Ensuring Profit and QoS When Dynamically Embedding Delay-Constrained ICN and IP Slices for Content Delivery
Bibliographic record
Abstract
Content Delivery Networks (CDNs) are becoming more critical due to the tremendous growth of video traffic. This paper proposes a complete framework targeting the creation of Information Centric Network (ICN) and IP slices for content delivery. Leveraging ICN's in-network caching advantages, our solution is tailored to a VNF placement context where ICN and IP slices are dynamically created over a physical infrastructure. Embedding delay-constrained slices with high profits requires adjustments of the available resources based on the traffic and slice demands, which leads to the reassigning of the slices. However, slice reassignment may lead to additional costs, content disruption in service delivery, and QoS violation. We formulate the problem as an Integer Linear Programming (ILP) and propose a cost-efficient dynamic network slicing heuristic. The objective is to maximize the profit of infrastructure providers while meeting the different QoS requirements of the slices. Our evaluation validates the effectiveness of profit maximization and analyzes the impact of QoS violation on infrastructure provider profit. The results are compared to a recent work from the literature and an optimal solution. The evaluation shows that our proposed heuristic is promising and offers a near-optimal solution while improving profitability and ensuring QoS.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".