A performance analysis method for autonomic computing systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an autonomic computing system, an autonomic manager makes tuning, load balancing, or provisioning decisions based on a predictive model of the system. This article investigates performance analysis techniques used by the autonomic manager. It looks at the complexity of the workloads and presents algorithms for computing the bounds of performance metrics for distributed systems under asymptotic and nonasymptotic conditions, that is, with saturated and nonsaturated resources. The techniques used are hybrid in nature, making use of performance evaluation and linear and nonlinear programming models. The workloads are characterized by the workload intensity , which represents the total number of users in the system, and by the workload mixes , which depict the number of users in each class of service. The results presented in this article can be applied to distributed transactional systems. Such systems serve a large number of users with many classes of services and can thus be considered as representative of a large class of autonomic computing systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it