Autonomic Business-Driven Dynamic Adaptation of Service-Oriented Systems and the WSPolicy4MASC Support for Such Adaptation
Bibliographic record
Abstract
When a need for dynamic adaptation of an information technology (IT) system arises, often several alternative approaches can be taken. Maximization of technical quality of service (QoS) metrics (e.g., throughput, availability) need not maximize business value metrics (e.g., profit, customer satisfaction). The goal of autonomic business-driven IT system management (BDIM) is to ensure that operation and adaptation of IT systems maximizes business value metrics, with minimal human intervention. The author presents how his WS-Policy4MASC language for specification of management policies for service-oriented systems supports autonomic BDIM. WS-Policy4MASC extends WS-Policy with new types of policy assertions: goal, action, probability, utility, and meta-policy assertions. Its main distinctive characteristics are description of diverse business value metrics and specification of policy conflict resolution strategies for business value maximization according to various business strategies. The author’s decision making algorithms use this additional WS-Policy4MASC information to choose the adaptation approach best from the business viewpoint.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".