TL;DR
Within the limits of trace replay and controlled fault injection, these results suggest that closed-loop anomaly-aware scheduling can improve recovery timeliness and operational stability for gray-failure mitigation in cloud clusters.
Reactive alert handling and manual intervention continue to limit resilience in cloud-native enterprise platforms. This paper presents AUTO-OPS, an implemented AIOps decision layer that connects predictive incident detection, dependency-aware event correlation, non-compensatory admission gates, bounded remediation, rol...
A three-layer predictive fault tolerance architecture, telemetry collection and signal engineering, machine learning-driven anomaly detection, and autonomous remediation orchestration are presented, designed for the operational realities of production-critical enterprise cloud infrastructure.
This study forms temporal concept-incremental anomaly detection for both univariate and multivariate time series, introduces time-based and entity-based concept extraction strategies together with Wasserstein-distance diagnostics for inter- and intra-concept distribution drift, and defines an evaluation protocol that m...
This study created and verified an adaptive machine learning framework that makes use of real-time model updates and domain-specific cloud infrastructure information and offers a deployable framework for improving cloud security in Kenya and other resource-constrained environments.
The results show that CorrFault-GNN improves task success, latency, energy efficiency, and deadline satisfaction compared with representative reactive, proactive, and learning-based baselines, and that its advantage grows as infrastructure sharing and failure correlation increase.
Cyber-physical power systems increasingly rely on data-driven tools to detect instability and support reliable grid operation. However, reliable stability prediction is difficult when unstable operating configurations are rare, sensitive, or unavailable during model development, since collecting such data safely and at...
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