Optimal caching for producer mobility support in Named Data Networks
Bibliographic record
Abstract
Named Data Networks (NDNs) offer a promising paradigm for the future Internet to cope with the growing demand for data. One of the main challenges in NDNs is how to support a seamless operation during mobility. In this paper, we investigate optimal caching for Producer mobility support and propose a scheme (named OpCacheMob) that exploits location predictors and data requests' patterns to cache the data proactively before handover occurs. In essence, OpCacheMob adopts the predicted future Interests, that will be sent to the mobile producers, and caches their data contents ahead. Thus, avoids Interest retransmission or redirection that increase the consumer's delay and decreases the network efficiency during producer's mobility. We provide a mathematical formulation for such caching problem that bounds both the cache update cost and the consumer delay while minimizing the total network overhead due to the change of content availability. OpCacheMob is then implemented in ndnSIM and evaluated against mainstream NDN mobility solutions. We demonstrate how the scheme can be used as a benchmark to measure the performance of other mobility schemes. In addition, a sensitivity analysis is presented to measure the impact of errors on the prediction gain of such solution.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".