Dedicated Protection for Survivable Virtual Network Embedding
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
Network virtualization is enabling infrastructure providers (InPs) to offer new services to service providers (SPs). InPs are usually bound by service level agreements to ensure various levels of resource availability for different SPs' virtual networks (VNs). They provision redundant backup resources while embedding an SP's VN request to conform to the SLAs during physical failures in the infrastructure. An extreme backup resource provisioning is to reserve a mutually exclusive backup of each element in an SP's VN request. Such dedicated protection scheme can enable an InP to ensure fast VN recovery, thus, providing high uptime guarantee to the SPs. In this paper, we study the 1 + 1-Protected Virtual Network Embedding (1 + 1-ProViNE) problem. We propose Dedicated Protection for Virtual Network Embedding (DRONE), a suite of solutions to the 1 + 1-ProViNE problem. DRONE includes an integer linear programming formulation for optimal solution (OPT-DRONE) and a heuristic (FAST-DRONE) to tackle the computational complexity of the optimal solution. Trace driven simulations show that FAST-DRONE allocates only 14.3% extra backup resources on average compared to the optimal solution, while executing 200-1200 times faster. Simulation results also show that FAST-DRONE can accept four times more VN requests on average compared to the state-of-the-art solution for providing dedicated protection to VNs.
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 | 0.000 | 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 | 0.001 | 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 it