A common approach to improving resource utilization in data centers is to adaptively provision resources based on the actual workload. One fundamental challenge of doing this in microservice management 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 with high probability in shared microservice environments. Erms profiles microservice latency as a piece-wise linear function of the workload, resource usage, and interference. Based on this profiling, 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 further 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 importantly, lead to a reduction in resource usage by 1.6×, compared to state-of-the-art approaches.

提高数据中心资源利用率的常见方法是根据实际工作负载自适应地配置资源。然而，在微服务管理框架中执行此操作的一个基本挑战是，服务的不同组件对端到端性能的影响可能表现出显着差异。为了使资源管理更具挑战性，单个微服务可以由具有不同工作负载模式和 SLA 要求的多个在线服务共享。

我们提出了一种高效的资源管理系统，即 Erms，用于在共享微服务环境中以高概率保证 SLA。Erms 将微服务延迟描述为工作负载、资源使用和干扰的分段线性函数。基于此分析，Erms 构建资源扩展模型，以最佳方式确定具有复杂依赖项的微服务的延迟目标。Erms还在共享微服务上设计了新的调度策略，以进一步提高资源效率。跨微服务基准测试以及跟踪驱动的模拟的实验表明，与最先进的方法相比，Erms 可以将 SLA 违规概率降低 5 倍，更重要的是，可以将资源使用量减少 1.6 倍。