Improving node behaviour in a QoS control environment by means of load-dependent resource redistributions in LANs: Research Articles
Bibliographic record
Abstract
An important means to guarantee an acceptable quality of service in networks with real-time communication requirements is the reservation of resources at connection setup time. However, such reserved resources, e.g. transmission bandwidth, may be unused as a consequence of the variations in the actual resource demands. Therefore, a more efficient resource utilization is possible if communicating stations or end-users dynamically hand over some of the free resources temporarily to the other communication partners, e.g. of a ‘broadcast network’.This paper concentrates on two fundamental problems of such a demand-based sharing of resources: on the one hand, estimation of the current resource requirement on the basis of load measurements is investigated and, on the other hand, we elaborate efficient algorithms for resource sharing respecting real-time requirements. The algorithms proposed for load estimation and for resource sharing are evaluated analytically with respect to their efficiency for worst-case, average-case and realistic load scenarios. Our approach suggested for resource and traffic management allows one to achieve significantly better utilization of network resources. Copyright © 2005 John Wiley & Sons, Ltd.A shorter preliminary version of this paper has appeared in the Proceedings of the Int. Symp. on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), Montreal, July 2003, 361–371.
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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 | 0.002 | 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".