Online VNF Placement and Chaining for Value-added Services in Content\n Delivery Networks
Bibliographic record
Abstract
Value-added Services (VASs) (e.g. dynamic site acceleration, media\nmanagement) play a critical role in Content Delivery Networks (CDNs). Network\nFunctions Virtualization (NFV) enables the agile provisioning of VASs. In NFV\nsettings, VASs are provisioned as ordered sets of Virtual Network Functions\n(VNFs), forming VNF-Forwarding Graphs (VNF-FG) which are deployed in the CDN\ninfrastructure. The CDN VAS VNF-FGs have a specific characteristic: they have\none end-point (corresponding to the content server) that is unknown, prior to\ntheir placement. The proposals for CDN VAS VNF-FG placement, so far, have only\nconsidered offline placement, where the VNF-FGs are placed before end-user\ntraffic steers into the network. However, in concrete cases, a change in\nservice usage patterns might occur, a situation that could require a VNF-FG\nplacement in an online manner. This paper tackles the problem of online VNF-FG\nplacement for VASs in CDNs, taking into account the eventual reuses and\nmigrations of already-deployed VNFs. A cost model is considered, including\nmultiple costs; i.e. new VNF instantiations, migration, hosting and routing\ncosts. The objective is to optimally place the VNF-FGs such that total\nreconfiguration costs are minimized while QoS is satisfied. An Integer Linear\nProgramming (ILP) formulation is provided and evaluated in a small-scale\nscenario.\n
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".