The rapid evolution of cloud computing, transition toward 6G networking, and the proliferation of microservice architectures have necessitated a paradigm shift in how system reliability and resource efficiency are managed. Traditional monolithic monitoring systems are increasingly inadequate for the dynamic, decoupled nature of modern distributed environments. This research provides an exhaustive exploration of the integration of machine learning and deep reinforcement learning (DRL) for detecting execution anomalies and optimizing resource distribution. By synthesizing methodologies ranging from time-weighted control flow graph mining to recurrent neural network attention mechanisms, this study develops a theoretical framework for proactive system maintenance. We analyze the role of log-based anomaly detection, such as DeepLog and Logsed, in predicting failures before they impact service level agreements (SLAs). Furthermore, the paper investigates the intersection of environmental sustainability and system performance, examining how self-adaptive approaches can harness renewable energy in cloud ecosystems. The research also delves into the complexities of multi-domain service deployment within 5G and 6G frameworks, emphasizing the necessity of reliability-aware algorithms. Through an extensive review of existing literature and the proposition of a multi-level self-adaptation model, this article demonstrates that the future of resilient distributed systems lies in the convergence of automated boundary detection, intelligent workload scheduling, and adaptive flow control mechanisms. The findings suggest that while deep learning offers unprecedented accuracy in anomaly diagnosis, the integration of human-centric design-moving from Industry 4.0 toward Society 5.0-remains critical for the ethical and practical deployment of autonomous IT infrastructures.

Microservices, Anomaly Detection, Deep Reinforcement Learning

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