Giles Winchester | Searcharxiv

arXiv2025

Complexity at Scale: A Quantitative Analysis of an Alibaba Microservice Deployment

Microservice management and testbed research often rests on assumptions about deployments that have rarely been validated at production scale. While recent studies have begun to characterise production microservice deployments, they are often limited in breadth, do not compare findings across deployments, and lack consideration of the implications of findings for commonly held assumptions. We analyse a distributed tracing dataset from Alibaba's production microservice deployment to examine its scale, heterogeneity, and dynamicity. By comparing our findings to prior measurements of Meta's MSA we illustrate both convergent and divergent properties, clarifying which patterns may generalise. Our study reveals extreme architectural scale, long-tail distributions of workloads and dependencies, highly diverse functionality, substantial call graph variability, and pronounced time-varying behaviour which diverge from assumptions underlying research models and testbeds. We summarise how these observations challenge common assumptions in research on fault management, scaling, and testbed design, and outline recommendations for more realistic future approaches and evaluations.

cs.DC↗

arXiv2025

FC-ADL: Efficient Microservice Anomaly Detection and Localisation Through Functional Connectivity

Microservices have transformed software architecture through the creation of modular and independent services. However, they introduce operational complexities in service integration and system management that makes swift and accurate anomaly detection and localisation challenging. Despite the complex, dynamic, and interconnected nature of microservice architectures, prior works that investigate metrics for anomaly detection rarely include explicit information about time-varying interdependencies. And whilst prior works on fault localisation typically do incorporate information about dependencies between microservices, they scale poorly to real world large-scale deployments due to their reliance on computationally expensive causal inference. To address these challenges we propose FC-ADL, an end-to-end scalable approach for detecting and localising anomalous changes from microservice metrics based on the neuroscientific concept of functional connectivity. We show that by efficiently characterising time-varying changes in dependencies between microservice metrics we can both detect anomalies and provide root cause candidates without incurring the significant overheads of causal and multivariate approaches. We demonstrate that our approach can achieve top detection and localisation performance across a wide degree of different fault scenarios when compared to state-of-the-art approaches. Furthermore, we illustrate the scalability of our approach by applying it to Alibaba's extremely large real-world microservice deployment.

cs.SE↗