Multi-site Scheduling with Multiple Job Reservations and Forecasting Methods

  • Maria A. Ioannidou
  • Helen D. Karatza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4330)


Most previous research on job scheduling for multi-site distributed systems does not take into consideration behavioral trends when applying a scheduling method. In this paper, we address the scheduling of parallel jobs in a multi-site environment, where each site has a homogeneous cluster of non-dedicated processors where users submit jobs to be executed locally, while at the same time, external parallel jobs are submitted to a meta-scheduler. We use collected load data to model the performance trends that each site exhibits in order to predict load values via time-series analysis and then perform scheduling based on the predicted values.


Instantaneous Load Global Scheduler Smoothing Constant Heterogeneous Distribute Computing System Load Prediction Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sabin, G., Kettimuthu, R., Rajan, A., Sadayappan, P.: Scheduling of Parallel Jobs in a Heterogeneous Multi-Site Environment. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2003. LNCS, vol. 2862, pp. 87–104. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Strazdins, P., Uhlmann, J.: A Comparison of Local and Gang Scheduling on a Beowulf Cluster. In: Proceedings of 2004 IEEE International Conference on Cluster Computing, San Diego, CA, September 20-23, pp. 55–62. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  3. 3.
    Ernemann, C., Hamscher, V., Schwiegelshohn, U., Yahyapour, R.: On advantages of grid computing for parallel job scheduling. In: Proceedings of 2nd IEEE International Symposium on Cluster Computing and the Grid (CC-GRID 2002), Berlin, Germany, pp. 39–46 (2002)Google Scholar
  4. 4.
    Subramani, V., Kettimuthu, R., Srinivasan, S., Sadayappan, P.: Distributed Job Scheduling on Computational Grids using Multiple Simultaneous Requests. In: Proc. of 11-th IEEE Symposium on High Performance Distributed Computing (HPDC 2002) (July 2002)Google Scholar
  5. 5.
    Kwok, Y.-K.: On exploiting Heterogeneity for cluster based parallel multithreading using task duplication. The Journal of Supercomputing 25(1), 63–72 (2003)MATHCrossRefGoogle Scholar
  6. 6.
    Ernemann, C., Hamscher, V., Yahyapour, R., Streit, A.: Enhanced algorithms for multi-site scheduling. In: Proceedings of 3rd International Workshop Grid 2002, in conjunction with Supercomputing 2002, Baltimore, MD, USA, November 2002, pp. 219–231 (2002)Google Scholar
  7. 7.
    Kafil, M., Ahmad, I.: Optimal task assignment in heterogeneous distributed computing systems. IEEE Concurrency 6(3), 42–51 (1998)CrossRefGoogle Scholar
  8. 8.
    Real, R., Yamin, A., da Silva, L., Frainer, G., Augustin, I., Barbosa, J., Geyer, C.: Re-source scheduling on grid: handling uncertainty. In: Proceedings of the Fourth International Workshop on Grid Computing (GRID 2003), IEEE, Los Alamitos (2003)Google Scholar
  9. 9.
    Schopf, J.M., Berman, F.: Stochastic Scheduling. In: Conference on High Performance Networking and Computing. In: Proceedings of the 1999 ACM/IEEE conference on Supercomputing, vol. 48 (1999)Google Scholar
  10. 10.
    Mitzenmacher, M.: How useful is old information. IEEE Transactions on Parallel and Distributed Systems 11(1), 6–20 (2000)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Kishimoto, Y., Ichikawa, S.: An execution-time estimation model for heterogeneous clusters. In: Proceedings of the 18th International Parallel and Distributed Processing Sympo-sium (IPDPS 2004). IEEE, Los Alamitos (2004)Google Scholar
  12. 12.
    Hoogenboom, P., Lepreau, J.: Computer System Performance Proble Detection Using Time Series Models. In: Proceedings of the USENIX Summer 1993 Technical Conference, Cincinnati, Ohio, June 21-25 (1993)Google Scholar
  13. 13.
    Karatza, H.D., Hilzer, R.C.: Scheduling Sequential Jobs and Gangs in a Distributed Server System. In: Proceedings of the 5th EUROSIM Congress on Modelling and Simulation (Special Session on Modelling and Simulation of Distributed Systems and Networks), September 06-10, ESIEE Paris, Scit Descartes, Marne la Valle, FRANCE, EUROSIM-FRANCOSIM-ARGESIM, pp. 17–22 (2004)Google Scholar
  14. 14.
    Mitzenmacher, M., Vocking, B.: The Asymptotics of Selecting the Shortest of Two, Improved, Allerton99Google Scholar
  15. 15.
    Karatza, H.D.: Performance Analysis of Gang Scheduling in a Partitionable Parallel System. In: Proceedings of 20th European Conference on Modeling and Simulation, Bonn, Sankt Augustin, Germany, May 28-31, pp. 699–704 (2006)Google Scholar
  16. 16.
    Law, A., Kelton, D.: Simulation Modeling and Analysis, 2nd edn. McGraw-Hill, Inc., New York (1991)Google Scholar
  17. 17.
    Karatza, H.D.: Gang Scheduling in a Distributed System under Processor Failures and Time-varying Gang Size. In: Proceedings of the 9th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS 2003), San Juan, Puerto Rico, May 28-30, pp. 330–336. IEEE Computer Society Press, Los Alamitos (2003)CrossRefGoogle Scholar
  18. 18.
    Karatza, H.D.: Scheduling Gangs in a Distributed System. International Journal of Simulation: Systems, Science Technology, UK Simulation Society 7(1), 15–22 (2006)Google Scholar
  19. 19.
    Wei, W.W.S.: Time Series Analysis - Univariate and Multivariate Methods, 1st edn. Addison-Wesley, Inc., Redwood City (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Maria A. Ioannidou
    • 1
  • Helen D. Karatza
    • 1
  1. 1.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece

Personalised recommendations