A value-based cache replacement approach for Information-Centric Networks
Why this work is in the frame
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Bibliographic record
Abstract
Information-Centric Networks (ICNs) represent a content-based model which addresses user's requests regardless of the content's location or the nature of its original publisher. The performance of an ICN is highly dependent on replicating the content across the caches of a multitude of nodes in the network. Given the high data turnover rates of contemporary applications and the finite nature of caching space, efficient caching algorithms play a crucial role in determining which data item can be safely dropped in order to accommodate for more important items. In this paper, we present a value-based cache replacement approach that executes a Least Valuable First (LVF) policy. Our approach employs a utility function that uses delay, popularity and age parameters to determine which item to drop from the cache. We present simulation results comparing our approach to other dominant cache replacement policies under varying conditions such as data popularity, in-network cache ratio and connectivity degree. Results show that our approach outperforms in terms of time-to-hit, hit rate, in-network delay and data publisher load.
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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 | 0.001 |
| 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