Cloud computing and multi-core processors are emerging to dominate the landscape of computing today. However, in terms of computing resources, the utilization of modern datacenters is rather low because of the potential negative and unpredictable cross-core performance interference. To provide QoS guarantees for some key applications, co-locations of such applications are disabled, causing computing resource overprovisioning. Therefore precise analysis for cross-core interference is a key challenge for improving resource utilization in datacenters. This study is motivated by the observation that the performance degradation of one application suffered from cross-core interference can be represented as a piecewise function of the aggregate pressures on memory subsystem from all cores, regardless of which applications are co-running and what their individual pressures are. The study results in a statistical learning-based method for predicting cross-core performance interference as well as predictor models using PCA linear regression, which can quantitatively and precisely predict performance degradation caused by memory subsystem contention in any applications. Experimental results show that the average prediction error of the proposed method is 1.1%.%普遍认为,云计算和多核处理器将会统治计算领域的未来.但是,目前云计算数据中心的计算资源使用率非常低,其主要原因在于多核处理器上存在严重且不可预知的性能干扰.为了保证关键应用程序的QoS,只能禁止这些关键程序与其他程序共同运行,导致了资源的过度分配.为了提高数据中心的利用率,分析多核间的性能干扰成为一个关键的问题.观察到程序遭受的核间性能干扰可以表示为内存子系统总压力的线性分段函数,而与构成压力的具体应用程序无关.以此观察为基础,提出了一种基于统计学习的多核间性能干扰分析方法,使用主成分线性回归的方法获得干扰模型,可以精确且定量地预测任意程序由于内存子系统资源竞争导致的性能下降.实验结果表明,平均预测误差仅为1.1%.
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