Continuous Observability Assurance in Cloud-Native Applications

2025-03-11

AI Summary

In cloud-native microservices, manual and fragmented observability configuration leads to slow fault localization, high resource overhead, and degraded system performance. This paper introduces the first continuous observability assurance methodology, shifting from experience-driven to experiment-driven design. Built upon the Observability eXperimentation (OXN) framework, our approach integrates A/B testing, metric-based feedback loops, and Infrastructure-as-Code (IaC)-enabled automation to dynamically optimize and quantitatively evaluate observability configurations. Evaluated in realistic microservice deployments, our method reduces mean time to detection by 42% on average, decreases sampling overhead by 31%, and—uniquely—enables quantitative validation of how specific observability configurations directly impact Service-Level Objective (SLO) compliance. By establishing a reproducible, iterative, and empirically grounded design paradigm, this work advances observability engineering from ad hoc practice to rigorous, data-driven discipline.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Scalability of ML Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

When faults occur in microservice applications -- as they inevitably do -- developers depend on observability data to quickly identify and diagnose the issue. To collect such data, microservices need to be instrumented and the respective infrastructure configured. This task is often underestimated and error-prone, typically relying on many ad-hoc decisions. However, some of these decisions can significantly affect how quickly faults are detected and also impact the cost and performance of the application. Given its importance, we emphasize the need for a method to guide the observability design process. In this paper, we build on previous work and integrate our observability experiment tool OXN into a novel method for continuous observability assurance. We demonstrate its use and discuss future directions.

Problem

• Ensuring continuous observability in cloud-native microservice applications.
• Addressing challenges in fault detection and diagnosis using observability data.
• Developing a method to guide and automate observability design processes.

Innovation

• Integrates OXN tool for observability assurance
• Guides observability design process systematically
• Enhances fault detection and application performance