Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the growing adoption of Software Defined Networking (SDN) technology, there is a compelling need for an SDN emulator that can facilitate experimenting with new SDN solutions. Unfortunately, Mininet, the de facto standard emulator for software defined networks, fails to scale with network size and traffic volume. To address these limitations, we developed Distributed OpenFlow Testbed (DOT), a highly scalable emulator for SDN. It can emulate large SDN deployments by distributing the workload over a cluster of compute nodes. Moreover, DOT can emulate a wider range of network services compared to other publicly available SDN emulators and simulators. Our demonstration will illustrate several features of DOT including: (i) how easy it is to setup the emulator, (ii) how to deploy a topology using a single configuration file, (iii) how to run a connectivity test to ensure that the emulated network is properly deployed, and (iv) how to control and monitor the emulated components from a centralized location. We will also showcase DOT by emulating two applications: (i) policy based traffic steering through middleboxes and (ii) traffic monitoring.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.003 |
| 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