Bibliographic record
Abstract
Named data networking (NDN) is a content-centric future Internet architecture that uses routable content names instead of IP addresses to achieve location-independent forwarding. Nevertheless, NDN's design is limited to offering hosted applications a simple content pull mechanism. As a result, increased complexity is needed in developing applications that require more sophisticated content delivery functionalities (e.g., push, publish/subscribe, streaming, generalized forwarding, and dynamic content naming). In this paper, we introduce a novel Enhanced NDN (ENDN) architecture that offers an extensible catalog of content delivery services (e.g., adaptive forwarding, customized monitoring, and in-network caching control) that can be programmed in the data plane using customizable P4 programs. More precisely, the proposed architecture allows hosted applications to associate their content namespaces with a set of services offered by the ENDN control plane. The controller then configures the data plane, which is comprised of two main modules: the enhanced packet processing and the forwarding logic modules. The former parses the packets and queries the enhanced content-based forwarding tables to generate a set of metadata fields used by P4 functions. The latter module is a novel P4 target architecture that executes these P4 functions on the arriving packets. The new architecture extends existing P4 models to overcome their limitations with respect to processing string-based content names. It also allows running independent P4 functions in isolation, thus enabling P4 code run-time pluggability. Experimental results demonstrate the ability of ENDN to achieve network efficiency with low latency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.210 | 0.212 |
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".