Autonomous Resilience and Predictive Orchestration In Microservice Architectures: A Deep Reinforcement Learning Perspective On Performance Debugging And Resource Management
Keywords:
Microservices, Deep Reinforcement Learning, Anomaly DetectionAbstract
The shift toward microservice-based cloud architectures and mobile edge computing has introduced unprecedented complexity in system monitoring and management. Traditional monolithic approaches to performance debugging and resource allocation are increasingly inadequate for environments characterized by highly dynamic workloads and strict Quality of Service (QoS) requirements. This research provides an extensive exploration of modern methodologies for maintaining system reliability, leveraging deep reinforcement learning (DRL) and advanced machine learning (ML) for anomaly detection and autonomous orchestration. By synthesizing recent advancements in deep Bayesian networks, variational auto-encoders, and self-supervised learning from distributed traces, this paper outlines a comprehensive framework for "self-healing" distributed systems. We analyze the theoretical underpinnings of systems like Seer and Sage, which utilize big data and ML-driven debugging to navigate microservice complexity, alongside resource management frameworks like Sinan and GrandSLAM that guarantee service-level agreements (SLAs). The study further extends into the realm of 5G network slicing and multi-access edge computing (MEC), where intelligent offloading and dynamic service migration are essential. The findings suggest that the integration of fine-grained performance monitoring with predictive, DRL-based decision-making represents the most viable path toward achieving human-level control in large-scale IT ecosystems. This article offers a deep dive into the theoretical implications of these technologies, providing a roadmap for future research in modularizing legacy systems and enhancing the predictability of cloud-native environments.
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