Improving call admission control in ATM networks using case-based reasoning
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Bibliographic record
Abstract
This paper presents a framework for call admission control (CAC) in ATM networks based on case-based reasoning (CBR). CBR is used to correct the error estimation of the required bandwidth computed by conventional call admission control schemes, which were shown to overestimate the required bandwidth. This leads to bandwidth wastage and increased call rejection. A CBR-based system is proposed to characterize the traffic that may affect the cell loss ratio (CLR) of the network. The proposed system consists of two phases, an off-line phase and an on-line phase. In the off-line phase, the system constructs an initial explanation for having a high cell loss rate (failure cases) resulting from accepting a larger number of calls than desired. In the on-line phase, the system uses its explanations to make a decision of accepting or rejecting a new call. If a failure explanation is applicable for the new call, then the new call is rejected. Otherwise, the new call is accepted. The learning arises from receiving a feedback of the resulting CLR to evaluate the decision made by the proposed system and to update the explanation previously made. The performance of the scheme was shown to be superior compared to conventional schemes in terms of system utilization and call blocking ratios.
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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 | 0.001 | 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