A new self-organizing approach for congestion control in ATM networks
Bibliographic record
Abstract
This paper presents a new approach to the problem of congestion control arising at a non-blocking ATM switch with input queues. When two or more packets in the switch input buffers simultaneously request the same output port of an ATM switch, congestion will occur, and one of these packets will be transmitted and the others will be either buffered or dropped. In order to prevent this cell discarding, input queuing is widely used. The only problem with input queuing is the head-of-line (HOL) blocking in which a packet in a first-in-first-out (FIFO) queue has no chance to access an available output port because the packet ahead of it in the buffer is blocked. In order to solve this problem, many algorithms have been presented. One of these algorithms is the input window policy algorithm which divides the contention resolution process into S steps during a time slot to find an optimal set of packets. In the optimal set, each packet must have a different destination address. We introduce a new self-organizing neural network approach to resolve the contention problem based on the input window policy algorithm. From the simulation results shown, our neural network model provides the same switch throughput as that of the input window policy. However, the new approach is definitely much faster than the original algorithm in terms of finding this optimal set due to the parallel processing performance of the neural networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".