Enhancing DHT-based object naming service architectures with geographic-awareness
Bibliographic record
Abstract
Existing Object Naming Service (ONS) architectures that are based on Distributed Hash Tables (DHT) are built on top of chord-like DHT networks, which are DHT P2P networks constructed by projecting the network nodes on a ring network and then adding long edges to each node to improve the lookup performance. A main weakness in these architectures is the lack of geographic awareness at the nodes. This paper proposes the enhancement of these architectures with geographic awareness using a technique, called Geographic-Aware Content Addressable Network (GCAN), that runs on top of any chord-like DHT network. GCAN uses the procedures of chord-like DHT networks as black-boxes. Thus, it requires only minimum additions to existing DHT-based ONS architectures. As a result, it inherits the scalability, reliability, and the maturity of chord-like DHT networks. DHT-based ONS architectures that are built with GCAN are guaranteed to have routing, join, leave complexities in O(log n), while the routing table size is also in Θ(log n) on average, where n is the number of the network nodes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".