Formalizing Reusable Communication Models for Distributed Systems Architecture
Bibliographic record
Abstract
Building distributed computing systems involves complex concerns integrating a multitude of communication styles, technologies (IoT, cloud and big data...), stakeholders (architects, developers, integrators, etc.) and addressing a multitude of application domains (smart cities, health, mobility, etc.). Existing architectural description languages fail to rigorously bridge the gap between the abstract representation of communication styles and those supported by existing execution infrastructures. In this paper, we aim at specifying software architecture of distributed systems using an approach combining semi-formal and formal languages to build reusable model libraries to represent communication solutions. Our contribution is two fold. First, we propose a metamodel to describe high level concepts of architecture in a component- port- connector fashion focusing on communication styles. Second, we attempt to formalize those concepts and their semantics following some properties (specifications) to check architectural conformance. To validate our work, we provide a set of reusable connector libraries within a set of properties to define architectures for systems with explicit communications models like message passing and remote procedure calls, that are common to most distributed systems.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".