SDN‐based fault‐tolerant on‐demand and in‐advance bandwidth reservation in data center interconnects
Bibliographic record
Abstract
Summary Geographically distributed data centers are interconnected through provisioned dedicated WAN links, realized by circuit/wavelength–switching that support large‐scale data transfer between data centers. These dedicated WAN links are typically shared by multiple services through on‐demand and in‐advance resource reservations, resulting in varying bandwidth availability in future time periods. Such an inter‐data center network provides a dynamic and virtualized environment when augmented with cloud infrastructure supporting end‐host migration. In such an environment, dynamically provisioned network resources are recognized as extremely useful capabilities for many types of network services. However, the existing approaches to in‐advance reservation services provide limited reservation capabilities, eg, limited connections over links returned by the traceroute over traditional IP‐based networks. Moreover, most existing approaches do not address fault tolerance in the event of node or link failures and do not handle end‐host migrations; thus, they do not provide a reliability guarantee for in‐advance reservation frameworks. In this paper, we propose using multiple paths to increase bandwidth usage in the WAN links between data centers when a single path does not provide the requested bandwidth. Emulation‐based evaluations of the proposed path computation show a higher reservation acceptance rate compared to state‐of‐art reservation frameworks, and such computed paths can be configured with a limited number of static forwarding rules on switches. Our prototype provides the RESTful Web service interface for link‐fail and end‐host migration event management and reroutes paths for all the affected reservations.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| Open science | 0.005 | 0.001 |
| 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".