Proactive Management of Service Instance Pools for Meeting Service Level Agreements

  • Kavitha Ranganathan
  • Asit Dan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3826)


Existing Grid schedulers focus on allocating resources to jobs as per the resource requirements expressed by end-users. This demands detailed knowledge of application behavior for different resource configurations on the part of end-users. Additionally, this model incurs significant delay in terms of the provisioning overhead for each request. In contrast, for interactive workloads, services are commonly pre-configured by an application server according to long-term steady-state requirements. In this paper, we propose a framework for bridging the gap between these two extremes. We target application services beyond simple interactive workloads, such as a parallel numeric application. In our approach, end users are shielded from lower-level resource configuration details and deal only with service metrics like average response time, expressed as SLAs. These SLAs are then translated into concrete resource allocation decisions. Since demand for a service fluctuates over time, static pre-configurations may not maximize utility of the common pool of resources. Our approach involves dynamic re-provisioning to achieve maximum utility, while accounting for overheads incurred during re-provisioning. We find that it is not always beneficial to re-provision resources according to perceived benefits and propose a model for calculating the optimal amount of re-provisioning for a particular scenario.


Service Level Agreement Service Type Average Response Time Service Instance Incremental Adaptation 
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.


  1. 1.
    Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration, Globus Project (2002)Google Scholar
  2. 2.
    Dan, A., Dumitrescu, C., Ripeanu, M.: Connecting Client Objectives with Resource Capabilities: An Essential Component for Grid Service Management Infrastructures. In: 2nd International Conference on Service Oriented Computing (ICSOC), New York, NY (November 2004)Google Scholar
  3. 3.
    Grimshaw, A.S., Wulf, W.A.: The Legion Vision of a Worldwide Virtual Computer. Communications of the ACM 40, 39–45 (1997)CrossRefGoogle Scholar
  4. 4.
    Foster, I.: The Grid: A New Infrastructure for 21st Century Science. Physics Today 55, 42–47 (2002)CrossRefGoogle Scholar
  5. 5.
    I.B.M Corporation. IBM LoadLeveler: User’s guide. Technical report, IBM (September 1993)Google Scholar
  6. 6.
    Ludwig, H., Keller, A., Dan, A., King, R.: A Service Level Agreement Language for Dynamic Electronic Services. In: Presented at 4th IEEE International Workshop on Advanced Issues of E-Commerce and Web-based Information Systems (WECWIS 2002), Newport Beach, California, USA (2002)Google Scholar
  7. 7.
    Raman, R., Livny, M., Solomon, M.: Matchmaking: Distributed Resource Management for High Throughput Computing. In: Proceedings of the Seventh IEEE International Symposium on High Performance Distributed Computing, Chicago, IL, July 28-31 (1998)Google Scholar
  8. 8.
    The Globus Resource Allocation and Management,
  9. 9.
    Liu, C., Yang, L., Foster, I., Angulo, D.: Design and Evaluation of a Resource Selection Framework for Grid Applications. HPDC, Edinburgh (July 2002)Google Scholar
  10. 10.
    Andrieux, A., Czajkowski, C., Dan, A., Keahey, K., Ludwig, H., Pruyne, J., Rofrano, J., Tuecke, S., Xu, M.: Web Services Agreement Specification (WS-Agreement). Version 1.1, Draft 20, June 6 (2004)Google Scholar
  11. 11.
    Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.): JSSPP 2004. LNCS, vol. 3277. Springer, Heidelberg (2005)Google Scholar
  12. 12.
    Smith, C.: Open Source Metascheduling for Virtual Organizations with the Community Scheduler Framework (CSF), White Paper, Platform Computing Inc.Google Scholar
  13. 13.
  14. 14.
    The Globus Resource Specification Language RSL v1.0,
  15. 15.
    The NorduGrid Project,
  16. 16.
    Gehring, J., Reinefeld, A.: MARS - a framework for minimizing the job execution time computing environment. Technical report, Paderborn Center for Parallel Computing (January 1995)Google Scholar
  17. 17.
    Allen, G., Angulo, D., Foster, I., Lanfermann, G., Liu, C.: The Cactus Worm: Experiments with dynamic resource discovery and allocation in a Grid environment. International Journal of High Performance Computing Applications 15(4) (January 2001)Google Scholar
  18. 18.
    Ranganathan, K., Dan, A.: Proactive management of Service Instance Pools for meeting Service Level Agreements, Technical Report, I.B.M - RC23723 (September 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kavitha Ranganathan
    • 1
  • Asit Dan
    • 1
  1. 1.IBM T J Watson Research CenterHawthorneUSA

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