LogSpect-AI: Predictive Log Intelligence for Autonomous Performance Assurance
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I2P102Keywords:
Predictive Log Intelligence, Anomaly Detection, Log Mining, Spectral Analysis, Autonomous Systems, AIOps, Machine Learning, Event Correlation, Performance Assurance, Observability, Root-Cause AnalysisAbstract
The logs generated by modern distributed systems are voluminous and are continually changing. The volume and intricacy of these logs are beyond what traditional rule-based monitoring systems can handle, hence these systems fail to identify subtle abnormalities, forecast performance drops, or trace execution flows involving several components. LogSpect-AI's hybrid predictive intelligence platform that integrates spectral log processing with machine learning models enables high-fidelity anomaly detection, event correlation, and performance prediction. The system undergoes testing through different failure scenarios such as resource congestion, cascading failures, and unusual event patterns. This test is performed with real production logs and synthetic datasets generated from a Kubernetes-based microservices testbed instrumented with Prometheus. The experimental results show that substantial improvements have been made in the predicted accuracy which has been increased by up to X%, and that the number of false positives has been decreased by Y%. There has also been a significant improvement in the early-warning detection as compared to static thresholding baselines. The outcomes achieved make it possible for LogSpect-AI to assume the role of a performance assurance engine that is capable of operating independently, is adaptable to the behavior of dynamic systems, can foresee disruptions before they happen, and can thus facilitate the workflows to self-heal in an intelligent manner. This framework is a major step towards real-time reliability management of large-scale, cloud-native distributed environments and, as such, it points to the near arrival of fully autonomous observability.
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