Skip to main content
SpringerLink
Log in

Greedy scheduling with custom-made objectives

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

We present a methodology to automatically generate an online job scheduling method for a custom-made objective and real workloads. The scheduling problem comprises independent parallel jobs and parallel identical machines and occurs in Massively Parallel Processing systems and computational Grids. The system administrator defines the scheduling objective that may consider job properties and priorities of users or user groups. Our scheduling method combines a Greedy scheduling algorithm with the dynamic sorting of the waiting queue. This sorting algorithm uses a criterion that is modifiable by a set of parameters. Finding good parameter settings for the sorting criterion is viewed as a nonlinear optimization problem which is solved with the help of Evolution Strategies. We evaluate our scheduling method with real workload data and compare it to approximated optimal offline solutions and to the online results of the standard EASY backfill algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aarts, E. H. L., Korst, J. H. M., & van Laarhoven, P. J. M. (1997). Simulated annealing. In Local search in combinatorial optimization (pp. 91–120). New York: Wiley.

    Google Scholar 

  • Bäck, T., & Schwefel, H.-P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1), 1–23.

    Article  Google Scholar 

  • Balicki, J., & Kitowski, Z. (2001). Multicriteria evolutionary algorithm with tabu search for task assignment. In E. Zitzler, K. Deb, L. Thiele, C. A. Coello & D. Corne (Eds.), Lecture notes in computer science (LNCS) : Vol. 1993. Proceedings of the 1st international conference on evolutionary multi-criterion optimization (EMO01) (pp. 373–384). Berlin: Springer.

    Chapter  Google Scholar 

  • Becchetti, L., & Leonardi, S. (2004). Nonclairvoyant scheduling to minimize the total flow time on single and parallel machines. Journal of the ACM, 51(4), 517–539.

    Article  Google Scholar 

  • Beyer, H.-G., & Schwefel, H.-P. (2002). Evolution strategies—a comprehensive introduction. Natural Computing, 1(1), 3–52.

    Article  Google Scholar 

  • Burke, E. K., McCollum, B., Meisels, A., Petrovic, S., & Qu, R. (2007). Graph-based hyper-heuristic for timetabling problems. European Journal of Operational Research, 176(1), 177–192.

    Article  Google Scholar 

  • Cirne, W., & Berman, F. (2001). A comprehensive model of the supercomputer workload. In Proceedings of the 4th workshop on workload characterization (pp. 140–148). IEEE Press.

  • Deb, K., Agrawal, S., Pratab, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In M. Schoenauer et al. (Eds.), Lecture notes in computer science (LNCS) : Vol. 1917. Parallel Problem Solving from Nature VI (pp. 849–858). Berlin: Springer.

    Chapter  Google Scholar 

  • Downey, A. B., & Feitelson, D. G. (1999). The elusive goal of workload characterization. Performance Evaluation Review, 26(4), 14–29.

    Article  Google Scholar 

  • Ernemann, C., Schwiegelshohn, U., Emmerich, M., Schönemann, L., & Beume, N. (2005). Scheduling algorithm development based on complex owner defined objectives. Technical Report 191/05, SFB 531, CI-Report, University of Dortmund.

  • Ernemann, C., Song, B., & Yahyapour, R. (2003). Scaling of workload traces. In D. G. Feitelson, L. Rudolph, & U. Schwiegelshohn (Eds.), Lecture notes in computer science (LNCS) : Vol. 2862. Proceedings of job scheduling strategies for parallel processing (JSSPP03) (pp. 166–183). Berlin: Springer.

    Chapter  Google Scholar 

  • Feitelson, D. G. (2002). Workload modeling for performance evaluation. In M. C. Calzarossa & S. Tucci (Eds.), Lecture notes in computer science (LNCS) : Vol. 2459. Performance evaluation of complex systems: techniques and tools (pp. 114–141). Berlin: Springer.

    Chapter  Google Scholar 

  • Feitelson, D. G. (2007). Parallel workload archive. http://www.cs.huji.ac.il/labs/parallel/workload/.

  • Feitelson, D. G., & Nitzberg, B. (1995). Job characteristics of a production parallel scientific workload on the NASA Ames iPSC/860. In D. G. Feitelson & L. Rudolph (Eds.), Lecture notes in computer science (LNCS) : Vol. 949. Proceedings of job scheduling strategies for parallel processing (JSSPP95) (pp. 337–360). Berlin: Springer.

    Google Scholar 

  • Feitelson, D. G., Rudolph, L., & Schwiegelshohn, U. (2004). Parallel job scheduling—a status report. In Lecture notes in computer science (LNCS) : Vol. 3277. Proceedings of job scheduling strategies for parallel processing (JSSPP04) (pp. 1–16). Berlin: Springer.

