Observability and controllability of autonomic computing systems for composed Web services
Bibliographic record
Abstract
Autonomic Computing is a research area whose aim is to embed “intelligent algorithms” in the IT infrastructure management software such that it can adapt to changes in regards to the configuration, provisioning, external attacks, and resource utilization variations at run time. It is therefore, almost natural to consider this IT infrastructure control software framework as being designed upon methods and technologies used for the design of control systems. In this paper the control system design methodology is extended to the analysis of the intrinsic properties of the autonomic system itself. Thus the controllability and observability properties of the computing process itself are defined and examined in more details. These properties are also investigated for the case of cloud services where the serial and parallel composition of these services is considered. These cloud based services are connected through cooperation protocols that define a global process dynamic. Web services are modeled as scheduled computational processes waiting in a queue to cooperate in delivering the service. This paper proposes an input-state-output mathematical model for the autonomic computing model of cloud based services and the observability and controllability are further analyzed on the above models. As an example a Kalman based control is applied to such processes and the general architecture and some simulation results are given.
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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 | 0.001 | 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 | 0.000 |
| 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".