A common approach to improving resource utilization in data cen- ters is to adaptively provision resources based on the actual work- load. One fundamental challenge of doing this in microservice man- agement frameworks, however, is that different components of a service can exhibit significant differences in their impact on end-to- end performance. To make resource management more challenging, a single microservice can be shared by multiple online services that have diverse workload patterns and SLA requirements.

We present an efficient resource management system, namely Erms, for guaranteeing SLAs in shared microservice environments. Erms profiles microservice latency as a piece-wise linear function of the workload, resource usage, and interference. Based on this pro- filing, Erms builds resource scaling models to optimally determine latency targets for microservices with complex dependencies. Erms also designs new scheduling policies at shared microservices to fur- ther enhance resource efficiency. Experiments across microservice benchmarks as well as trace-driven simulations demonstrate that Erms can reduce SLA violation probability by 5× and more impor- tantly, lead to a reduction in resource usage by 1.6×, compared to state-of-the-art approaches.