Advancements in Automated Incident Management: A Survey within Cloud-Native SRE (Site Reliability Engineering) Practices

Abstract

Cloud-native environments that are central to modern infrastructures have, to a large extent, increased the complexity of system management, thereby making the traditional methods of incident management less efficient in terms of reliability and scalability. SRE as a technology to a large extent has been very influential in bringing together the elements of automation, observability, and AI in ensuring the delivery of essential services across the network of distributed systems. This article reviews the transition to cloud-native SRE automated incident management solutions featuring predictive incident detection, intelligent alert correlation, and self-healing mechanisms. Through AI and ML, the SRE team is enabled to uncover anomalies before they happen, eliminate noise, perform root cause analysis, and execute the recovery process in an automated manner over hybrid and multi-cloud setups. Observability integration through Prometheus, Grafana, and ELK allows for real-time system monitoring and performance tuning. In general, the transition to automated and AI-involved strategies has been instrumental in the decrease of MTTD and MTTR resulting in an increase of the scalability, reliability, and resilience of cloud-native operations. The research also points to the importance of feedback-driven learning models in proactive recovery enhancement. These innovations at the core of operational resilience capabilities serve as a vehicle for the next level of autonomous, intelligent SRE ecosystems.