Resource pooling in network virtualization and heterogeneous scenarios using Stochastic Petri nets
Bibliographic record
Abstract
Wireless cellular networks are undergoing severe changes due to the ever increasing demand of data rate. Additionally, the demand is more and more heterogeneous (imbalanced) in time and space. Sudden peaks in demand at a certain location have to be absorbed by the network. While operators traditionally over-provisioned their own separate network capacity in order to reduce the blocking and overload probabilities, this approach seems no longer economically viable. Instead, the idea of network virtualization (NV) emerged. One aspect of NV is that resources from all operators are pooled together. Shared and virtualized resources can be better distributed among all users compared to having separate subsets of users to separate subsets of resources. This holds especially if the demand is imbalanced among the operators, as shown in this paper. In this paper the stochastic Petri net (SPN) paradigm is used to provide with a compact model of NV resource pooling (RP). In contrast to the equivalent but tedious analysis of Markovian systems the SPN approach allows a quick numeric performance evaluation with tool support, thus olfering a strong modeling advantage. The scenarios analyzed here are networks of separate operators and resources, compared to one virtualized network. In a second step the scenario includes heterogeneity in demand, i.e., a load imbalance between the providers and results show much higher gains in this unbalance.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".