Minimizing the provisioning delay in the cloud network: Benefits, overheads and challenges
Bibliographic record
Abstract
In the cloud computing era, virtualized data centers are expected to host most of the cloud services such as computation, storage and multimedia applications. Cloud services are expected to be transported over the Internet backbone based on anycast/manycast paradigms between the users and data centers. In this paper, we present an optimization model which aims at reconfiguring the cloud network topology so that the delay of cloud service provisioning is minimized without disrupting the service quality of regular Internet services. We compare the performance of the proposed model to the delay performance of an optimization model which aims at minimizing the operational expenditure of the operator. Through numerical results, we show that the proposed optimization model is capable of assuring minimum delay guarantee for the traffic demands destined to/from the data centers, as well as the traffic demands destined to/from the core nodes of the cloud network. Furthermore, we study the overheads and challenges of delay minimized reconfiguration of the cloud network. Numerical results confirm that minimum delay objective does not introduce significant overhead to the data centers in terms of operational expenditure, namely power consumption. On the other hand, we show that the increase in the power consumption of the network equipment in the cloud backbone arises as an important challenge of the presented optimization model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".