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An integrated approach towards aggressive state-tracking migration for maximizing performance benefit in distributed computing

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Abstract

This paper presents a new state-tracking migration scheme that is integrated with aggressive reservation strategies such as immediate restart, greedy backfilling and selective preemption. The main contribution of this paper is an analysis of the effects of three techniques that can be used beyond the conventional migration schemes. Our simulation results suggest that state-tracking migration with selective preemption entirely outperforms the others. We also observe that the overall performance of immediate restart strategy combining to migration can be stably maintained under various job lifetime distributions. Moreover, it is found that performance would be improved by fitting jobs ruled by the immediate restart strategy rather than queued jobs into the void-intervals under the state-tracking migration scheme.

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References

  1. Zomaya, A.Y., Teh, Y.-H.: Observations on using genetic algorithms for dynamic load-balancing. IEEE Trans. Parallel Distrib. Syst. 12(9), 899–911 (2001)

    Article  Google Scholar 

  2. Buyya, R., Abramson, D., Giddy, J.: Nimrod/g: an architecture for a resource management and scheduling system in a global computational grid. In: Proc. of the HPC ASIA 2000, pp. 283–289 (2000)

    Google Scholar 

  3. Xiong, Y., Vandenhoute, M., Cankaya, H.C.: Control architecture in optical burst-switched WDM networks. IEEE J. Sel. Areas Commun. 18, 1838–1851 (2000)

    Article  Google Scholar 

  4. Iizuka, M., Sakuta, M., Nishino, Y., Sasase, I.: A scheduling algorithm minimizing voids generated by arriving bursts in optical burst switched WDM network. In: Proc. of IEEE GLOBECOM 2000, pp. 2736–2740 (2000)

    Google Scholar 

  5. Zhao, W., Ramamritham, K., Stankovic, J.A.: Preemptive scheduling under time and resource constraints. IEEE Trans. Comput. C-36(8), 949–960 (1987)

    Article  Google Scholar 

  6. Chiang, S.H., Vernon, M.K.: Production job scheduling for parallel shared memory systems. In: Proc. of Int. Parallel and Distributed Processing Symp. (2002)

    Google Scholar 

  7. Parsons, E.W., Sevcik, K.C.: Implementing multiprocessor scheduling disciplines. In: Feitelson, D.G., Rudolph, L. (eds.) Proc. of Job Scheduling Strategies for Parallel Processing (IPPS’97), pp. 166–192. Springer, London (1997)

    Chapter  Google Scholar 

  8. Ruscio, J.F., Heffner, M.A., Varadarajan, S.: DejaVu: transparent user-level checkpointing migration and recovery for distributed systems. In: IEEE Int. Parallel and Distributed Processing Symp. (2007)

  9. Baker, K.R., Trietsch, D.: Principles of Sequencing and Scheduling. Wiley, New York (2009)

    Book  MATH  Google Scholar 

  10. Moon, Y.-H., Youn, C.-H.: Integrated approach towards adaptive state-tracking job migration for maximising performance benefit. IEE Electron. Lett. 46(25), 1659–1661 (2010)

    Article  Google Scholar 

  11. Song, S., Hwang, K., Kwok, Y.: Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling. IEEE Trans. Comput. 55(6), 703–719 (2006)

    Article  Google Scholar 

  12. Rodríguez, G., Pardo, X.C., Martín, M.J., González, P.: Performance evaluation of an application-level checkpointing solution on grids. Future Gener. Comput. Syst. 26(7), 1012–1023 (2010)

    Article  Google Scholar 

  13. Mu’alem, A.W., Feitelson, D.G.: Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling. IEEE Trans. Parallel Distrib. Syst. 12(6), 529–543 (2001)

    Article  Google Scholar 

  14. Gonzalez Pico, C.A., Wainwright, R.L.: Dynamic scheduling of computer tasks using genetic algorithms. In: Evolutionary computation, 1994. IEEE World Congress on Computational Intelligence, Proc. of the First IEEE Conference on, vol. 2, pp. 829–833 (1994)

    Chapter  Google Scholar 

  15. Logs of Real Parallel Workloads: http://www.cs.huji.ac.il/labs/parallel/workload/logs.html (2011)

  16. Albert, E., Arenas, P., Gnaim, S., Herraiz, I., Puebla, G.: Comparing cost functions in resource analysis. In: Proc. of the Int. Conference on Foundational and Practical Aspects of Resource Analysis (FOPARA) (2009)

    Google Scholar 

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Acknowledgements

This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2011-0020522).

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Authors and Affiliations

  1. Software Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea

    Yong-Hyuk Moon

  2. Department of Information and Communications Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea

    Yong-Hyuk Moon

  3. Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea

    Chan-Hyun Youn

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  1. Yong-Hyuk Moon
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Correspondence to Yong-Hyuk Moon.

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Moon, YH., Youn, CH. An integrated approach towards aggressive state-tracking migration for maximizing performance benefit in distributed computing. Cluster Comput 16, 367–378 (2013). https://doi.org/10.1007/s10586-011-0197-0

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