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

Fault diagnosis is critical in many domains, as faults may lead to safety threats or economic
losses. In the field of online service systems, operators rely on enormous monitoring data to …

Accurate and efficient localization of root cause instances in large-scale microservice
systems is of paramount importance. Unfortunately, prevailing methods face several …

Automatic failure diagnosis is crucial for large microservice systems. Currently, most failure
diagnosis methods rely solely on single-modal data (ie, using either metrics, logs, or traces) …

In cloud systems, incidents are potential threats to customer satisfaction and business
revenue. When customers are affected by incidents, they often request customer support …

Microservice is a widely-adopted architecture for constructing cloud-native applications. To
test application resiliency, chaos engineering is widely used to inject faults proactively in …

Ensuring the reliability and user satisfaction of cloud services necessitates prompt anomaly
detection followed by diagnosis. Existing techniques for anomaly detection focus solely on …

Ensuring the reliability of cloud systems is critical for both cloud vendors and customers.
Cloud systems often rely on virtualization techniques to create instances of hardware …

The Vision of Autonomic Computing (ACV), proposed over two decades ago, envisions
computing systems that self-manage akin to biological organisms, adapting seamlessly to …

Microservice resilience, the ability of microservices to recover from failures and continue
providing reliable and responsive services, is crucial for cloud vendors. However, the current …