Raja R. Sambasivan

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%).

The microservice architecture is a novel paradigm for building and operating distributed applications in many organizations. This paradigm changes many aspects of how distributed applications are built, managed, and operated in contrast to monolithic applications. It introduces new challenges to solve and requires changing assumptions about previously well-known ones. But, today, the characteristics of large-scale microservice architectures are invisible outside their organizations, depressing opportunities for research. Recent studies provide only partial glimpses and represent only single design points. This paper enriches our understanding of large-scale microservices by characterizing Meta’s microservice architecture. It focuses on previously unreported (or underreported) aspects important to developing and researching tools that use the microservice topology or traces of request workflows. We find that the topology is extremely heterogeneous, is in constant flux, and includes software entities that do not cleanly fit in the microservice architecture. Request work- flows are highly dynamic, but local properties can be predicted using service and endpoint names. We quantify the impact of obfuscating factors in microservice measurement and conclude with implications for tools and future-work opportunities.

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 applica- tions. In response to newly-observed performance problems, VAIF automatically searches the space of possible instrumen- tation 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.

Workflow-centric tracing captures the workflow of causally- related events (e.g., work done to process a request) within and among the components of a distributed system. As distributed systems grow in scale and complexity, such tracing is becoming a critical tool for understanding distributed system behavior. Yet, there is a fundamental lack of clarity about how such infrastructures should be designed to provide maximum benefit for important management tasks, such as resource ac- counting and diagnosis. Without research into this important issue, there is a danger that workflow-centric tracing will not reach its full potential. To help, this paper distills the design space of workflow-centric tracing and describes key design choices that can help or hinder a tracing infrastructure’s utility for important tasks. Our design space and the design choices we suggest are based on our experiences developing several previous workflow-centric tracing infrastructures.

The causes of performance changes in a distributed system often elude even its developers. This paper de- velops a new technique for gaining insight into such changes: comparing request flows from two executions (e.g., of two system versions or time periods). Build- ing on end-to-end request-flow tracing within and across components, algorithms are described for identifying and ranking changes in the flow and/or timing of request pro- cessing. The implementation of these algorithms in a tool called Spectroscope is evaluated. Six case studies are presented of using Spectroscope to diagnose perfor- mance changes in a distributed storage service caused by code changes, configuration modifications, and compo- nent degradations, demonstrating the value and efficacy of comparing request flows. Preliminary experiences of using Spectroscope to diagnose performance changes within select Google services are also presented.