Structured Alert Modeling, Aggregation, and Runbook Integration for Network Operations

High-volume network monitoring environments generate thousands of alerts per day, the majority of which are duplicates, noise, or downstream symptoms of a single root cause. This paper presents a three-part framework for managing network alerts at scale. First, a structured alert schema encodes alert metadata, severity, device role, and relational context in a machine-processable format that enables programmatic processing. Second, a five-stage aggregation pipeline reduces raw alert volume through deduplication, topology-aware correlation (derived from the Network Intent Model), time-based burst aggregation, DBSCAN semantic clustering, and maintenance-aware suppression — reducing actionable alert volume by an estimated 60–80% while preserving detection fidelity. Third, a runbook integration model links every alert type to executable Ansible playbooks for automated diagnosis and recovery, closing the gap between alert firing and operator response. A complete Python implementation of the aggregation pipeline is provided, including DBSCAN clustering for semantic grouping. The framework is demonstrated on a VXLAN datacenter environment where a spine failure generating 12 raw alerts is reduced to a single actionable P1 ticket with diagnostic outputs pre-attached. This paper is the third in a series on self-healing network operations. Related works: Ghosh (2026), A Declarative Network Intent Schema (https://doi.org/10.5281/zenodo.20552531); Ghosh (2026), Intent-Driven Alert Generation Using LLMs (https://doi.org/10.5281/zenodo.20552688); Ghosh (2026), Network Health Score Framework (https://doi.org/10.5281/zenodo.20552168).