Effects of software aging and rejuvenation on performability of layered distributed systems
Bibliographic record
Abstract
When a fault-tolerant layered distributed system continues its operation despite the presence of component failures, its performance is usually degraded. Its performance can also be degraded if it is executing continuously for a long period of time due to a phenomenon known as software aging. To prevent unexpected or unplanned outages due to aging, a pro-active technique called software rejuvenation can be employed. This technique involves gracefully terminating an application and immediately restarting it with a refreshed internal state. For proper modeling of these systems, their performance and dependiability characteristics need to be considered in a unified way, called performability. This thesis proposes a new model called "Rejuvenated-FTLQN", to evaluate the effects of software aging and rejuvenation on performability of these layered systems. Specifically a Layered Queueing Network (LQN) is used for performance analysis and a Multi State Fault Tree (MSFT) is used for dependability analysis. The model is also used to study the impact of performing rejuvenation, time to perform rejuvenation and rejuvenation frequencey on performability of a system. A software tool called "Rejuvenated-FTLQNS" has been developed to automate the model solution.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".