Diagnostic and troubleshooting of OpenFlow‐enabled switches using kernel and userspace traces
Bibliographic record
Abstract
Summary Although software‐defined networking (SDN) provides a flexible way to provision and control networks, it also makes network debugging and troubleshooting more complex. In SDN, the network is fully managed by software programs that increase flexibility and sophistication but are prone to bugs. Pinpointing those bugs is challenging because they can occur at multiple locations, such as the forwarding plane, the controller OS, and the network services running on top of the controller. Compared to functional bugs, performance bugs are particularly irritating due to their non‐failure semantics. They can lead to performance loss (reduced throughput, increased latency, and wasted resources) while maintaining the network connectivity. The literature reports several tools and techniques to diagnose SDN bugs, but, unfortunately, they are mostly ineffective against performance bugs. In this paper, we propose a novel monitoring and diagnostic framework capable of diagnosing performance bugs in the SDN data plane. The proposed tool works within Open vSwitch (OVS), a popular software switch, albeit it can easily be adapted to any OpenFlow switch. Tracing techniques are used to collect low‐level performance data from monitored switches. Our tool derives adapted performance metrics from kernel and userspace traces, and then displays them in time‐synchronized graphical views. These views provide valuable insights into OVS operation. They also enable practitioners to discover performance‐related issues and analyze their root causes. A few use cases are presented to demonstrate the efficiency of our tool in optimizing OVS performance and diagnosing its performance bugs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".