Knowledge Graph Enhanced AIOps Framework for Root Cause Analysis in Network Slice Service Degradation
Keywords:
AIOps; knowledge graph; root cause analysis; network slicing; 5G; service degradationAbstract
The provisioning of network slices in fifth-generation and beyond communication systems introduces unprecedented flexibility in delivering tailored services with distinct performance guarantees. However, the inherent complexity of virtualized infrastructures and the tight coupling between logical slices and shared physical resources significantly complicate the detection and diagnosis of service degradation. Artificial intelligence for IT operations has emerged as a promising paradigm to automate operational tasks, yet conventional data-driven root cause analysis often suffers from a lack of semantic context, leading to ambiguous fault localization and reactive remediation. This paper proposes a knowledge graph enhanced AIOps framework specifically designed for root cause analysis in network slice service degradation. By integrating heterogeneous telemetry data, topological models, service level agreement policies, and historical incident records into a unified knowledge graph, the framework enables context-aware reasoning over complex dependencies. We examine architectural considerations, including the layered design of the ingestion, knowledge graph persistence, inference engine, and decision support subsystems. The discussion extends to structural trade-offs between centralized and distributed knowledge graph deployments, the balance between model interpretability and diagnostic accuracy, and the integration of graph neural networks for anomaly propagation tracing. Furthermore, we address operational governance aspects such as federated knowledge sharing across domains, zero-trust security principles, and the policy implications of AI-driven decision-making in regulated telecommunications environments. The framework emphasizes sustainability through resource-efficient knowledge maintenance and fairness in cross-tenant diagnostics. A comprehensive cross-domain comparison situates the proposed approach within the broader landscape of intelligent network management. The paper concludes with a forward-looking perspective on the role of knowledge graphs in achieving autonomous and trustworthy network operations.
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