Bibliographic record
Abstract
Network management and Quality of Service (QoS) support are becoming more challenging with the increase in network traffic, size, and service requirements. To meet these challenges, we need a programmable and intelligent framework for automated QoS support; static or threshold-based approaches are not adequate. We propose a software-defined and machine-learning-based intelligent QoS framework called PIQoS. PIQoS enables software-defined networking (SDN) controllers to effectively, efficiently, and autonomously react, in a vendor agnostic way, to changes in network links by (1) placing link failure detection and recovery in the data plane and (2) applying supervised learning methods to the tasks of dynamically detecting link failures and congestion and appropriately reconfiguring the network so that it can continue to provide the required QoS as link properties change over time. To test the performance of a system based on PIQoS, we performed extensive simulation experiments in Mininet, using real network topologies. We also studied the comparative performance of several supervised learning methods applied to our specific detection and correction problems to determine which methods are most appropriate for this domain. Our simulation results highlight the potential efficacy of the PIQoS framework when applied in real networks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".