Automated State-Space Exploration for Configuration Management of Service-Oriented Applications
Bibliographic record
Abstract
Configuration management is a complex task, even for experienced system administrators, which makes self-managing systems a desirable solution. Self-management implies the need for a model based on which configuration changes may be decided. In previous work, we described a method for constructing a state-transition model of application behavior, by observing the application in simulation. This method relied on an expert to manage the (simulated) application in order to collect the necessary observations for constructing the model. However, that method was agnostic about (a) the size of the system space space as implied by the granularity of the observations, and (b) the sufficiency of the actual observations collected for understanding the application in a variety of configurations and environments. In this paper, we replace the (expensive) expert domain knowledge with automatic approaches to ensuring coverage of the application, and demonstrate the superiority of this approach. We present empirical data regarding state space and granularity to explore the use of state models for understanding applications.
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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 | 0.001 | 0.011 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".