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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".