Performance model for mapping processing tasks to OpenFlow switch resources
Bibliographic record
Abstract
In a recent effort to push forward the powerful concept of software defined networks, OpenFlow has gained a lot of popularity as a practical approach to split the data and the control planes by standardizing an open interface that allow remote software controllers to dictate the forwarding behavior of network devices. The latest flexible version 1.1 of OpenFlow is limited to software forwarding plane implementations. In order to deliver high performance, we implement an OpenFlow v1.1 hardware forwarding plane based on network processors. Delivering the optimal performance requires finding the optimal mapping of OpenFlow tasks to hardware resources which is already known as an NP-hard combinatorial problem. In our work, we propose a performance model that helps choose a better mapping without the burden of implementing and comparing all possible mappings on network processor. Our model assumes that the performance bottleneck of the hardware forwarding pipeline comes from the lookup tasks. This is generally the case of OpenFlow lookup tasks based on more than 14 tuple headers and requiring high latency external memories in order to provide a large number of possible flow entries. Our model, validated using real hardware implementations comparisons, shows that the lookup tasks that use the same external memories in the same pipeline are not worth parallelizing. In fact, mapping them to different parallel processing elements will only increase the response time of the lookup memory which will slow down the forwarding pipeline.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".