Software Defined Network-Based Edge Cloud Resource Allocation Framework
Bibliographic record
Abstract
A growing number of user applications rely on the computing resources of edge cloud data centers (ECDCs). The efficient management of ECDC resources has a positive impact on the cloud service providers' profitability and on the stringent quality-of-service (QoS) requirements of user applications. This paper proposes a resource allocation framework of interconnected ECDCs using software-defined networking (SDN). SDN technology is used to ensure QoS and to efficiently embed user applications into the ECDCs. The proposed framework, called infrastructure as a service provisioning using SDN (IaaSP-SDN), includes the two-phase coordinated IaaS requests. Provisioning approach and a set of SDN management modules to set up accepted IaaS. The performance of IaaSP-SDN is evaluated in two steps: 1) a prototype is compared with generalized multi-protocol label switching (GMPLS) and 2) the impact of SDN controller physical location on the performance of the IaaSP-SDN framework is evaluated. The results illustrate that the SDN framework is more stable and scalable than GMPLS. We also show that SDN controller location attributes have a significant effect on the acceptance ratio of IaaS requests.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".