Cloud Architecture for Dynamic Service Composition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Service composition provides value-adding services through composing basic Web services, which may be provided by various organizations. Cloud computing presents an efficient managerial, on-demand, and scalable way to integrate computational resources (hardware, platform, and software). However, existing Cloud architecture lacks the layer of middleware to enable dynamic service composition. To enable and accelerate on-demand service composition, the authors explore the paradigm of dynamic service composition in the Cloud for Pervasive Service Computing environments and propose a Cloud-based Middleware for Dynamic Service Composition (CM4SC). In this approach, the authors introduce the CM4SC ‘Composition as a Service’ middleware layer into conventional Cloud architecture to allow automatic composition planning, service discovery and service composition. The authors implement the CM4SC middleware prototype utilizing Windows Azure Cloud platform. The prototype demonstrates the feasibility of CM4SC for accelerating dynamic service composition and that the CM4SC middleware-accelerated Cloud architecture offers a novel way for realizing dynamic service composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it