On Deploying and Executing Data-Intensive Code on SMart Autonomous Storage (SmAS) Disks

  • V. V. Dimakopoulos
  • A. Kinalis
  • E. Pitoura
  • I. Tsoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1884)


There is an increasing demand for storage capacity and storage throughput, driven largely by new data types such as video data and satellite images as well as by the growing use of the Internet and the web that generate and transmit rapidly evolving datasets. Thus, there is a need for storage architectures that scale the processing power with the growing size of the datasets. In this paper, we present the SMAS system that employs network attached disks with processing capabilities. In the SMAS system, users can deploy and execute code at the disk. Application code is written in a stream-based language that enforces code security and bounds the codeś memory requirements. The SMAS operating system at the disk provides basic support for process scheduling and memory management. We present an initial implementation of the system and report performance results that validate our approach for data-intensive applications.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    A. Acharya, M. Uysal, and J. Saltz. Active disks: programming model, algorithms and evaluation. In ASPLOS’ 98, 8th Conf. on Archit. Support for Programming Languages and Operationg Systems, pages 212–217, San Jose, California, Oct. 1998.Google Scholar
  2. 2.
    ANSI. Information systems-small computer system interface-2 (scsi-2). Technical report, ANSI X3.131-1994, 1994.Google Scholar
  3. 3.
    Axis Communications. Cd-rom servers, white paper. Technical report, 1996.Google Scholar
  4. 4.
    D. J. DeWitt and P. Hawthorn. A performance evaluation of database machine architectures. In VLDB’ 81, September 1981.Google Scholar
  5. 5.
    G. Gibson, D. Nagle, K. Amiri, F. Chang, E. Feinberg, H. Gobioff, C. Lee, B. Ozceri, E. Riedel, D. Rochberg, and J. Zelenka. File server scaling with network-attached secure disks. In Sigmetrics’ 97, Seattle, Washington, June 1997.Google Scholar
  6. 6.
    J. Gray. What happens when processors are infinitely fast and storage is free? In 5th Workshop on I/O in Parallel and Distributed Systems, November 1997.Google Scholar
  7. 7.
    K. Keeton, D. A. Patterson, and J. M. Hellerstein. A case for intelligent disks (idisks). SIGMOD Record, 27(3):42–52, July 1998.Google Scholar
  8. 8.
    George Lawton. Storage technology takes central state. IEEE Computer, 32(11), November 1999.Google Scholar
  9. 9.
    E. Riedel, G. Gibson, and C. Faloutsos. Active storage for large-scale data mining and multimedia. In VLDB’ 98, pages 62–73, New York, USA, August 1998.Google Scholar
  10. 10.
    M. Rodriguez and N. Roussopoulos. Automatic deployment of application-specific metadata and code in mocha. In 7th Conference on Extending Database Technology (EDBT), March 2000.Google Scholar
  11. 11.
    A. S. Tanenbaum and A. S. Woodhull. Operating Systems: Design and Implementation. 2nd ed. Prentice Hall, 1997.Google Scholar
  12. 12.
    M. Uysal, A. Acharya, and J. Saltz. An evaluation of architectural alternatives for rapidly growing datasets: active disks, clusters, smps. Technical report, Dept. of Computer Science, University of California, Santa Barbara, Technical Report RCS 98-27, October 1998.Google Scholar
  13. 13.
    M. Uysal, A. Acharya, and J. Saltz. Evaluation of active disks for decision support databases. In HPCA, 2000.Google Scholar
  14. 14.
    R. Winter and K. Auerbach. The big time: the 1998 vldb survey. Database Programming and design, 11(8), August 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • V. V. Dimakopoulos
    • 1
  • A. Kinalis
    • 1
  • E. Pitoura
    • 1
  • I. Tsoulos
    • 1
  1. 1.Computer Science DepartmentUniversity of IoanninaIoanninaGreece

Personalised recommendations