Multiuser prefetching with queuing prioritization in heterogeneous wireless systems
Why this work is in the frame
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Bibliographic record
Abstract
We study the performance of a multi-user prefetching strategy in a two-tier heterogeneous wireless network. A predictive framework was previously introduced for mobility-aware document prefetching to enhance the experience of a mobile user roaming between heterogeneous wireless access networks. However, an undesirable effect of multiple prefetching users is the potential for system instability due to the racing behavior between document access delay and user prefetch quantity. This phenomenon is particularly acute in the heterogeneous environment. We propose to alleviate the system traffic load through optimizing a prefetch thresholding algorithm, accounting for server queuing prioritization. We evaluate the performance of the proposed algorithm through numerical analysis and simulation. We show that stability can be maintained even under heavy usage, providing both the same scalability as a non-prefetching system and the performance gains associated with prefetching.
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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 | 0.000 | 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 | 0.000 |
| 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