Systemizing and Mitigating Topological Inconsistencies in Alibaba’s Microservice Call-graph Datasets

Abstract

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 contains numerous inconsistencies preventing accurate 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. Tools that discard traces with inconsisten- cies will discard many traces. We present Casper, a construction method that uses redundancies in the datasets to sidestep the inconsistencies. Compared to an approach that discards traces with inconsistencies, Casper accurately reconstructs an additional 25.5% of traces in the 2021 dataset (going from 58.32% to 83.82%) and an additional 12.18% in the 2022 dataset (going from 86.42% to 98.6%).

Bio:

Raja is an assistant professor in the CS department at Tufts