Predictive Failure Detection and Scalable Enterprise Data Management through Intelligent Cloud Infrastructure Observability
Abstract
The escalating scale of cloud resource data systems requires intelligent systems to detect possible failures to ensure that grids do not affect the delivery of the application, availability of information, and integrity of the service. Conventional methods of monitoring mainly rely on fixed thresholds and proactive notices that do not work in detecting nuanced anomalies over distributed infrastructure elements. This research work describes a smart way to be observable during the prediction of failures, which involves a combination of metrics, logs, distributed tracing, and infrastructure telemetry, as well as the identification of new patterns of failure. This framework relies on machine learning-based anomaly detection, time-trend analysis, and the correlation of dissimilar observability signals as approximations of the risks of failures and contributes to the choices of a proactive operating mode. Developed to support scalable cloud data platform architecture, the proposed design considers dynamic workloads and resource usage, dependencies between various services, and varying operating conditions. The framework also facilitates explainable notifications, prioritization of risks, and automated incident-response suggestions to reduce the mean time to identify and enhance system flexibility. It represents an architecturally structured framework, the main focus of which is scalability and ongoing data collection, dynamic model analysis, and cloud-native monitoring environment integration. The research presents an assessment-founded foundation for defining predictive accuracy, false-positive, and detection latency and efficiency at different workloads. The proposed framework will enhance active infrastructural control, effectiveness of resource utilization and credibility of opinionated cloud information platforms
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 9 No. 4 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
16105-16115 |
Published |
July 20, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr.Alex Mathew (%2024). Predictive Failure Detection and Scalable Enterprise Data Management through Intelligent Cloud Infrastructure Observability. International Journal of Emerging Trends in Engineering and Management Research , Vol. 9 No. 4 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 16105-16115. https://doi.org/10.15662/ijetemr.2024.0904004 |
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