Traffic optimization in anonymous networks
Bibliographic record
Abstract
Anonymous communication networks, such as Tor, are facing big challenge how to deliver content to users in low latency and with no interruption. The latency issues were caused by increased amount of transferred data and low-bandwidth nodes in Tor network. Those are limiting overall circuit capacity providing to users. Conflux, the Tor plugin, is improving effort and decreasing latency time by creating multipath within Tor circuits. Conflux is doing dynamic traffic-splitting and load-balancing through multipath to improve throughput and avoid bottlenecks in Tor circuits. Our solution is focusing to analyze and modification network flows and sessions in Tor network. As an output of problem's analysis we're proposing possibilities to improve Conflux's performance by its modification and deploy to the Tor network. The paper describes solution implementation, setup and configuration Tor client and Tor exit node. We're explaining necessary modifications that need to be provided on Tor components. Our solution achieved average improvement versus Conflux more than 20% decrease of download time in various file size.
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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 | 0.001 | 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 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".