Bibliographic record
Abstract
Software Defined Networking (SDN) has been emerging to be a new paradigm of datacenter network architecture. As SDN in the datacenter environment continues to grow in scale and complexity, one of the challenges to the SDN researchers and developers is to verify a SDN design before deployment. Although there are several existing tools developed with the goal to fill this need, they are unfortunately either lacking the seamless porting ability from a simulated environment to a real deployment (simulation-based approach) or suffering from the scalability issue (emulation-based approach). In this paper, we propose ScalaSEM, a system leveraging the advantages of both simulation and emulation methods to valid the SDN design. ScalaSEM establishes a high-fidelity environment with the real-world OpenFlow-based communication channel and abstracts networks with higher yet accurate-enough level. Through two usage scenarios and the comparison with the state-of-the-art solutions, we demonstrate that ScalaSEM provides the validating solution to the SDN design which can scale to the network with the scale of tens of thousands hosts and thousands of machines, and imposes no necessary to modify the implementation of the SDN design to move between validation and deployment environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".