Performance Evaluation for Web Applications with Web Caching in a Distributed Wireless System Using Opnet™
Why this work is in the frame
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Bibliographic record
Abstract
We analyze and evaluate the performance of a distributed wireless system with an added Web cache server. We use OPNET™ simulation tools to perform two experiments. In the first one, keeping the number of wireless clients constant, data traffic at the remote server running Web applications is analyzed as the cache hit rate of the caching device is varied. We noticed that the load at the Web server is improved by having increased caching capabilities at the cache server. Interestingly, it is observed that the traffic improvement is the best at a certain range of caching. The second experiment investigates the pattern of data dropped, and the delay at the remote Web server as the number of wireless clients is varied at a fixed cache hit rate. The results from this study are expected to help us understand the importance of cache servers while planning and designing a distributed wireless system with many clients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it