Large-scale datacenters often host latency-sensitive services that have stringent Quality-of-Service requirement and experience diurnal load pattern. Co-locating best-effort applications that have no QoS requirement with the latency-sensitive services has been widely used to improve the resource utilization of datacenters with careful shared resource management. However, existing co-location techniques tend to result in the power overload problem on power constrained servers due to the ignorance of the power consumption. To this end, we propose Sturgeon, a runtime system proactively manages resources between co-located applications in a power constrained environment, to ensure the QoS of latency-sensitive services while maximizing the throughput of best-effort applications. Our investigation shows that, at a given load, there are multiple feasible resource configurations to meet both QoS requirement and power budget, while one of them yields the maximum throughput of best-effort applications. To find such a configuration, we establish models to accurately predict the performance and power consumption of the co-located applications. Sturgeon monitors the QoS of the services periodically, in order to eliminate the potential QoS violation caused by the unpredictable interference. Besides, when the datacenter hosts different types of applications to perform co-location, Sturgeon places applications with their preferable candidates to improve the overall throughput. The experimental results show that at server level Sturgeon improves the throughput of the best-effort application by 25.43 percent compared to the state-of-the-art technique, while guaranteeing the 95%-ile latency within the QoS target; at cluster level, Sturgeon improves the overall throughput of best-effort applications by 13.74 percent compared to the baseline.

中文翻译：

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用于提高功率受限数据中心资源利用率的自适应偏好感知协同定位

大型数据中心通常托管对延迟敏感的服务，这些服务具有严格的服务质量要求并经历昼夜负载模式。将没有 QoS 要求的尽力而为的应用程序与延迟敏感的服务共置已被广泛用于通过谨慎的共享资源管理来提高数据中心的资源利用率。然而，由于对功耗的无知，现有的主机托管技术往往会导致功率受限服务器上的功率过载问题。为此，我们提出了 Sturgeon，一个运行时系统在功率受限的环境中主动管理协同定位应用程序之间的资源，以确保延迟敏感服务的 QoS，同时最大限度地提高尽力而为应用程序的吞吐量。我们的调查表明，在给定的负载下，有多种可行的资源配置可以满足 QoS 要求和功率预算，而其中之一可以产生尽力而为应用程序的最大吞吐量。为了找到这样的配置，我们建立了模型来准确预测协同定位应用程序的性能和功耗。Sturgeon 定期监控服务的 QoS，以消除不可预测的干扰导致的潜在 QoS 违规。此外，当数据中心托管不同类型的应用程序以执行共存时，Sturgeon 将应用程序与其优选的候选对象一起放置以提高整体吞吐量。实验结果表明，与最先进的技术相比，在服务器级别 Sturgeon 将尽力而为应用程序的吞吐量提高了 25.43%，同时保证 QoS 目标内的 95%-ile 延迟；在集群级别，与基准相比，Sturgeon 将尽力而为应用程序的整体吞吐量提高了 13.74%。