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 …

Software architecture is undergoing a transition from monolithic architectures to
microservices to achieve resilience, agility and scalability in software development …

Large language model (LLM) applications in cloud root cause analysis (RCA) have been
actively explored recently. However, current methods are still reliant on manual workflow …

Computing paradigms have evolved significantly in recent decades, moving from large room-
sized resources (processors and memory) to incredibly small computing nodes. Recently …

With the development of cloud computing technology, the microservice architecture (MSA)
has become a prevailing application architecture in cloud-native applications. Many user …

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 …

Detecting runtime anomalies is very important to monitoring and maintenance of distributed
services. People often use execution logs for troubleshooting and problem diagnosis …

The traditional approach for performance debugging relies upon performance profilers (eg,
gprof, VTune) that provide average function runtime information. These aggregate statistics …