Towards flexible, scalable and autonomic virtual tenant slices
Bibliographic record
Abstract
Multi-tenant flexible, scalable and autonomic virtual networks isolation has long been a goal of the network research and industrial community. For today's Software-Defined Networking (SDN) platforms, providing cloud tenants requirements for scalability, elasticity, and transparency is far from straightforward. SDN programmers typically enforce strict and inflexible traffic isolation resorting to low-level encapsulations mechanisms which help and facilitate network programmer reasoning about their complex slices behavior. In this paper, we propose SD-NMS, a novel software-defined architecture overcoming SDN and encapsulation techniques limitations. SD-NMS lifts several network virtualization roadblocks by combining these two separate approaches into an unified design. SD-NMS design leverages the benefits of SDN to provide Layer 2 (L2) isolation coupled with network overlay protocols with simple and flexible virtual tenant slices abstractions. This yields a network virtualization architecture that is both flexible, scalable and secure on one side, and self-manageable on the other. The experiment results showed that the proposed design offers negligible overhead and guarantees the network performance while achieving the desired isolation goals.
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".