FCTrees: A Front-Coded Family of Compressed Tree-Based FIB Structures for NDN Routers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it