Modern micro-service and container-based cloud-native applications have leveraged multi-tenancy as a first class system design concern. The increasing number of co-located services/workloads into server facilities stresses resource availability and system capability in an unconventional and unpredictable manner. To efficiently manage resources in such dynamic environments, run-time observability and forecasting are required to capture workload sensitivities under differing interference effects, according to applied co-location scenarios. While several research efforts have emerged on interference-aware performance modelling, they are usually applied at a very coarse-grained manner e.g., estimating the overall performance degradation of an application, thus failing to effectively quantify, predict or provide educated insights on the impact of continuous runtime interference on per-resource allocations. In this paper, we present Rusty, a predictive monitoring system that leverages the power of Long Short-Term Memory networks to enable fast and accurate runtime forecasting of key performance metrics and resource stresses of cloud-native applications under interference. We evaluate Rusty under a diverse set of interference scenarios for a plethora of representative cloud workloads, showing that Rusty i) achieves extremely high prediction accuracy, average $R^2$R2 value of 0.98, ii) enables very deep prediction horizons retaining high accuracy, e.g., $R^2$R2 of around 0.99 for a horizon of 1 sec ahead and around 0.94 for an horizon of 5 sec ahead, while iii) satisfying, at the same time, the strict latency constraints required to make Rusty practical for continuous predictive monitoring at runtime.

中文翻译：

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Rusty：现代多租户系统的运行时干扰感知预测监控

现代微服务和基于容器的云原生应用程序已经利用多租户作为一流的系统设计关注点。越来越多的服务器设施并置的服务/工作负载以非常规和不可预测的方式强调了资源可用性和系统能力。为了在这种动态环境中有效地管理资源，根据应用的共址场景，需要运行时可观察性和预测来捕获不同干扰影响下的工作负载敏感性。虽然在干扰感知性能建模方面已经出现了一些研究成果，但它们通常以非常粗粒度的方式应用，例如，估计应用程序的整体性能下降，因此无法有效量化，预测或提供有关持续运行时干扰对每个资源分配的影响的有根据的见解。在本文中，我们介绍了 Rusty，这是一种预测监控系统，它利用长短期内存网络的强大功能，能够快速准确地预测受干扰的云原生应用程序的关键性能指标和资源压力。我们在大量具有代表性的云工作负载的各种干扰场景下评估了 Rusty，表明 Rusty i) 实现了极高的预测准确度，平均 一个预测监控系统，利用长短期内存网络的力量，能够快速准确地预测受干扰的云原生应用程序的关键性能指标和资源压力。我们在大量具有代表性的云工作负载的各种干扰场景下评估了 Rusty，表明 Rusty i) 实现了极高的预测准确度，平均 一个预测监控系统，利用长短期内存网络的力量，能够快速准确地预测受干扰的云原生应用程序的关键性能指标和资源压力。我们在大量具有代表性的云工作负载的各种干扰场景下评估了 Rusty，表明 Rusty i) 实现了极高的预测准确度，平均$R^2$电阻2 0.98 的值，ii) 使非常深的预测范围保持高精度，例如， $R^2$电阻2 在 1 秒的范围内约为 0.99，在 5 秒的范围内约为 0.94，同时 iii) 同时满足严格的延迟限制，使 Rusty 在运行时进行连续预测监控实用。