Studying the Influence of Network-Aware Grid Scheduling on the Performance Received by Users

  • Luis Tomás
  • Agustín Caminero
  • Blanca Caminero
  • Carmen Carrión
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5331)


Grid computing is the key enabling technology to aggregate geographically distributed resources in the context of a particular application. As Grids are extremely distributed systems, requirements on the communication network should also be taken into account when performing usual tasks such as scheduling, migrating or monitoring of jobs. Note that users, services, and data need to communicate with each other over networks, thus the network should be used in an efficient and fault-tolerant way. There are Grid schedulers that consider the network when performing their tasks, but the way they have been implemented does not allow easy extensions. Thus, they are not suitable to be modified and try different scheduling approaches. The authors have extended the GridWay metascheduler to perform scheduling considering the network status. This is the first step in order to proceed with more complicated and efficient scheduling and reservation processes. In this work, the extension has been evaluated by means of a testbed, in which users simultaneously submit different jobs with different frequencies to GridWay. Results presented here show that the response time perceived by Grid users is reduced when data on network performance are considered in the job scheduling process.


Grid metascheduling network Quality of Service 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure, 2nd edn. Morgan Kaufmann, San Francisco (2003)Google Scholar
  2. 2.
  3. 3.
    Al-Ali, R., et al.: Network QoS Provision for Distributed Grid Applications. Intl. Journal of Simulations Systems, Science and Technology, Special Issue on Grid Performance and Dependability 5(5) (December 2004)Google Scholar
  4. 4.
    Roy, A.: End-to-End Quality of Service for High-End Applications. PhD thesis, Dept. of Computer Science, University of Chicago (2001)Google Scholar
  5. 5.
    Marchese, F.T., Brajkovska, N.: Fostering asynchronous collaborative visualization. In: Proc. of the 11th Intl. Conference on Information Visualization, Washington DC, USA (2007)Google Scholar
  6. 6.
    Kurowski, K., Ludwiczak, B., Nabezyski, J., Oleksiak, A., Pukacki, J.: Dynamic grid scheduling with job migration and rescheduling in the gridlab resource management system. Scientific Programming 12(4), 263–273 (2004)CrossRefGoogle Scholar
  7. 7.
    Venugopal, S., Buyya, R., Winton, L.J.: A Grid service broker for scheduling e-Science applications on global data Grids. Concurrency and Computation: Practice and Experience 18(6), 685–699 (2006)CrossRefGoogle Scholar
  8. 8.
    Wei, X., Ding, Z., Yuan, S., Hou, C., Li, H.: CSF4: A WSRF compliant meta-scheduler. In: Proc. of the 2006 Intl. Conference on Grid Computing & Applications, GCA 2006, Las Vegas, Nevada, USA (June 2006)Google Scholar
  9. 9.
    Huedo, E., Montero, R.S., Llorente, I.M.: A modular meta-scheduling architecture for interfacing with pre-ws and ws grid resource management services. Future Generation Computing Systems 23(2), 252–261 (2007)CrossRefGoogle Scholar
  10. 10.
    Waldrich, O., Wieder, P., Ziegler, W.: A Meta-scheduling Service for Co-allocating Arbitrary Types of Resources. In: Wyrzykowski, R., Dongarra, J., Meyer, N., Waśniewski, J. (eds.) PPAM 2005. LNCS, vol. 3911, pp. 782–791. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Interactive European Grid Project (2008),
  12. 12.
    Tomás, L., Caminero, A., Caminero, B., Carrión, C.: Grid Metascheduling Using Network Information: A Proof-of-Concept Implementation. Technical Report DIAB-08-04-2, Dept. of Computing Systems. University of Castilla La Mancha, Spain (April 2008)Google Scholar
  13. 13.
    Foster, I.: Globus toolkit version 4: Software for service-oriented systems 21(4), 513–520 (2006)Google Scholar
  14. 14.
    Grid(Lab) Resource Management (2008),
  15. 15.
    GridBus Project (2008),
  16. 16.
    Wolski, R., Spring, N.T., Hayes, J.: The network weather service: A distributed resource performance forecasting service for metacomputing. Future Generation Computer Systems 15(5–6), 757–768 (1999)CrossRefGoogle Scholar
  17. 17.
    McClatchey, R., Anjum, A., Stockinger, H., Ali, A., Willers, I., Thomas, M.: Scheduling in Data Intensive and Network Aware (DIANA) Grid Environments. ArXiv e-prints 707 (July 2007)Google Scholar
  18. 18.
    Fuentes, A., Huedo, E., Moreno, R., Martin-Llorente, I.: A grid scheduling algorithm considering dynamic interconnecting network. In: The 3rd Cracow Grid WorkShop (2003)Google Scholar
  19. 19.
    NLANR/DAST: Iperf - The TCP/UDP Bandwidth Measurement Tool (2008),
  20. 20.
    GridWay Team: GridWay 5.2 Documentation: User Guide. Distributed Systems Architecture Group, Universidad Complutense de Madrid (2007)Google Scholar
  21. 21.
    Massie, M.L., Chun, B.N., Culler, D.E.: The Ganglia distributed monitoring system: design, implementation, and experience. Parallel Computing 30(5-6), 817–840 (2004)CrossRefGoogle Scholar
  22. 22.
    Caminero, A., Rana, O., Caminero, B., Carrión, C.: An Autonomic Network-Aware Scheduling Architecture for Grid Computing. In: Proc. of the 5th Intl. Workshop on Middleware for Grid Computing (MGC), Newport Beach, USA (2007)Google Scholar
  23. 23.
    Fitzgerald, S., Foster, I., Kesselman, C., von Laszewski, G., Smith, W., Tuecke, S.: A directory service for configuring high-performance distributed computations. In: Proc. 6th Symposium on High Performance Distributed Computing (HPDC), Portland, USA (1997)Google Scholar
  24. 24.
    Sohail, S., Pham, K.B., Nguyen, R., Jha, S.: Bandwidth Broker Implementation: Circa-Complete and Integrable. Technical report, School of Computer Science and Engineering, The University of New South Wales (2003)Google Scholar
  25. 25.
    Frumkin, M., Van der Wijngaart, R.: Nas grid benchmarks: a tool for grid space exploration. In: Proceedings of 10th IEEE International Symposium on High Performance Distributed Computing (2001)Google Scholar
  26. 26.
    Vetro, A., Christopoulos, C., Sun, H.: Video transcoding architectures and techniques: an overview. IEEE Signal Processing Magazine 20(2), 18–29 (2003)CrossRefGoogle Scholar
  27. 27.
    The NAS Parallel Benchmark (2008),

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Luis Tomás
    • 1
  • Agustín Caminero
    • 1
  • Blanca Caminero
    • 1
  • Carmen Carrión
    • 1
  1. 1.Instituto de Investigación en Informática de Albacete (I3A)Universidad de Castilla-La ManchaAlbaceteSpain

Personalised recommendations