Optimizing Distributed Log Tracing for Intelligent Cloud System Operations
Abstract
This paper investigates optimization strategies for distributed log tracing in intelligent cloud system operations. As modern
computing shifts toward large-scale distributed infrastructures, efficient log tracing has become a cornerstone of observability, fault detection, and automated decision-making. The study proposes an AI-driven optimization framework combining adaptive sampling, semantic correlation modeling, and graph-based reasoning to enhance trace accuracy and efficiency. The proposed approach leverages machine
learning techniques to dynamically adjust trace collection policies and infer causal relationships among microservices. Experimental
evaluation demonstrates improved observability accuracy, reduced storage overhead, and accelerated anomaly detection compared with
conventional tracing systems. The results highlight the potential of intelligent tracing in building self-optimizing and resilient cloud operation environments.
computing shifts toward large-scale distributed infrastructures, efficient log tracing has become a cornerstone of observability, fault detection, and automated decision-making. The study proposes an AI-driven optimization framework combining adaptive sampling, semantic correlation modeling, and graph-based reasoning to enhance trace accuracy and efficiency. The proposed approach leverages machine
learning techniques to dynamically adjust trace collection policies and infer causal relationships among microservices. Experimental
evaluation demonstrates improved observability accuracy, reduced storage overhead, and accelerated anomaly detection compared with
conventional tracing systems. The results highlight the potential of intelligent tracing in building self-optimizing and resilient cloud operation environments.
Keywords
Distributed tracing; Cloud operations; Artificial intelligence; observability; Optimization; Log analysis
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PDFReferences
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DOI: http://dx.doi.org/10.70711/aitr.v3i3.8042
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