An Idle Compute Cycle Prediction Service for Computational Grids

  • Suntae Hwang
  • Eun-Jin Im
  • Karpjoo Jeong
  • Hyoungwoo Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3036)


The utilization of idle compute cycles has been known as most promising and cost-effective way to build a large scale high performance computing system, but not widely used because of the lack of effective idleness prediction techniques. In this paper, we argue PCs at university computer labs have a great potential for the utilization of idle CPU cycles, and propose two techniques for predicting idle cycles of those PCs: heuristic and statistical. Based on these techniques, we present the design and implementation of an idle compute cycle prediction service for computational grids. Our experimental results show that the utilization of idle compute cycles is a viable approach to cost-effective large scale computational grids.


  1. 1.
    Bilmes, J.: What HMMs Can Do. UWEE Technical Report UWEETR-2002-0003, University of Washington (January 2002)Google Scholar
  2. 2.
    Czajkowski, K., Foster, I., Kesselman, C.: Resource co-allocation in computational grids. In: The Eighth IEEE International Symposium on High Performance Distributed Computing (August 1999)Google Scholar
  3. 3.
    Foster, I., Kesselman, C.: Globus: A Toolkit-based Grid Architecture. In: Foster, I., Kesselman, C. (eds.) The Grid: Blueprint for a New Computing Infrastructure, pp. 259–278. Morgan Kaufmann, San Francisco (1999)Google Scholar
  4. 4.
    Foster, I., Kesselman, C., Lee, C., Lindell, R., Nahrstedt, K.: A distributed resource management architecture that supports advance reservations and co-allocation. In: International Workshop on Quality of Service (1999)Google Scholar
  5. 5.
    Hwang, S., Jeong, K., Im, E.-J., Woo, C., Hahn, K.-S., Kim, M., Lee, S.: An Analysis of Idle CPU Cycles at University Computer Labs. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003. LNCS, vol. 2667, pp. 733–741. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Liu, C., Yang, L., Foster, I., Angulo, D.: Design and evaluation of a resource selection framework for Grid applications. In: Proc. of 11th IEEE Symposium on High Performance Distributed Computing (July 2002)Google Scholar
  7. 7.
    Subramani, V., Kettimuthu, R., Srinivasan, S., Sadayappan, P.: Distributed job scheduling on computational grids using multiple simultaneous requests. In: Proc. of 11th IEEE Symp. on High Performance Distributed Computing (July 2002)Google Scholar
  8. 8.
    Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. IEEE Trans. Informat. Theory IT-13, 260–269 (1967)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Suntae Hwang
    • 1
  • Eun-Jin Im
    • 1
  • Karpjoo Jeong
    • 2
  • Hyoungwoo Park
    • 3
  1. 1.School of Computer ScienceKookmin UniversitySeoulKorea
  2. 2.College of Information and CommunicationKonkuk UniversitySeoulKorea
  3. 3.Supercomputing CenterKISTIDaejonKorea

Personalised recommendations