FCTrees: A Front-Coded Family of Compressed Tree-Based FIB Structures for NDN Routers
Bibliographic record
Abstract
Named data networking (NDN) is a nascent vision for the future Internet that replaces IP addresses with content names searchable at the network layer. One challenging task for NDN routers is to manage huge forwarding information bases (FIBs) that store next-hop routes to contents. In this article, we propose a family of compressed FIB data structures that significantly reduce the required storage space within the NDN routers. Our first compressed FIB data structure is FCTree. FCTree employs a localized front-coding compression, that eliminates repeated prefixes, to buckets containing partitions of routes. These buckets are then organized in self-balancing trees to speed up the longest prefix match (LPM) operations. We propose two enhancements to FCTree, a statistically compressed FCTree (StFCTree) and a dictionary compressed FCTree (DiFCTree). Both StFCTree and DiFCTree achieve higher compression ratios for NDN FIBs and can be used for FIB updates or exchanges between the forwarding and control planes. Finally, we provide the control plane with several knobs that can be employed to achieve different target trade-offs between the lookup speed and the FIB size in each of these structures. Theoretical analysis along with experimental results demonstrate the significant space savings and performance achieved by the proposed schemes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".