FCTree: A Space Efficient FIB Data Structure for NDN Routers
Bibliographic record
Abstract
Named Data Networking (NDN) is a future Internet architecture that replaces IP addresses with namespaces of contents that are searchable at the network layer. A challenging task for NDN routers is to manage forwarding-information bases (FIBs) that store next-hop routes to contents using their stored usually long names or name prefixes. In this paper, we propose FCTree, a compressed FIB data structure that significantly reduces the required storage space at the router and can efficiently meet the demands of having routes that are orders of magnitude larger than IP-based ones in conventional routing tables. FCTree employs a localized front-coding compression to buckets containing partitions of the routes. The top routes in these buckets are then organized in B-ary self-balancing trees. By adjusting the size of the buckets, the router can reach an optimal tradeoff between the latency of the longest prefix matching (LPM) operation and the FIB storage space. In addition, in contrast to existing hash and bloom-filter based solutions, the proposed FCTree structure can significantly reduce the latency required for range and wildcard searches (e.g., for latency sensitive streaming applications or network-layer search engines) where up to k routes are returned if they are prefixed by a requested name. Performance evaluation results demonstrate the significant space savings achieved by FCTree compared to traditional hash-based FIBs.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".