Analyzing Cross-Core Performance Interference on Multi-Core Processors Based on Statistical Learning

Abstract

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%.