OUR: Optimal Update‐based Replacement policy for cache in wireless data access networks with optimal effective hits and bandwidth requirements
Bibliographic record
Abstract
ABSTRACT In mobile wireless data access networks, remote data access is expensive in terms of bandwidth consumption. An efficient caching scheme can reduce the amount of data transmission, hence, bandwidth consumption. However, an update event makes the associated cached data objects obsolete and useless for many applications. Data access frequency and update play a crucial role in deciding which data objects should be cached. Seemingly, frequently accessed but infrequently updated objects should have higher preference while preserving in the cache. Other objects should have lower preference or be evicted, or should not be cached at all, to accommodate higher‐preference objects. In this paper, we proposed Optimal Update‐based Replacement, a replacement or eviction scheme, for cache management in wireless data networks. To facilitate the replacement scheme, we also presented two enhanced cache access schemes, named Update‐based Poll‐Each‐Read and Update‐based Call‐Back. The proposed cache management schemes were supported with strong theoretical analysis. Both analysis and extensive simulation results were given to demonstrate that the proposed schemes guarantee optimal amount of data transmission by increasing the number of effective hits and outperform the popular Least Frequently Used scheme in terms of both effective hits and communication cost. Copyright © 2011 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".