Metric Criticality Identification for Cloud Microservices

For Site Reliability Engineers, alerts are typically the first and often the primary indications that a system may not be performing as expected. Once alerts are triggered, Site Reliability Engineers delve into detailed data across various modalities such as metrics, logs, and traces - to diagnose system issues. However, defining an optimal set of alerts is increasingly challenging due to the sheer volume of multi-modal observability data points in large cloud-native systems. Typically, alerts are manually curated, primarily defined on the metrics modality, and heavily reliant on subject matter experts manually navigating through the large state-space of intricate relationships in multi-modal observability data. Such a process renders defining alerts prone to insufficient coverage, potentially missing critical events. Defining alerts is even more challenging with the shift from traditional monolithic architectures to microservice based architectures due to the intricate interplay between microservices governed by the application topology in an ever stochastic environment. To tackle this issue, we take a data driven approach wherein we propose KIMetrix, a system that relies only on historical metric data and lightweight microservice traces to identify microservice metric criticality. KIMetrix significantly aids Subject Matter Experts by identifying a critical set of metrics to define alerts, averting the necessity of weaving through the vast multi-modal observability sphere. KIMetrix delves deep into the metric-trace coupling and leverages information theoretic measures to recommend microservice-metric mappings in a microservice topology-aware manner. Experimental evaluation on state-of-the-art microservice based applications demonstrates the effectiveness of our approach.