On Tackling Virtual Data Center Embedding Problem
Bibliographic record
Abstract
Virtualizing data center networks has been considered a feasible alternative to satisfy the requirements of advanced cloud services. Proper mapping of virtual data center (VDC) resources to their physical counterparts, also known as virtual data center embedding, can impact the revenue of cloud providers. Similar to virtual networks, the problem of mapping virtual requests to physical infrastructures is known to be NP- hard. Although some proposals have come up with heuristics to cope with the complexity of the embedding process focusing on virtual machine placement, these solutions ignore the correlation among other data resources, such as switches and storage. In this paper, we propose a new embedding solution for data centers that, in addition to virtual machine placement, explicitly considers the relation between switches and links, allows multiple resources of the same request to be mapped to a single physical resource, and reduces resource fragmentation in terms of CPU. Simulations show that our solution results in high acceptance ratio of VDC requests, improves utilization of the physical substrate, and generates increased revenue for infrastructure providers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".