This article analyzes the shortcomings of fragmented monitoring systems, defines key metrics such as MTTA and MTTR, proposes a unified alert convergence architecture using Redis delayed queues, and details design, implementation, and future AI‑enhanced improvements to reduce alert fatigue and accelerate incident response.
This article explains the background and challenges of legacy monitoring systems, defines key concepts such as exceptions, problems, alerts and recoveries, introduces critical metrics like MTTA and MTTR, and details the design, architecture, and core implementation of a unified alert convergence service using Redis delay queues.
The paper presents a unified alert convergence system that centralizes metric calculation, detection, and alarm handling across monitoring subsystems, employing mechanisms such as convergence, claiming, silencing, escalation, and a Redis‑based delayed queue integrated via Kafka or REST to reduce alarm fatigue, improve MTTA/MTTR, and enable future AI‑driven AIOps.
