Network Data Center Traffic Predictive Model Analysis Based on Machine Learning
Bibliographic record
Abstract
Due to the transient nature and uncertainty of traffic produced by applications and services, data center networks have a lot of challenges. As a response, networking as a domain is continually evolving to maintain the exponential growth in network traffic. The primary objective of this paper is to predict the network traffic before it impacts the system's performance. This paper first describes existing Machine Learning (ML) applications in telecommunications and then lists the most prominent difficulties and probable remedies for implementing them. We tried to implement different ML algorithms to predict the network traffic like Gradient Boosting (GB), Random Forest (RF), K-Nearest Neighbor (KNN), Adaptive Boosting (AB), Neural Network (NN), Decision Tree (DT), and Support Vector Machines (SVM) with different sub-parameters for predicting network traffic. Relying on a sequential dataset, we create the corresponding ML environment and present a comparison table of Mean square error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) for each model. The simulation results show that the AB and GB are the best-fitted models with performance matrix parameters like MSE 0.000 and RMSE 0.002 and 0.011, respectively. The orange tool is used to stimulate the predictive models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".