Detecting emergent behavior in autonomous distributed systems with many components of the same type
Bibliographic record
Abstract
In design of distributed systems with specification languages such as message sequence charts (MSC), communication between different component (agent) types or instances of them are defined. There are a number of methods to verify the design using scenarios of inter-component communication. Those methods usually ignore the intra-component communication, i.e. communication between components of the same type. However in large scale systems, such as e-commerce systems, there are several components of one type that may communicate with each other and this may violate some regulatory policies defined in the design. On the other hand, there are declarative policies in system design that need to be integrated in the implemented system. In this paper a method that takes a topology of the system and regulatory policies as its inputs and detects the components having emergent behavior at its output is proposed. This method is defined to reveal the components that may violate the policies in the design phase by defining message types and extracting a version of MSCs called modified MSCs (MMSCs). Then by clustering and analyzing the send messages in the communications of different components the violating components are detected. By applying this method, all instances of components can be examined for policy violation in the implemented system. The method is explained along with a case study of a realistic online auction system and it is shown how this method can detect the components with emergent behaviors.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".