PCPV: Pattern-based Cost-efficient Proactive VNF placement and chaining for value-added services in content delivery networks
Bibliographic record
Abstract
Value-added services (VASs) are an integral part of todays Content Delivery Networks (CDNs). They can be implemented as a chain of Virtual Network Functions (VNFs). These chains need to be placed in an efficient way in CDNs in order to optimize quality of service (QoS) for end-users(EUs) while minimizing cost for providers. We formulate the problem as an Integer Linear Program (ILP) and propose a Pattern-based Cost-efficient Proactive VNF placement and chaining (PCPV) algorithm. The objective is to find the optimal number, location and chaining of VNFs in such a manner that the cost is minimized while QoS is met. Apart from cost minimization, the support for large-scale CDNs with a large number of physical machines (servers) and EUs is an important feature of the proposed algorithm. Through simulations, the algorithm behavior for small-scale to large scale CDN networks is analyzed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 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".