PayLess: A low cost network monitoring framework for Software Defined Networks
Why this work is in the frame
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Bibliographic record
Abstract
Software Defined Networking promises to simplify network management tasks by separating the control plane (a central controller) from the data plane (switches). OpenFlow has emerged as the de facto standard for communication between the controller and switches. Apart from providing flow control and communication interfaces, OpenFlow provides a flow level statistics collection mechanism from the data plane. It exposes a high level interface for per flow and aggregate statistics collection. Network applications can use this high level interface to monitor network status without being concerned about the low level details. In order to keep the switch design simple, this statistics collection mechanism is implemented as a pull-based service, i.e. network applications and in turn the controller has to periodically query the switches about flow statistics. The frequency of polling the switches determines monitoring accuracy and network overhead. In this paper, we focus on this trade-off between monitoring accuracy, timeliness and network overhead. We propose PayLess - a monitoring framework for SDN. PayLess provides a flexible RESTful API for flow statistics collection at different aggregation levels. It uses an adaptive statistics collection algorithm that delivers highly accurate information in real-time without incurring significant network overhead. We utilize the Floodlight controller's API to implement the proposed monitoring framework. The effectiveness of our solution is demonstrated through emulations in Mininet.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it