LatenSeer: Causal Modeling of End-to-End Latency Distributions by Harnessing Distributed Tracing

End-to-end latency estimation in web applications is crucial for system operators to foresee the effects of potential changes, helping ensure system stability, optimize cost, and improve user experience. However, estimating latency in microservices-based architectures is challenging due to the complex interactions between hundreds or thousands of loosely coupled microservices. Current approaches either track only latency-critical paths or require laborious bespoke instrumentation, which is unrealistic for end-to-end latency estimation in complex systems. This paper presents LatenSeer, a modeling framework for estimating end-to-end latency distributions in microservicebased web applications. LatenSeer proposes novel data structures to accurately represent causal relationships between services, overcoming the drawbacks of simple dependency representations that fail to capture the complexity of microservices. LatenSeer leverages distributed tracing data to practically and accurately model end-to-end latency at scale. Our evaluation shows that LatenSeer predicts latency within a 5.35% error, outperforming the state-of-the-art that has an error rate of more than 9.5%.