Drawing the Cloud Map: Virtual Network Provisioning in Distributed Cloud Computing Data Centers
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
Efficient virtualization methodologies constitute the core of cloud computing data center implementation. Clients are attracted to the cloud model by its ability to scale the resources dynamically and the flexibility in payment options that it offers. However, performance hiccups may push them to go back to the buy-and-maintain model. Virtualization plays a key role in the synchronous management of the thousands of servers along with clients' data living on them. To achieve seamless virtualization, cloud providers require a system that performs the function of virtual network provisioning. This includes receiving the cloud client requests and allocating their computational and network resources in a way that guarantees the quality-of-service conditions for clients while maximizing the data center resource utilization and providers' revenue. We introduce a comprehensive system to solve the problem of virtual network mapping for a set of connection requests sent by cloud clients. Connections are collected in time intervals called windows. Consequently, node and link provisioning is performed. Different window size selection schemes are introduced and evaluated. Three schemes to prioritize connections are used, and their effect is assessed. Moreover, a technique dealing with connections spanning over more than a window is introduced. The proposed algorithm is compared with previous work well known in the literature. Simulation results show that the dynamic window size algorithm achieves cloud service providers' objectives in terms of generated revenue, served-connection ratio, resource utilization, and computational overhead. In addition, experimental results show that handling spanning connections independently improves the performance of the system.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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