DATA-DRIVEN RELIABILITY ENGINEERING FOR PROACTIVE FAILURE DETECTION IN LARGE-SCALE CLOUD SYSTEMS
Bibliographic record
Abstract
Massive cloud technology is the new infrastructure behind new digital services, with applications reaching out to millions of users.The growing complexities of distributed microservices, virtualization technologies and multi-region deployments, however, present a serious challenge in ensuring the reliability of the system.Conventional monitoring methods tend to be reactive and in most cases do not identify hidden or silent failures in real time thus resulting in degradation of services and SLA breach.This article introduces a data-based reliability engineering model of proactive failure detection in the large scale cloud setting.The suggested solution uses telemetry data, logs, metrics, and traces and employs state-of-the-art machine learning methods, including anomaly detection, predictive modeling, and event correlation, to find out possible failures before they happen.The system will allow self-healing actions to be performed by automated decision-making, and early warning mechanisms can be realized by combining real-time data processing and intelligent analytics.The structure increases the reliability of the system by minimizing downtime, better fault detection and Venkatramana Reddy Panyala, Andrew Levi Gazula https://iaeme.com/Home/journal/IJDA18 editor@iaeme.comincreased adherence to service level agreements.Moreover, it offers scalability and flexibility to dynamic clouds.The findings show that data-oriented solutions are much more efficient in detection and operational aspects than conventional rule-based solutions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".