Online VNF Placement and Chaining for Value-added Services in Content Delivery Networks
Bibliographic record
Abstract
Value-added Services (VASs) (e.g. dynamic site acceleration, media management) play a critical role in Content Delivery Networks (CDNs). Network Functions Virtualization (NFV) enables the agile provisioning of VASs. In NFV settings, VASs are provisioned as ordered sets of Virtual Network Functions (VNFs), forming VNF-Forwarding Graphs (VNF-FG) which are deployed in the CDN infrastructure. The CDN VAS VNF-FGs have a specific characteristic: they have one end-point (corresponding to the content server) that is unknown, prior to their placement. The proposals for CDN VAS VNF-FG placement, so far, have only considered offline placement, where the VNF-FGs are placed before end-user traffic steers into the network. However, in concrete cases, a change in service usage patterns might occur, a situation that could require a VNF-FG placement in an online manner. This paper tackles the problem of online VNF-FG placement for VASs in CDNs, taking into account the eventual reuses and migrations of already-deployed VNFs. A cost model is considered, including multiple costs; i.e. new VNF instantiations, migration, hosting and routing costs. The objective is to optimally place the VNF-FGs such that total reconfiguration costs are minimized while QoS is satisfied. An Integer Linear Programming (ILP) formulation is provided and evaluated in a small-scale scenario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".