Bibliographic record
Abstract
A Layered Queueing Network (LQN) is a recognized performance modelling technique for performance prediction and evaluation of distributed systems.However, at present LQNs do not handle models with state-based behaviour such as timeouts and aborts, called 'decisions' here.This research extends LQNs by incorporating decisions into the model.The XML input language used to describe LQNs has been extended to handle these decisions.Both the LQN simulator, lqsim, and analytic solver, lqns, were then modified to solve models with decisions.The analytic solver uses decomposition and mean value analysis to solve models.Unfortunately, mean value analysis cannot be used to solve models with state-based behaviour.To overcome this limitation, a new approach called Dynamic Parameter Substitution (DPS) is used where intermediate results found while solving the model are used to alter the input parameters for subsequent iterations of the solution.To accomplish this goal, Layered Queueing eXperiment (LQX) language functions were derived to handle timeout and retry decisions and to handle fair-share queueing.The results from solving models using DPS were compared to results found from hybrid modelling, simulation, and where feasible, Petri nets.This research also improves the accuracy of the LQN analytic solver when solving models with traffic dependencies, namely interlocking and sub-chain dependent behaviour.Interlocking occurs from the decomposition of the model into submodels; a single customer from an upper submodel may appear as two or more customers in lower level submodels.Some forms of interlocks were handled by the previous solutions, but with some limitations.Sub-chain dependencies are hidden more deeply in LQN models, and are handled for the first time.Sub-chain dependencies occur when traffic from multiple independent clients share a common intermediate server then diverges to lower level servers when this server is acting as a source of customers.This research presents a generalized solution to handle more general cases of inter-iii locks and sub-chain dependencies.The results of the improved solution have good accuracy compared with simulation results and the problems of the extraneous delays and infeasible utilizations are eliminated.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".