EL2W: Extended Layer 2 Services for Bare-Metal Provisioning Over WAN
Bibliographic record
Abstract
Researchers are embracing deep learning in various interdisciplinary research domains, recognizing undeniable benefits offered by deep neural networks. However, in order to meet the substantial computational demands for processing deep learning models, researchers extensively rely on cloud servers. Nevertheless, the shared nature of cloud servers encourages research labs and facilities to establish private clouds, ensuring exclusive access to computational resources and safeguarding data privacy. Creating a private cloud from bare metal presents challenges with existing provisioning solutions. These solutions not only come with a set of complex installation and configuration steps but are also limited to a constrained local Ethernet broadcast domain for network loading, which may pose unforeseen difficulties and risks for researchers who do not specialize in computing. To address these issues, this paper introduces EL2W, Extended Layer 2 services to Wide area networks (WAN), a novel approach we developed following Infrastructure-as-Code (IaC) principles. EL2W aims to help automate the system installation procedure by reducing the repetitive configurations and setups using Infrastructure-as-Code based scripts and codes. In addition, EL2W can securely expand an Ethernet network’s logical and functional extent beyond the current physical limitations of Ethernet layer 2 networks. We describe the implementation and architecture of a remote bare metal provisioning system built upon secure extended layer 2 networks. Experimental results demonstrate the capability of EL2W for establishing a secure layer 2 connection to provide essential bare metal provisioning services, as well as the effectiveness of a local proxy cache server to reduce the operating system loading time.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".