Service Overlay Network Resource Adaptations Based on an Economic Model
Bibliographic record
Abstract
In the current Internet, service overlay networks (SON) can provide a means for offering end-to-end quality of service (QoS) required by real time services such as VoIP, streaming multimedia and interactive games. We consider the approach where the SON operator leases bandwidth with QoS guarantees for the overlay links from Internet autonomous systems. Available bandwidth is managed by the SON admission and routing policy to provide end-to-end QoS connections to the service user. Maximizing profit is a key objective for the SON operator. In this paper, we propose a novel resource management approach which uses an economic model, including costs and revenues, to drive resource adaptations to changing network conditions, so that network profit can continuously be optimized. The approach integrates link capacity adaptations with connection admission control and routing policies based on Markov decision process theory. Numerical analysis results on a network example shows that the approach is effective at attaining maximized profit under varying network conditions
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".