Memory-Dump Signatures for Automated Root-Cause Ranking in System Failures
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Abstract
The increasing complexity of modern computing environments, encompassing distributed microservices, intricate operating system kernels, and heavily concurrent user-space applications, has severely exacerbated the difficulty of diagnosing system failures. When a catastrophic failure occurs, the system typically generates a memory dump, which serves as a forensic snapshot of the precise state of the machine at the moment of the crash. Traditionally, the analysis of these memory dumps has been a highly manual, labor-intensive process requiring profound domain expertise. To address this bottleneck, this paper proposes a novel framework for automated root-cause ranking utilizing advanced memory-dump signatures. By extracting complex multidimensional features from stack traces, heap metadata, and processor register states, the proposed methodology constructs a robust forensic signature for any given crash. These signatures are subsequently processed through a sophisticated ranking algorithm that evaluates historical fault data to predict and rank the most probable root causes. Through extensive evaluation on a comprehensive dataset of injected and naturally occurring software faults across both kernel and user-space domains, the automated ranking mechanism demonstrates a substantial improvement in diagnostic accuracy compared to traditional stack-hashing techniques. The results indicate that integrating memory-state anomalies with execution flow context significantly reduces the time to resolution for critical system failures, paving the way for more resilient and self-healing software infrastructures.
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