On the Delay Scaling Laws of Cache Networks
Bibliographic record
Abstract
The Internet is becoming more and more content-oriented. CDN (Content Distribution Networks) has been a popular architecture compatible with the current Internet, and a new revolutionary paradigm such as ICN (Information Centric Networking) has studied. One of the main components in both CDN and ICN is considering cache on network. Despite a surge of extensive use of cache in the current and future Internet architectures, analysis on the performance of general cache networks are still quite limited due to complex inter-plays among various components and thus analytical intractability. Due to mathematical tractability, we consider 'static' cache policies and study asymptotic delay performance of those policies in cache networks, in particular, focusing on the impact of heterogeneous content popularities and nodes' geographical 'importances' in caching policies. Furthermore, our simulation results suggest that they perform quite similarly as popular 'dynamic' policies such as LFU (Least-Frequently-Used) and LRU (Least-Recently-Used). We believe that our theoretical findings provide useful engineering implications such as when and how various factors have impact on caching performance.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".