PayLess: A low cost network monitoring framework for Software Defined Networks
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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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".