SDN-Based Framework for Infrastructure as a Service Clouds
Bibliographic record
Abstract
Infrastructure as a service (IaaS) has attracted significant attention from cloud research communities. While compute and storage resource management has been developed and studied to a greater extent, the use of network resource management in the context of IaaS is still in its early stages. There is a need for a comprehensive virtualization framework capable of providing users with low-level network and compute resource control while improving underlying resource utilization. We propose a Software-Defined Networking (SDN) IaaS framework that explicitly integrates network virtualization, including computing and storage, into a cloud platform. Our proposed framework abstracts data-center compute and network resources into a virtualized pool of resources, links them to logically compose virtual networks, and performs automated configurations to serve IaaS requests from users. We designed and constructed an SDN test-bed to verify the operation of the proposed framework. We successfully demonstrated our system's ability to meet our design objectives: (i) providing high degree of control over connectivity and Quality of Service (QoS), (ii) fully automated service delivery, (iii) fast and low overhead resource provisioning. We evaluated the feasibility and scalability of the proposed framework using statistics gathered from its deployment.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".