We predict the conditional distributions of service metrics, such as response time or frame rate, from infrastructure measurements in a networked environment. From such distributions, key statistics of the service metrics, including mean, variance, or quantiles can be computed, which are essential for predicting SLA conformance and enabling service assurance. We present and assess two methods for prediction: (1) mixture models with Gaussian or Lognormal kernels, whose parameters are estimated using mixture density networks, a class of neural networks, and (2) histogram models, which require the target space to be discretized. We apply these methods to a VoD service and a KV store service running on our lab testbed. A comparative evaluation shows the relative effectiveness of the methods when applied to operational data. We find that both methods allow for accurate prediction. While mixture models provide a general and elegant solution, they incur a very high overhead related to hyper-parameter search and neural network training. Histogram models, on the other hand, allow for efficient training, but require adjustment to the specific use case.

中文翻译：

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网络服务的服务度量的条件密度估计

我们根据网络环境中的基础设施测量来预测服务指标的条件分布，例如响应时间或帧速率。从这些分布中，可以计算服务指标的关键统计数据，包括平均值、方差或分位数，这对于预测 SLA 一致性和实现服务保证至关重要。我们提出并评估了两种预测方法：（1）具有高斯或对数正态核的混合模型，其参数使用混合密度网络（一类神经网络）进行估计，以及（2）直方图模型，需要离散化目标空间. 我们将这些方法应用于在我们实验室测试台上运行的 VoD 服务和 KV 存储服务。比较评估显示了这些方法在应用于操作数据时的相对有效性。我们发现这两种方法都可以进行准确的预测。虽然混合模型提供了一种通用且优雅的解决方案，但它们会产生与超参数搜索和神经网络训练相关的非常高的开销。另一方面，直方图模型允许进行有效的训练，但需要针对特定​​用例进行调整。