Evaluating CRoS-NDN: a comparative performance analysis of a controller-based routing scheme for named-data networking
Bibliographic record
Abstract
Abstract The huge amount of content names available in Named-Data Networking (NDN) challenges both the required routing table size and the techniques for locating and forwarding information. Content copies and content mobility exacerbate the scalability challenge to reach content in the new locations. We present and analyze the performance of a proposed Controller-based Routing Scheme, named CRoS-NDN, which preserves NDN features using the same interest and data packets. CRoS-NDN supports content mobility and provides fast content recovery from copies that do not belong to the consumer-producer path because it splits identity from location without incurring FIB size explosion or supposing prefix aggregation. It provides features similar to Content Distribution Networks (CDN) in NDN, and improves the routing efficiency. We compare our proposal with similar routing protocols and derive analytical expressions for lower-bound efficiency and upper-bound latency. We also conduct extensive simulations to evaluate results in data delivery efficiency and delay. The results show the robust behavior of the proposed scheme achieving the best efficiency and delay performance for a wide range of scenarios. Furthermore, CRoS-NDN results in low use of processing time and memory for a growing number of prefixes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".