A graph-based algorithm for partitioning of mobile web services
Bibliographic record
Abstract
Web services are getting popular in the domain of business to business electronic commerce and in automating information exchange between business processes because of the interoperability they provide in a distributed heterogeneous environment. Most existing systems use web services that are hosted in fixed infrastructures. Hosting web services on wireless mobile devices is challenging because of their limited resources. The few efforts made by researchers in the past are effective only for hosting of simple web service (WS) applications. However, the existing techniques are not adequate for hosting of WS applications that involve large and complex business processes or invoked by multiple concurrent WS clients. This research proposes to use an application partitioning approach so that the execution of some parts (partitions) of a WS application can be offloaded to a powerful computing node. A critical analysis of existing partitioning approaches for traditional and mobile applications is presented and a graph theory based algorithm for WS application partitioning is proposed. A performance analysis of the algorithm is made through prototyping and measurements.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".