Evaluating chord over a hierarchical MANET
Bibliographic record
Abstract
Hierarchical network architectures are widely deployed to reduce routing overheads and increase scalability. In our work, we are interested in deploying a P2P overlay in ultimately large-scale Mobile Ad-Hoc Networks (MANETs). We therefore study the performance of such an approach, creating a number of hierarchical network configurations and deploy Chord over them. The clusters are MANETs, running OLSR locally. Each cluster has a gateway, and the gateways are interconnected through a backbone that deploys flooding. As we increase the number of clusters, we keep the number of nodes in the Chord overlay constant and run simulations in OMNeT++ to evaluate the performance of Chord (GET and PUT success ratio, latency). Our results show that overall the performance of the P2P overlay deteriorates as we increase the number of clusters. One of the main reasons is that the backbone carried more and more of the overlay maintenance and lookup traffic, becoming a performance bottleneck. We therefore conclude that deploying a more efficient routing protocol in the backbone, in contrast with the basic flooding approach, may be required to significantly improve the performance of a P2P overlay over hierarchical MANETs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".