The proliferation of services and service interactions within microservices and cloud-native
applications, makes it harder to detect failures and to identify their possible root causes …

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big
data generated by IT Operations processes, particularly in cloud infrastructures, to provide …

A microservice system in industry is usually a large-scale distributed system consisting of
dozens to thousands of services running in different machines. An anomaly of the system …

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 …

The complexity and dynamism of microservices pose significant challenges to system
reliability, and thereby, automated troubleshooting is crucial. Effective root cause localization …

Microservice architectures have become the lifeblood of modern service-oriented software
systems. Remote Procedure Calls (RPCs) among microservices are deeply nested …

Microservice architecture is applied by an increasing number of systems because of its
benefits on delivery, scalability, and autonomy. It is essential but challenging to localize root …

In large-scale online service systems, software changes are inevitable and frequent. Due to
importing new code or configurations, changes are likely to incur incidents and destroy user …

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 …

Multivariate time series (MTS) data are becoming increasingly ubiquitous in networked
systems, eg, IoT systems and 5G networks. Anomaly detection in MTS refers to identifying …