Adaptation Strategies in Policy-Driven Autonomic Management
Bibliographic record
Abstract
Policies have been proposed as a means to express required or desired behavior of systems and applications. Policies can be used within the autonomic management elements of the system to provide action directives to adjust application or system tuning parameters in order to meet operational requirements. Policy specification is frequently component-based, that is, focused on the operational requirements of a particular system component, e.g., a Web server or a database. In multi-component systems, such as e-commerce systems, multiple components may co-exist on a single server and cooperate to deliver a set of services. It is reasonable to expect that each component would have its own set of associated policies and that, in turn, the autonomic management system could identify actions to take per component. However, these independent sets of policies may possibly yield multiple directives from which the autonomic manager must select one or more appropriate actions. In this work, we look at an approach which tries to select one or more actions based on previous behavior. Previous decisions are captured in the form of states and transitions which in turn are used to try to find good sequences of actions. We describe how the states are formed and actions identified. We describe the implementation of an autonomic manager using this approach and report on the experience in managing a dynamic Web server.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".