applications on Wide-Area Distributed Computing Platforms,” int. J. of High Performance Computing Applications 20(3), 365–381 (2006).

    Article  Google Scholar 

  18. 18.

    C. Banino, O. Beaumont, L. Carter, et al., “Scheduling Strategies for Master-Slave Tasking on Heterogeneous Processor Platforms,” IEEE Trans. Parallel Distributed Systems 15(4), 319–330 (2004).

    Article  Google Scholar 

  19. 19.

    O. Beaumont, H. Casanova, A. Legrand, et al., “Scheduling Divisible Loads for Star and Tree Networks: Main Results and Open Problems,” IEEE Trans. Parallel and Distributed Systems 16(3), 207–218 (2005).

    Article  Google Scholar 

  20. 20.

    M. Chen, G. Yang, and X. Liu, “Gridmarket: A Practical, Efficient Market Balancing Resource for Grid and P2P Computing,” Lect. Notes Comp. Sci. 3033, 612–619 (2003).

    Article  Google Scholar 

  21. 21.

    D. Grosu and A. Das, “Auction-Based Resource Allocation Protocols in Grids” in Proceedings of 16th IASTED International Conference on Parallel and Distributed Computing and Systems, MIT, Cambridge, USA, 2004, pp. 20–27.

  22. 22.

    L. V. Kale, S. Kumar, M. Potnuru, et al., “Faucets: Efficient Resource Allocation on the Computational Grid,” in Proceedings of International Conference on Parallel Processing, ICPP’2004, Montreal, Canada, 2004 (IEEE Comp. Sc. Press, 2004), pp. 396–405.

  23. 23.

    U. Kant and D. Grosu, “Double Auction Protocols for Resource Allocation in Grids”, in Proceedings of International Conference on Information Technology: Coding and Computing (ITCC’05) (IEEE Comp. Sc. Press, Washington, DC, 2005), Vol. 1, pp. 366–371.

    Google Scholar 

  24. 24.

    R. Wolski, J. S. Plank, J. Brevik, et al., “Analyzing Market-Based Resource Allocation Strategies for the Computational Grid,” int. J. of High Performance Computing Applications 15(3), 258–281 (2001).

    Article  Google Scholar 

  25. 25.

    D. Grosu and A. Das, “Auction-Based Resource Allocation Protocols in Grids” in Proceedings of 16th IASTED International Conference on Parallel and Distributed Computing and Systems, MIT, Cambridge, USA, 2004, pp. 20–27.

  26. 26.

    L. Xiao, Y. Zhu, M. Lionel, et al., “GridIS: An Incentive-Based Grid Scheduling”, in Proceedings of 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS’05), Denver, USA, 2005, Vol. 1, p. 65.

  27. 27.

    C. Ernemann, V. Hamscher, U. Schwiegelshohn, et al., “On Advantages of Grid Computing for Parallel Job Scheduling,” in Proceedings of 2nd IEEE/ACM International Symposium on Cluster Computing and the GRID (CCGrid2002) (Los Alamitos, IEEE Comp. Sc. Press, 2002), pp. 39–46.

  28. 28.

    R. Buyya, D. Abramson, and J. Giddy, “Nimrod/G: An Architecture for a Resource Management and Scheduling System in a Global Computational Grid,” in Proceedings of 4th International Conference on High Performance Computing in Asia-Pacific Region, Monash, Australia, 2000, pp. 238–289.

  29. 29.

    J. Slegers, I. Mitrani, and N. Thomas, “Optimal Dynamic Server Allocation in Systems with On/Off Sources,” Lect. Notes Comp. Sci. 4748, 186–199 (2007).

    Article  Google Scholar 

  30. 30.

    M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley, New York, 1994).

    MATH  Google Scholar 

  31. 31.

    V. G. Sragovich, Mathematical Theory of Adaptive Control (World Sci., Singapore, 2006).

    MATH  Google Scholar 

  32. 32.

    S. Mahadevan, “Average Reward Reinforcement Learning Foundations, Algorithms, and Empirical Results,” Machine Learning 22, 159–195 (1996).

    Google Scholar 

  33. 33.

    P. L. Bartlett, “An Introduction to Reinforcement Learning Theory: Value Function Methods,” Lect. Notes Comp. Sci. 2600, 184–202 (2003).

    Article  Google Scholar 

  34. 34.

    M. G. Konovalov, Methods of Acaptive Information Processing and Their Applications (IPI RAN, Moscow, 2007) [in Russian].

    Google Scholar 

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Correspondence to M. G. Konovalov.

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Original Russian Text © M.G. Konovalov, Yu.E. Malashenko, I.A. Nazarova, 2011, published in Izvestiya Akademii Nauk. Teoriya i Sistemy Upravleniya, 2011, No. 2, pp. 43–61.

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Konovalov, M.G., Malashenko, Y.E. & Nazarova, I.A. Job control in heterogeneous computing systems. J. Comput. Syst. Sci. Int. 50, 220–237 (2011).

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  • Adaptive Strategy
  • System Science International
  • Batch Size
  • Static Strategy
  • Note Comp