Improving Software Performance and Reliability with an Architecture-Based Self-Adaptive Framework
Why this work is in the frame
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Bibliographic record
Abstract
Modern computer systems for distributed service computing become highly complex and difficult to manage. A self-adaptive approach that integrates monitoring, analyzing, and actuation functionalities has the potential to accommodate to a dynamically changing environment. The main objective of this paper is to develop an architecture-based self-adaptive framework to improve performance and resource efficiency of a server while maintaining reliable services. The target problem is distributed and concurrent systems. This paper proposes a Self-Adaptive Framework for Concurrency Architecture (SAFCA) that includes multiple concurrency architectural patterns or alternatives. The framework has monitoring and managing capabilities that can invoke another architectural alternative at run-time to cope with increasing demands or for reliability purpose. Two control mechanisms have been developed: SAFCA-Q and SAFCA-R. With SAFCA-Q, the system does not need to be statically configured for the highest workloads; hence, resource usage becomes more efficient in normal conditions and the system still is able to handle busty demands. SAFCA-R is used to improve reliability in the case of a failure by conducting a switchover to another software architecture. Experiment results demonstrate that the performance of SAFCA-Q is better than systems using only standalone concurrency architecture and resources are also better utilized. SAFCA-R also shows fast recovery in the face of a failure.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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