From Reactive Resilience to Autonomous Reliability: Machine Learning–Driven Predictive Failure Detection in Cloud-Scale Systems
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Abstract
Modern cloud systems operate at hyperscale, often spanning geographically distributed data centers that host tens of thousands of compute nodes, petabytes of storage, and highly interdependent microservices orchestrated through sophisticated schedulers and container platforms. Even with built-in redundancy, replication strategies, and automated orchestration frameworks, failures remain statistically inevitable due to hardware wear-out, transient faults, firmware bugs, resource contention, software misconfigurations, and unpredictable workload spikes. In such environments, reactive failure handling where remediation begins only after an outage or degradation occurs is insufficient to meet stringent service-level objectives. Predictive failure detection using machine learning (ML) therefore represents a paradigm shift from reactive resilience to proactive reliability engineering. By leveraging large-scale telemetry streams, SMART disk metrics, workload traces, and operational logs, ML models can identify subtle precursors to failure, forecast time-to-failure probabilities, and trigger preventive interventions such as workload migration, disk replacement, or node quarantine. This article synthesizes empirical failure analyses, cluster management architectures like Google’s Borg, and industry-scale reliability datasets such as Backblaze’s drive statistics to provide a unified perspective on ML-driven failure prediction. Through this integration of real-world production evidence and modern modeling techniques, we outline architectural design principles, feature engineering strategies, model selection trade-offs, and deployment considerations necessary to build robust predictive failure detection systems for next-generation cloud infrastructure
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