Accounting Information Systems Genetic Algorithms for All-Optical Shared Fiber-Delay-Line Packet Switches
Bibliographic record
Abstract
All-optical shared fiber-delay-line (FDL) packet switches have been studied intensively in the literature and with theliterature, many scheduling genetic algorithms have been proposed. However, these genetic algorithms suffer from notbeing able to provide a delay bound, or require complex timing methods to compute scheduling assignments. In thispaper, we propose two fast scheduling algorithms for all-optical shared-FDL packet switches. In the first algorithm,packet scheduling is formulated as a tree-searching problem. This is accomplished by breaking down the search tree intomultiple smaller subsets and assigning each subset to a parallel processor. By using this method, scheduling solutions canbe obtained in a shorter time. Although this approach is superior to other algorithms, its overall complexity and processingoverheads are still too high to warrant its day to day use. In the second algorithm, the search tree is carefully trimmeddown in order to reduce complexity and overheads. The conclusion will consider a 32 × 32 switch with 32 FDLs, andassume a processor clock rate of 200MHz for schedulers. With this new and second algorithm, a scheduling assignmentcan be calculated for a given packet in 30ns if 8 parallel processors are employed and we show by simulation that bothalgorithms can achieve a loss rate of ? 10?7 even at load 0.9, where the average delay is 11.5 timeslots.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".