Dimensioning backbone networks for multi-site data centers: Exploiting anycast routing for resilience
Bibliographic record
Abstract
In the current era of big data, applications increasingly rely on powerful computing infrastructure residing in large data centers (DCs), often adopting cloud computing technology. Clearly, this necessitates efficient and resilient networking infrastructure to connect the users of these applications with the data centers hosting them. In this paper, we focus on backbone network infrastructure on large geographical scales (i.e., the so-called wide area networks), which typically adopts optical network technology. In particular, we study the problem of dimensioning such backbone networks: what bandwidth should each of the links provide for the traffic, originating at known sources, to reach the data centers? And possibly even: how many such DCs should we deploy, and at what locations? More concretely, we summarize our recent work that essentially addresses the following fundamental research questions: (1) Does the anycast routing strategy influence the amount of required network resources? (2) Can we exploit anycast routing for resilience purposes, i.e., relocate to a different DC under failure conditions, to reduce resource capacity requirements? (3) Is it advantageous to change anycast request destinations from one DC location to the other, from one time period to the next, if service requests vary over time?
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".