Discovering the architecture of geo-located web services for next generation mobile networks
Bibliographic record
Abstract
As many geo-located Web services will be deployed in the future, mobile clients will be interested in locating a specific application server based on requirements such as proximity, service cost per location area, bandwidth, and server utilization rates. This paper presents a middleware system called GLWSA (Geo-Located Web Services Architecture) that aims to satisfy these requirements in addition to providing a thematic factorization of common location functions in order to locate mobile clients. The GLWSA supports a set of GLWSMs (Geo-Located Web Services Manager) distributed over the mobile network. It defines protocols to discover and inform a Supplier Application Server (SAS) to migrate the service execution (from a specific client) to the nearest SAS based on the client's location. This architecture is suitable to assist mobile clients to discover the geo-located Web services and to maintain the service execution closest to their location.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".