The rapid convergence of cloud-native microservices, edge computing, and emerging 6G network architectures has intensified the need for intelligent, real-time anomaly detection and failure prediction mechanisms. As distributed systems evolve toward ultra-reliable and low-latency communication paradigms, particularly in support of holographic communications and immersive applications, traditional monitoring approaches become insufficient. This study presents a comprehensive research framework integrating log-based modeling, machine learning-assisted service boundary detection, semi-Markov failure prediction, correlation-driven anomaly analysis, and edge-enabled diagnostics tailored for next-generation distributed environments. Drawing on foundational work in hidden semi-Markov models for failure prediction, automated log inference, and unsupervised anomaly diagnosis in microservice ecosystems, this article synthesizes theoretical and applied perspectives into a unified architecture suitable for cloud-to-edge-to-6G infrastructures.

The research explores how structured log inference, adaptive heartbeat algorithms, long-tail latency diagnosis, and container-based performance monitoring can be orchestrated to detect system degradation before catastrophic failure. Furthermore, the study situates anomaly detection within the broader context of 6G visions, holographic multiple-input multiple-output (MIMO) surfaces, immersive telepresence, and ultra-reliable low-latency communications. A conceptual and methodological blueprint is proposed to bridge classical reliability engineering with emerging network requirements beyond 2030.

Results indicate that predictive log modeling combined with correlation analysis and unsupervised real-time diagnosis significantly enhances early fault identification, particularly in multi-server and multi-connectivity architectures. The discussion elaborates on scalability, interpretability, architectural modularization of legacy systems, and implications for future edge-centric infrastructures. This research contributes an integrated theoretical model and detailed analytical discourse suitable for deployment in next-generation cloud-native environments supporting immersive, real-time applications.

Anomaly Detection, Microservices, Hidden Semi-Markov Models

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