Improved interlock correction when solving layered queueing networks using decomposition
Bibliographic record
Abstract
Layered Queueing networks are a common method for solving performance models of modern distributed computer systems that use blocking remote procedure calls. Several analytic methods exist to solve these networks, many of which use the method of decomposition to break the model up into smaller, more easily solved submodels. Analytic solutions that break up a model must take into consideration interlocking, which is a phenomena that arises when a single customer in one submodel is represented by more than one customer in another. Failing to correct for interlocking can result in large errors in the final solution. This paper revisits interlocking, as implemented in the analytic Layered Queueing Network Solver. The interlock calculation it uses often distributes the waiting a customer experiences incorrectly among intermediate tasks. Further, certain models with external contention can yield unfeasible utilizations at interlocked servers. This paper introduces a new interlock calculation which is more accurate, and does not produce unfeasible utilizations. The new approach is compared against the old approach (and against solutions with no interlock correction) and is shown to produce better results in all cases.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".