Bibliographic record
Abstract
Middleware has a major impact on the performance of distributed software applications.To predict the performance (e.g., response time, throughput) of an application using a performance model, it is essential to capture not only the characteristics of the application, but also the platform and the middleware.Model elements, which are added to an application software model for performance analysis are known as Completions.This research proposes a unified and flexible framework, called MMLQ (Middleware Modeling for Layered Queues), for modeling middleware completions and automatically composing them into software performance models.The existing approaches have many limitations.Some of them require to build and compose models manually, others lack generality, while others use component-based modeling which creates many elements that are not essential for performance analysis and complicate the model.MMLQ overcomes the limitations of the existing approaches and can model a wide range of middleware platforms.The contributions of this thesis include identifying the commonalities and differences among different middleware platforms, modeling their features, specializing middleware models to cater to the needs of an application, calibrating the specialized models and a proposing a process to compose calibrated models with the application models.The software application models and the middleware models are all built using Layered Queuing Network Model (LQN) developed at the RADS lab of Carleton University.The MMLQ framework is validated by showing its ability to model three different categories of middleware and by comparing the predicted performance with measured performance metrics.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".