Bibliographic record
Abstract
Layered queueing networks (LQN), as an extended queueing network, are used widely to evaluate many distributed systems which have a client-server architecture. However, LQN models also share the difficulty that convention queueing networks have in modelling state-based behavior, such as the decisions made in exception handling during resource allocations. In order to enhance the modelling power of LQN models to handle decisions, this paper defines four decision patterns: abort, timeout, infinite-retries and finite-retries, which are commonly used in the exception handling during resource allocations. These four decision patterns are generalized to two cases: timeout and retry decisions and implemented in the LQN simulation tool, LQSIM. The LQN input language was modified to allow these actions to be specified directly. The simulator was then verified by comparing its results from solving a model a small-scale web server model to results found from solving a Petri net model using GreatSPN. The results were quite similar to each other despite the extensive simplifications in the Petri net model required for solution.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".