Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it