    Chapter  Google Scholar 

  • Feitelson, D. G., Rudolph, L., Schwiegelshohn, U., Sevcik, K. C., & Wong, P. (1997). Theory and practice in parallel job scheduling. In Lecture notes in computer science (LNCS) : Vol. 1291. Proceedings of job scheduling strategies for parallel processing (JSSPP97) (pp. 1–34). Berlin: Springer.

    Google Scholar 

  • Feitelson, D. G., & Weil, A. M. (1998). Utilization and predictability in scheduling the IBM SP2 with backfilling. In Proceedings of the 12th international parallel processing symposium and the 9th symposium on parallel and distributed processing (pp. 542–547). IEEE Computer Society Press.

  • Foster, I., Kesselman, C., & Tuecke, S. (2001). The anatomy of the grid: enabling scalable virtual organizations. International Journal of High Performance Computing Applications, 15, 200–222.

    Article  Google Scholar 

  • Franke, C., Lepping, J., & Schwiegelshohn, U. (2006). On advantages of scheduling using genetic fuzzy systems. In E. Frachtenberg & U. Schwiegelshohn (Eds.), Lecture notes in computer science (LNCS) : Vol. 4376. Proc. 12th workshop on job scheduling strategies for parallel processing (JSSPP) (pp. 68–93). Berlin: Springer.

    Chapter  Google Scholar 

  • Garey, M., & Graham, R. L. (1975). Bounds for multiprocessor scheduling with resource constraints. SIAM Journal on Computing, 4(2), 187–200.

    Article  Google Scholar 

  • Hertz, A., Taillard, E., & de Werra, D. (1997). In Local search in combinatorial optimization (pp. 121–136). New York: Wiley.

    Google Scholar 

  • Krallmann, J., Schwiegelshohn, U., & Yahyapour, R. (1999). On the design and evaluation of job scheduling systems. In D. G. Feitelson & L. Rudolph (Eds.), Lecture notes in computer science (LNCS) : Vol. 1659. Proceedings of job scheduling strategies for parallel processing (JSSPP99). Berlin: Springer.

    Chapter  Google Scholar 

  • Lee, C. B., Schwartzman, Y., Hardy, J., & Snavely, A. (2005). Are user runtime estimates inherently inaccurate?. In Lecture notes in computer science (LNCS) : Vol. 3277. Proceedings of job scheduling strategies for parallel processing (JSSPP05) (pp. 253–263). Berlin: Springer.

    Google Scholar 

  • Lee, C. B., & Snavely, A. E. (2007). Precise and realistic utility functions for user-centric performance analysis of schedulers. In HPDC ’07: Proceedings of the 16th international symposium on High performance distributed computing (pp. 107–116).

  • Lifka, D. A. (1995). The ANL/IBM SP scheduling system. In Lecture notes in computer science (LNCS) : Vol. 949. Proceedings of job scheduling strategies for parallel processing (JSSPP95) (pp. 295–303). Berlin: Springer.

    Google Scholar 

  • Lublin, U., & Feitelson, D. G. (2003). The workload on parallel supercomputers: modeling the characteristics of rigid jobs. Journal of Parallel and Distributed Computing, 63(11), 1105–1122.

    Article  Google Scholar 

  • Pinedo, M. (2002). Scheduling: theory, algorithms, and systems (2nd ed.). New York: Prentice-Hall.

    Google Scholar 

  • Schwiegelshohn, U., & Yahyapour, R. (2000). Fairness in parallel job scheduling. Journal of Scheduling, 3(5), 297–320.

    Article  Google Scholar 

  • Schwiegelshohn, U., & Yahyapour, R. (2003). In Grid resource management—state of the art and future trends (pp. 41–52). Dordrecht: Kluwer.

    Google Scholar 

  • Sleator, D., & Tarjan, R. E. (1985). Amortized efficiency of list update and paging rules. Communications of the ACM, 28, 202–208.

    Article  Google Scholar 

  • Tsafrir, D. (2006). Modeling, evaluating, and improving the performance of supercomputer scheduling. PhD thesis, School of Computer Science and Engineering, the Hebrew University, Jerusalem, Israel. Technical Report 2006-78.

Download references

Author information

Authors and Affiliations

  1. ABB Schweiz AG, 5405, Baden, Switzerland

    Carsten Franke

  2. Robotics Research Institute, Technische Universität Dortmund, 44227, Dortmund, Germany

    Joachim Lepping & Uwe Schwiegelshohn

Authors
  1. Carsten Franke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joachim Lepping
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Uwe Schwiegelshohn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Uwe Schwiegelshohn.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Franke, C., Lepping, J. & Schwiegelshohn, U. Greedy scheduling with custom-made objectives. Ann Oper Res 180, 145–164 (2010). https://doi.org/10.1007/s10479-008-0491-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-008-0491-2

Keywords

Search

Navigation