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Microservices have been a cornerstone for building scalable, flexible, and robust applications, thereby enabling service providers to enhance their systems' resilience and fault tolerance. However, adopting this architecture has often led to many challenges, particularly when pinpointing performance bottlenecks and diagnosing their underlying causes. Various tools have been developed to bridge this gap and facilitate comprehensive observability in microservice ecosystems. While these tools are effective at detecting latency-related anomalies, they often fall short of isolating the root causes of these problems. In this paper, we present a novel method for identifying and analyzing performance anomalies in microservice-based applications by leveraging cross-layer tracing techniques. Our method uniquely integrates system resource metrics-such as CPU, disk, and network consumption-with each user request, providing a multi-dimensional view for diagnosing performance issues. Through the use of sequential pattern mining, this method effectively isolates aberrant execution behaviors and helps identify their root causes. Our experimental evaluations demonstrate its efficiency in diagnosing a wide range of performance anomalies.
Belkhiri et al. (Tue,) studied this question.