A scalable model for multi-period virtual network mapping for resilient multi-site data centers
Bibliographic record
Abstract
In the currently dominant cloud computing paradigm, applications are being served in data centers (DCs) which are connected to high capacity optical networks. For cost efficiency reasons, in both DC and optical network domains, virtualization of the physical hardware is exploited. In a DC, it means that multiple so-called virtual machines (VMs) are being hosted on the same physical server. Similarly, the network is partitioned into separate virtual networks, thus providing isolation between distinct virtual network operators (VNOs). Thus, the problem of virtual network mapping arises: how to decide which physical resources to allocate for a particular virtual network? In this paper, we study that problem in the context of cloud computing with multiple DC sites. This introduces additional flexibility, due to the anycast routing principle: we have the freedom to decide at what particular DC location to serve a particular application. We can exploit this choice to minimize the required resources when solving the virtual network mapping problem. This paper builds on our earlier work and solves a resilient virtual network mapping problem that optimally decides on the mapping of both network and data center resources, considering time-varying traffic conditions and protecting against possible failures of both network and DC resources. We consider the so-called VNO resilience scheme: rerouting under failure conditions is provided in the virtual network layer. To minimize physical resource capacity requirements, we allow reuse of both network and DC resources: we can reuse the same resources for the rerouting under failure scenarios that are assumed not to occur simultaneously. Since we also protect against DC failures, we allocate backup DC resources, and account for synchronization between primary and backup DCs. To deal with the time variations in the volume and geographical pattern of the application traffic, we investigate the potential benefits (in terms of overall resource capacity requirements) of reconfiguring the virtual network mapping from one period to the next. Previously, we developed a model to solve the multi-period traffic case one step at a time: given the virtual network mapping fore time t, we determine the (possibly changed) mapping for t + 1. Compared to that previous work, we now (i) define a truly multi-period model path formulation exploiting column generation, and (ii) demonstrate its scalability on a nation-wide network with hourly varying traffic.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".