Context: The aim of statistical causal inference (SCI) methods is to estimate causal effects
from observational data (ie, when randomized controlled trials are not possible). In this …

Widely adopted for their scalability and flexibility, modern microservice systems present
unique failure diagnosis challenges due to their independent deployment and dynamic …

Most cloud applications use a large number of smaller sub-components (called
microservices) that interact with each other in the form of a complex graph to provide the …

Fault localization is challenging in an online service system due to its monitoring data's large
volume and variety and complex dependencies across/within its components (eg, services …

Microservices architecture is a new approach for deploying applications and services in the
cloud, gaining popularity for constructing large-scale systems that are highly resilient, robust …

Effectively localizing root causes of performance anomalies is crucial to enabling the rapid
recovery and loss mitigation of microservice applications in the cloud. Depending on the …

Root cause analysis (RCA) in large-scale microservice systems is a critical and challenging
task. To understand and localize root causes of unexpected faults, modern observability …

Distributed databases are fundamental infrastructures of today's large-scale software
systems such as cloud systems. Detecting anomalies in distributed databases is essential …

Detecting failures and identifying their root causes promptly and accurately is crucial for
ensuring the availability of microservice systems. A typical failure troubleshooting pipeline …

Microservices architecture improves software scalability, resilience, and agility but also
poses significant challenges to system reliability due to their complexity and dynamic nature …