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 inconsistencies 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%).

Developers use logs to diagnose performance problems in distributed applications. However, it is difficult to know a priori where logs are needed and what information in them is needed to help diagnose problems that may occur in the future. We present the Variance-driven Automated Instrumentation Framework (VAIF), which runs alongside distributed applications. In response to newly-observed performance problems, VAIF automatically searches the space of possible instrumentation choices to enable the logs needed to help diagnose them. To work, VAIF combines distributed tracing (an enhanced form of logging) with insights about how response-time variance can be decomposed on the critical-path portions of requests' traces. We evaluate VAIF by using it to localize performance problems in OpenStack and HDFS. We show that VAIF can localize problems related to slow code paths, resource contention, and problematic third-party code while enabling only 3-34% of the total tracing instrumentation.