Bibliographic record
Abstract
This paper presents a new pricing mechanism for virtual network (VN) services to regulate the demand for their shared substrate network (SN) resources. The contributions of this article are two-fold; first, we introduce a new time-of-use pricing policy for the SN resources that reflects the effect of resource congestion introduced by VN users. The preferences of the VN users are first represented through corresponding demand-utility functions that quantify the sensitivity of the applications hosted by the VNs to resource consumption, time-of-use and prices during peak-demand periods. We then introduce a novel model of time-varying VNs, where users are allowed to up- or down-scale the requested resources to continuously maximize their utility while minimizing the cost of embedding the VNs onto the SN. The second contribution is a novel hierarchical embedding management approach tailored to efficiently map these dynamic VNs. The proposed VN embedding scheme recasts the VN embedding problem as a subgraph matching one, and introduces a simple heuristics-based matching procedure to find a good VN embedding from a number of candidate solutions obtained in parallel. In contrast to existing solutions, the proposed scheme does not impose any limitations on the size or topology of the VN requests. Instead, the search is customized according to the VN size and the associated utility. Experimental results demonstrate the performance achieved by the proposed work in terms of the increased profit, resource utilization and number of accepted requests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".