Network-Aware Scalable Application: End to End Performance
The middleware components described are integrated into an adaptive system where the application's features are selected based on network weather conditions. A learner selects an anatomy image resource, such as "Lung and Pleura" on the course's iAnatomy web page. The click on the data link automatically launches the Remote Stereo Viewer client program on the learner's computer, and links to the corresponding program on the server. The lung images begin displaying on the learner's screen.
In the background, the application sends its network performance data to the LogServ program, and queries (CorQ) the performance data base about any changes it needs to make to its transmission settings. The current network weather (bandwidth, delay, packet loss, jitter, multicast setup) is checked against the information stored in the LogServ database. If CorQ suggests that current network weather requires changes to the application settings, the scalable application selects some of the many possibilities in a predetermined priority order.
The possible changes we have tested include a change in image resolution, frame transmission rate, and in the rate of interleaving of prefetch data according to the application's speed in decoding compressed image data. Experiments conducted by us suggest that responsiveness of the application is one of the most important characteristics sought by the user. Other scalable features, such as image resolution, can be modified as long as they remain adequate for the learning task.