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SLOWPOKE is a new system to accurately quantify the effects of hypothetical optimizations on end-to-end throughput for microservice applications, without relying on tracing or a priori knowledge of the call graph. Microservice operators can use SLOWPOKE to ask what-if performance analysis questions of the form "What throughput could my retail application sustain if I optimized the shopping cart service from 10K req/s to 20K req/s?". Given a target service and its hypothetical optimization, SLOWPOKE employs a perfor- mance model that determines how to selectively slow down non-target services to preserve the relative effect of the optimization. It then performs profiling experiments to predict the end-to-end throughput, as if the optimization had been implemented. Applied to four real-world microservice applications, SLOWPOKE accurately quantifies optimization effects with a root mean squared error of only 2.07%. It is also effective in more complex scenarios, e.g., predicting throughput after scaling optimizations or when bottlenecks arise from mutex contention. Evaluated in large-scale deployments of 45 nodes and 108 synthetic benchmarks, SLOWPOKE further demonstrates its scalability and coverage of a wide range of microservice characteristics. 

We revisit the gap between what distributed systems need from the transport layer and what protocols in wide deployment provide. Such a gap complicates the implementation of distributed systems and impacts their performance. We introduce Tunable Multicast Communication (TMC), an abstraction that allows developers to easily specialize communication channels in distributed systems. TMC is presented as a deployable and extensible user-space library that exposes high-level tunable guarantees. TMC has the potential of improving the performance of distributed applications with minimal-to-zero development and deployment effort.