JANUS: Design of a software-defined infrastructure manager and its network control architecture
Bibliographic record
Abstract
We present an integrated management architecture based on Software-Defined Infrastructure (SDI), an approach where an SDI Manager controls heterogeneous resources with a global view of the entire infrastructure. By exposing APIs on top of our SDI management system, we create a programmatic cloud environment where users can innovate, creating unique applications and services in a software-defined manner. In this paper we first provide an overview of the SDI manager, dubbed JANUS, that was developed and implemented in the SAVI testbed for application platforms. We focus on advanced software-defined network services that can be provided in this SDI environment. We present requirements that must be met by the SDI management system: Pro-active rule installation; Broadcast-free networking; VN creation and management; scalability and extensibility; and support for legacy networks. We describe the Network Control Module of the JANUS SDI Manager which was designed to meet the specified requirements. A key innovation that promotes scalability and performance is a FlowStore that tracks flows and cache rules in the SDN network.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".