Software Defined Survivable Optical Interconnects for Data Centers
Bibliographic record
Abstract
Extending the notion of Software Defined Network (SDN) from packet switching in Layers 2 and 3 to circuit switching in transport layer for service providers is a promising scenario to meet the high burstiness and high bandwidth requirements. For service providers to have a multilayer, multi-domain controller, which can provide automated controller based restoration and protection even in unprotected links in the multi-administrative domain can evince as a promising solution. With this approach, service providers can assure to provide guaranteed Service Level Agreement (SLA) maintenance with optimum use of bandwidth, high availability and reduced error performance. In this paper, we propose a novel Software Defined Survivable Optical Interconnects (SDSOI) architecture for Data Centers (DCs). The feasibility of this architecture is demonstrated using Open Network Operating System (ONOS) as the SDN controller and building a day-night scheduling application on top of it. This application guarantees on-demand bandwidth as well as optimum bandwidth utilization in accord with the calendar. We also demonstrate a survivability technique for these interconnects.
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 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".