Uploaded June 2026 | Updated September 2026, 3 weeks ago
Slowpoke: End-to-end Throughput Optimization Modeling for Microservice Applications
Yizheng Xie, Di Jin, and Oğuzhan Çölkesen, Brown University; Vasiliki Kalavri and John Liagouris, Boston University; Nikos Vasilakis, Brown University
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 performance 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.
View the full NSDI '26 program at usenix.org/conference/nsdi26/technical-sessions
Slowpoke: End-to-end Throughput Optimization Modeling for Microservice Applications
Yizheng Xie, Di Jin, and Oğuzhan Çölkesen, Brown University; Vasiliki Kalavri and John Liagouris, Boston University; Nikos Vasilakis, Brown University
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 performance 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.
View the full NSDI '26 program at usenix.org/conference/nsdi26/technical-sessions
