Evaluating Bandwidth Management Techniques on Mikrotik Routers: A Multiple Linear Regression Approach
Bibliographic record
Abstract
For computer networks, bandwidth management is essential.Bandwidth management is a strategy used in network administration to try and provide fair and acceptable network performance.Researchers have monitored the effectiveness of bandwidth management methods including Per Connection Queue (PCQ), Random Early Detection (RED), and First In First Out (FIFO) schemes using the Simple Network Management Protocol (SNMP) protocol.(1) To ascertain which of the PCQ, RED, and FIFO bandwidth management techniques performs best is the main goal of this study.( 2) Is able to forecast how well bandwidth management techniques will function on a network, providing a guide for putting the best techniques into practice.This study included a variety of methodologies, including testing, design, implementation, and analysis.The PCQ method performs better than the RED or FIFO methods when monitored using the SNMP protocol and the Cacti application as an interface.Predictions using multiple linear regression on bandwidth management methods are used to estimate the CPU and memory performance of the PCQ, RED, and FIFO Mikrotik routers that are implemented on the network constructed by researchers.Compared to the prediction accuracy on CPU performance, which has a total average error value of 0.9204, the memory performance prediction accuracy using multiple linear regression is more accurate, with a total average error value of 0.0315.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".