Resource pooling in network virtualization and heterogeneous scenarios using Stochastic Petri nets
Why this work is in the frame
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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.
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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 | 0.000 | 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