Mapping Cooperating GRID Applications by Affinity for Resource Characteristics

  • Ki-Hyung Kim
  • Sang-Ryoul Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3397)


The Computational Grid, distributed and heterogeneous collections of computers in the Internet, has been considered a promising platform for the deployment of various high-performance computing applications. One of the crucial issues in the Grid is how to discover, select and map possible Grid resources in the Internet for meeting given applications. The general problem of statically mapping tasks to nodes has been shown to be NP-complete. In this paper, we propose a mapping algorithm for cooperating Grid applications by the affinity for the resources, named as MACA. The proposed algorithm utilizes the general affinity of Grid applications for certain resource characteristics such as CPU speeds, network bandwidth, and input/output handling capability. To show the effectiveness of the proposed mapping algorithm, we compare the performance of the algorithm with some previous mapping algorithms by simulation. The simulation results show that the algorithm could effectively utilize the affinity of Grid applications and shows good performance.


Mapping Algorithm Grid Resource Total Completion Time Candidate Node Grid Application 
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.
    Foster, I., Kesselman, C.: Computational Grids. In: The Grid: Blueprint for a New Computing Infrastructure, ch. 2. Morgan-Kaufman, San Francisco (1999)Google Scholar
  2. 2.
    Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International J. Supercomputer Applications 15(3), 200–222 (2001)CrossRefGoogle Scholar
  3. 3.
    Allen, G., Angulo, D., Foster, I., Lanfermann, G., Liu, C., Radke, T., Seidel, E., Shalf, J.: The Cactus Worm: Experiments with Dynamic Resource Discovery and Allocation in a Grid Environment. International Journal of High-Performance Computing Applications 15(4) (2001)Google Scholar
  4. 4.
    Ripeanu, M., Iamnitchi, A., Foster, I.: Cactus Application: Performance Predictions in Grid Environments. In: EuroPar 2001, Manchester, UK (August 2001)Google Scholar
  5. 5.
    Liu, C., Yang, L., Foster, I., Angulo, D.: Design and evaluation of a resource selection framework for Grid applications. In: Proceedings of 11th IEEE International Symposium on High Performance Distributed Computing (HPDC-11), pp. 63–72 (2002)Google Scholar
  6. 6.
    Takefusa, A., Matsuoka, S., Casanova, H., Berman, F.: A Study of Deadline Scheduling for Client-Server Systems on the Computational Grid. In: Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing (HPDC), pp. 406–415 (2001)Google Scholar
  7. 7.
    Dail, H., Sievert, O., Berman, F., Casanova, H., YarKhan, A., Vadhiyar, S., Dongarra, J., Liu, C., Yang, L., Angulo, D., Foster, I.: Scheduling in the Grid Application Development Software Project. In: Resource Management in the Grid, ch  1. Kluwer, Dordrecht (2003)Google Scholar
  8. 8.
    Vadhiyar, S., Dongarra, J.: A Metascheduler For The Grid. In: HPDC 2002, 11th IEEE International Symposium on High Performance Distributed Computing, Edinburgh, Scotland, pp. 343–351. IEEE Computer Society, Los Alamitos (2002)CrossRefGoogle Scholar
  9. 9.
    Braun, T., Siegel, H., Beck, N., Boloni, L., Maheswaran, M., Reuther, A., Robertson, J., Theys, M., Yao, B.: A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. Journal of Parallel and Distributed Computing 61, 810–837 (2001)CrossRefGoogle Scholar
  10. 10.
    Czajkowski, K., et al.: A Resource Management Architecture for Metacomputing Systems. In: Proc. IPPS/SPDP 1998 Workshop on Job Scheduling Strategies for Parallel Processing (1998)Google Scholar
  11. 11.
    Foster, I., Kesselman, C.: Globus: A Toolkit-Based Grid Architecture. In: The Grid: Blueprint for a New Computing Infrastructure, pp. 259–278. Morgan Kaufmann, San Francisco (1999)Google Scholar
  12. 12.
    Chapin, J., et al.: Resource Management in Legion. In: Proceedings of the 5th Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 1999 (1999)Google Scholar
  13. 13.
    Dail, H., Obertelli, G., Berman, F.: Application-Aware Scheduling of a Magnetohydrodynamics Applications in the Legion Metasystem. In: Proceedings of the 9th Heterogeneous Computing Workshop (2000)Google Scholar
  14. 14.
    Berman, F., Wolski, R.: The AppLeS project: A Status Report. In: Proceedings of the 8th NEC Research Symposium (1997)Google Scholar
  15. 15.
    Allen, G., et al.: Cactus Tools for Grid Applications. Cluster Computing, 179–188 (2001)Google Scholar
  16. 16.
    Czajkowski, K., Fitzgerald, S., Foster, I., Kesselman, C.: Grid Information Services for Distributed Resource Sharing. In: 10th IEEE International Symposium on High Performance Distributed Computing, pp. 181–184. IEEE Press, Los Alamitos (2001)CrossRefGoogle Scholar
  17. 17.
    Fitzgerald, S., Foster, I., Kesselman, C., Laszewski, G., Smith, W., Tuecke, S.: A Directory Service for Configuring High-performance Distributed Computations. In: Proc. 6th IEEE Symp. on High Performance Distributed Computing, pp. 365–375 (1997)Google Scholar
  18. 18.
    Wolski, R.: Dynamically Forecasting Network Performance Using the Network Weather Service. Journal of Cluster Computing (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ki-Hyung Kim
    • 1
  • Sang-Ryoul Han
    • 1
  1. 1.Dept. of Computer EngYeungnam UniversityGyungsan, GyungbukKorea

Personalised recommendations