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Related Experiment Videos

A network model for wide area access to structural information

D J Dailey1, K Eno, G L Zick

  • 1Dept. Electrical Engineering, University of Washington, Seattle 98195.

Proceedings. Symposium on Computer Applications in Medical Care
|January 1, 1993
PubMed
Summary
This summary is machine-generated.

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Predicting structural information client performance is crucial for remote medical data access. A model using ping time can forecast network transfer times, aiding in system planning and deployment.

Area of Science:

  • Medical Imaging
  • Biotechnology
  • Network Performance

Background:

  • Advances in medical imaging and biotechnology generate vast amounts of structural information.
  • High Performance Computing and Communication Initiative (HPCC) enables wide area network delivery of this data.
  • Network latency significantly impacts the usability of remote structural information clients.

Purpose of the Study:

  • To develop a predictive model for structural information client performance.
  • To assess the utility of ping time as a network performance metric.
  • To facilitate efficient deployment and network planning for remote access to medical structural data.

Main Methods:

  • Utilized ping time, a network measurement, to model client-server performance.

Related Experiment Videos

  • Analyzed the relationship between ping time and data transfer time for large files.
  • Collected preliminary data to validate the predictive model.

    • Main Results:

    • Preliminary findings indicate a linear correlation between ping time and data transfer time.
    • The proposed model shows potential for predicting structural information client performance.
    • Ping time is a viable, unobtrusive metric for assessing network conditions.

    Conclusions:

    • A ping time-based model can predict structural information client performance.
    • This model can reduce the costs and time associated with client installation and testing.
    • The model supports strategic network improvements for enhanced remote medical data access.