We present Twine, Facebook's cluster management system which has been running in production for the past decade. Twine has helped convert our infrastructure from a collection of siloed pools of customized machines dedicated to individual workloads, into a large-scale shared infrastructure with fungible hardware. Our goal of ubiquitous shared infrastructure leads us to some decisions counter to common practices. For instance, rather than deploying an isolated control plane per cluster, Twine scales a single control plane to manage one million machines across all data centers in a geographic region and transparently move jobs across clusters. Twine accommodates workload-specific customization in shared infrastructure, and this approach further departs from common practices. The TaskControl API allows an application to collaborate with Twine to handle container lifecycle events, e.g., restarting a ZooKeeper deployment's followers first and its leader last during a rolling upgrade. Host profiles capture hardware and OS settings that workloads can tune to improve performance and reliability; Twine dynamically allocates machines to workloads and switches host profiles accordingly. Finally, going against the conventional wisdom of prioritizing stacking workloads on big machines to increase utilization, we universally deploy power-efficient small machines outfit with a single CPU and 64GB RAM to achieve higher performance per watt, and we leverage autoscaling to improve machine utilization. We describe the design of Twine and share our experience in migrating Facebook's workloads onto shared infrastructure.

This paper presents Canopy, Facebook's end-to-end performance tracing infrastructure. Canopy records causally related performance data across the end-to-end execution path of requests, including from browsers, mobile applications, and backend services. Canopy processes traces in near real-time, derives user-specified features, and outputs to performance datasets that aggregate across billions of requests. Using Canopy, Facebook engineers can query and analyze performance data in real-time. Canopy addresses three challenges we have encountered in scaling performance analysis: supporting the range of execution and performance models used by different components of the Facebook stack; supporting interactive ad-hoc analysis of performance data; and enabling deep customization by users, from sampling traces to extracting and visualizing features. Canopy currently records and processes over 1 billion traces per day. We discuss how Canopy has evolved to apply to a wide range of scenarios, and present case studies of its use in solving various performance challenges.