Prediction and preemptive control of network congestion in distributed real-time environment
Bibliographic record
Abstract
Due to ever increasing demand for network capacity, the congestion problem is inflating. Congestion results in queuing within the network, packet loss and increased delays. It should be controlled to increase the system throughput and quality of service. The existing congestion control approaches such as source throttling and re-routing focus on controlling congestion after it has already happened. However, it is much more desirable to predict future congestion based on the current state and historical data, so that efficient controlling techniques can be applied to prevent congestion from happening in future. We have proposed a Neural Network Prediction-based routing (NNPR) protocol to predict as well as control the network traffic in distributed real time environment. A distributed real time transaction processing simulator (DRTTPS) has been used as the test-bed. For predictions, multi-step neural network model is developed in SPSS Modeler, which predicts congestion in future. ADAPA (Adaptive Decision and Predictive Analytics) scoring engine has been used for real-time scoring. An ADAPA wrapper calls the prediction model through web services and predicts the congestion in real-time. Once predicted results are obtained, messages are re-routed to prevent congestion. To compare our proposed work with existing techniques, two routing protocols are also implements "" Dijkstra's Shortest Path (DSP) and Routing Information Protocol (RIP). The main metric used to analyze the performance of our protocol is the percentage of transactions which complete before their deadline. The NNPR protocol is analyzed with various simulation runs having parameters both inside and outside the neural network input training range. Various parameters which can cause congestion were studied. These include bandwidth, worksize, latency, max active transactions, mean arrival time and update percentage. Through experimentation, it is observed that NNPR consistently outperforms DSP and RIP for all congestion loads. --Leaves [i]-ii.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".