A Decentralized Self-Organizing Service Composition for Autonomic Entities
Bibliographic record
Abstract
In service-oriented environments and distributed systems, service composition allows simple services to be dynamically combined into new, more complex services. Service composition techniques are usually designed as an extension to service discovery. Traditional techniques try to match a user’s requirements, often complex, with the available services. However, one-to-one matching is inefficient; it is preferable to meet the request from available services even when one of the basic services is not present. Separating composition and discovery has also led to inefficiency, especially in a highly dynamic environment. With the heterogeneity of networks, users, and applications having multiple sources, constructing service-specific overlays in large distributed networks is challenging. In this article, we propose a new service composition algorithm to deal with the problem of composing multiple autonomic elements to achieve system-wide goals. Using a self-organizing approach, autonomic entities are dynamically and seamlessly composed into service-specific overlay networks. The algorithm combines composition and service discovery into one step, thereby achieving more efficiency and less latency. The decentralized and self-organizing nature of the algorithm allows it to respond rapidly to system changes. Extensive simulation results validate the effectiveness of the approach when it is compared to other solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".