Monitoring service level workload and adapting highly available applications
Bibliographic record
Abstract
Elasticity is a key feature in the cloud while High-Availability (HA) is one of the challenges. Recently, an architecture has been proposed for managing HA in the cloud using the SA Forum middleware and an Elasticity Engine for triggering resource provisioning and de-provisioning at the application level while preserving HA. This Elasticity Engine requires the monitoring of the application service level workload in contrast to the monitoring of VM workload as done usually. In this paper we propose an approach for the monitoring of HA applications at the service level and its integration with the Elasticity Engine. The approach allows for the monitoring of application processes in the traditional manner and for the mapping of this workload to their combined service level workload. Resource usages are aggregated and mapped to the service level workload using a distributed client-server architecture. The approach allows for distinguishing between the different HA states, active and standby, a component can be assigned at runtime and it adapts to the situations where switchovers happen under the control of the SA Forum middleware due to failures for example.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".