Systemizing and mitigating topological inconsistencies in Alibaba’s microservice call-graph datasets
Darby Huye, Lan Liu, and Raja R. Sambasivan
In ACM/SPEC International Conference on Performance Engineering, May 2024
Alibaba’s 2021 and 2022 microservice datasets are the only publicly available sources of request-workflow traces from a large-scale microservice deployment. They have the potential to strongly influence future research as they provide much-needed visibility into industrial microservices’ characteristics. We conduct the first systematic analyses of both datasets to help facilitate their use by the community. We find that the 2021 dataset contain numerous inconsistencies preventing reconstruction of full trace topologies. The 2022 dataset also suffers from inconsistencies, but at a much lower rate. Tools that strictly follow Alibaba’s specs for constructing traces from these datasets will silently ignore these inconsistencies, misinforming researchers by creating traces of the wrong sizes and shapes. We present \casper, a construction method that uses redundancies in the datasets to sidestep the inconsistencies. We show that constructing traces using \casper results in different trace characteristics than other less-informed methods.
