Citation:

Prabhudoss Jayakumar, 2026. Predictive Infrastructure Drift Detection Framework for Cloud-Native Carrier Network Reliability Engineering,
  Volume 6 Issue 4: 1-9.

Abstract:

Cloud-native carrier networks rely on declarative infrastructure, virtualized network functions, distributed orchestration, and dynamic service topologies. Divergence among intended configuration, deployed state, and runtime behaviour may create reliability risks that traditional alarms detect only after service degradation begins. This review examines the literature relevant to predictive infrastructure drift detection and links infrastructure-as-code analysis, network anomaly detection, automated log analysis, concept-drift detection, artificial-intelligence-driven operations and carrier-network automation. Existing research demonstrates strong capabilities for retrospective anomaly detection, failure localization, and workload forecasting, but provides limited evidence for predicting harmful divergence before operational impact occurs. Most existing approaches treat intended state, runtime state, telemetry, topology, and service level objectives as largely independent analytical units. Carrier-specific validation remains very limited because public datasets do not typically contain configuration histories, dependency graphs, orchestration events and service consequences within a common timeline. The predictive framework needs to incorporate temporal state reconciliation, topology-aware feature construction, risk-driven forecasting with uncertainty calibration, and intervention policies. Advances in this area will help mitigate the problems of silent degradation, incorrect automated remediation, and loss of reliability in programmable carrier infrastructure systems.

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Keywords:

Aiops, Carrier Networks, Cloud-Native Reliability, Concept Drift, Infrastructure Drift, Predictive Detection.