AI-Driven Self-Healing System for Real-Time Software Incident Management
Abstract
Modern cloud-native software systems, including banking applications, cloud platforms, enterprise resource planning architectures, and real-time online services, continuously produce large volumes of operational data such as application logs, performance metrics, and system traces. Monitoring these systems using conventional rule-based or threshold-driven methods introduces operational bottlenecks because static thresholds fail to adapt to dynamic workloads and cannot identify unseen failure modes. Furthermore, relying on human operators for diagnosis and remediation increases the Mean Time to Recovery during outages. This paper introduces an autonomous, closed-loop telemetry and incident management framework titled AI-Driven Self-Healing System for Real-Time Software Incident Management. The architecture implements memory-efficient stream processing by embedding probabilistic data structures—specifically Count-Min Sketches and Cuckoo Filters—within an in-memory Redis Stack layer. An automated stream aggregator compiles multi-route frequencies and HTTP status code distributions into normalized 18-dimensional feature vectors over 5-second sliding windows. An unsupervised machine learning engine combining an Isolation Forest algorithm with dynamic Z-score statistical profiling learns operational baselines and isolates multivariate anomalies in real time without requiring labeled historical datasets. When anomalous deviations are detected, the system executes automated self-healing actions such as service restarts, dynamic resource allocation, and traffic management to restore operational stability. An interactive operations dashboard visualizes realtime core health, incident alerts, service mesh topologies, and forensic audit trails. Empirical benchmarking demonstrates that the platform sustains a live ingestion throughput of 124 messages per second across 24 monitored endpoints with negligible CPU saturation (0.0%) and bounded memory pressure, confirming its effectiveness for distributed cloud environments.