Venice: Reliable virtual data center embedding in clouds
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
Cloud computing has become a cost-effective model for deploying online services in recent years. To improve the Quality-of-Service (QoS) of the provisioned services, recently a number of proposals have advocated to provision both guaranteed server and network resources in the form of Virtual Data Centers (VDCs). However, existing VDC scheduling algorithms have not fully considered the reliability aspect of the allocations in terms of (1) hardware failure characteristics on which the service is hosted, and (2) the impact of individual failures on service availability, given the dependencies among the virtual components. To address this limitation, in this paper we present a technique for computing VDC availability that considers heterogeneous hardware failure rates and dependencies among virtual components. We then propose Venice, an availability-aware VDC embedding framework for achieving high VDC availability and low operational costs. Experiments show Venice can significantly improve VDC availability while achieving higher income compared to availability-oblivious solutions.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| 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