Optimal Cache Budget Distribution for Hierarchical ICN Networks
Bibliographic record
Abstract
Caching facilities can be deployed by all or some of the ICN nodes on the path of delivering data items from a content source to users. However, some inconsistent conclusions have been made from different studies regarding the benefits of in-network caching. To investigate the benefits of in-network caching, we propose an analytical model that optimally distributes a total cache budget among the nodes of a given ICN network in an environment that does not follow the Independent Reference Model (IRM).The cache budget distribution problem is studied with respect to optimizing system-centric and user-centric metrics, using a small number of synthetic and realistic topologies as case studies. Our findings reveal the benefits of in-network caching as well as the optimal distribution of the cache budget in ICNs with respect to our selected objective function. Although the efficiency of in-network caching on user-centric metrics strongly depends on topologies and the strength of temporal locality, in-network caching is very helpful in optimizing the ISP-centric metrics for all network and traffic settings.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".