Passive observation-based architectures for management of web services
Bibliographic record
Abstract
Web Services technologies are emerging as the standard paradigm for program-to-program interactions over the Internet. A Web Service is any application that offers its functionalities through the Internet by publishing a description of its interfaces. Web Services are gaining more and more momentum and their utilization is being spread and even standardized in many areas including e -Government, e -Telecomm, e -Health, and digital imaging. The management of Web Services will play an important role for the success of this emerging technology and its adoption by both providers and consumers. As the technology matures and spreads, consumers are likely to be very picky and restrictive with regards to the quality of the offered Web Services. Another challenging factor for the management of Web Services is related to the diversity of platforms on which Web Services are developed and deployed. In this thesis, the focus is on the management of Web Services using passive observation with the intent to have open and platform-independent management architectures capable of assessing both functional and non-functional aspects of Web Services. The bulk of the observation process is carried out by model-based entities known as observers. These observers make use of formal model such as Finite State Machines, Communicating Finite State Machines, and Extended Finite State Machines. The proposed architectures include observers developed and deployed as Web Services: mono-observer architecture and multi-observer architectures. A single observer is enough for observation of a non-composite Web Service while a network of observers is preferred when observing a composite Web Service. Passive observation requires traces' collection mechanisms which are thoroughly studied and their performance compared for all architectures. A new approach for online observation based on Extended Finite State Machine is proposed to accelerate misbehaviors' detection. This approach proposes backward and forward walks in the model to reduce possible sets of states and values of variables. I adopted a pragmatic evaluation approach to assess each of my contributions: analytical analysis and proof, implementation, and real case studies. All components of management architectures have been studied, their complexities determined, developed, and deployed. The use cases used for the evaluation of the effectiveness of the architecture, including simple and composite Web Services, are also fully implemented and deployed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".