Improving call admission control in ATM networks using case-based reasoning
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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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".