Anycast (re)routing of multi-period traffic in dimensioning resilient backbone networks for multi-site data centers
Bibliographic record
Abstract
We consider the problem of dimensioning resilient backbone networks for cloud-like scenarios where demand is to be served at one among several candidate data centers (DCs), and where that demand varies over time, which we assume to be slotted. We thus consider multi-period traffic, for which we need to find routes to both a primary DC and a backup DC (in case the primary, or the network connection towards it, fails) and account also for synchronization traffic (following its own routes) between the two chosen DCs. We propose a path formulation and adopt a column generation approach: the (restricted) master problem (RMP) selects “configurations” to use for each demand in each of the time periods, while pricing problems (PPs) construct new, potentially cost-reducing configurations for a given demand. Our model allows for several PPs to be solved in parallel, and we demonstrate the time savings achieved by doing so. We compare several anycast (re)routing strategies, where we allow traffic that spans multiple periods to either (i) not be rerouted in different periods, (ii) only change the backup DC and routes, or (iii) freely change both primary and backup DC choices and routes towards them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".