Prediction and preemptive control of network congestion in distributed real-time environment
Bibliographic record
Abstract
Network congestion must be managed to increase system throughput and quality of service. The existing congestion control approaches such as source throttling and rerouting focus on controlling congestion after it has already occurred. We propose a multistep Neural Network Prediction-based Routing (NNPR) protocol to predict as well as control network traffic before congestion actually happens. A distributed real time transaction processing simulator serves as the test-bed and a cloud-based scoring engine has been used to obtain results in real-time; messages are then rerouted to prevent congestion. Various parameters which can cause congestion are studied. These include bandwidth, work size, latency, max active transactions, mean arrival time and update percentage. The performance of proposed protocol is compared with existing protocols. Through experimentation, it is demonstrated that NNPR consistently provides superior performance for all congestion loads.
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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 | 0.000 | 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 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".