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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Predicting Cross-Core Performance Interference on Multicore Processors with Regression Analysis
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Predicting Cross-Core Performance Interference on Multicore Processors with Regression Analysis

机译：使用回归分析预测多核处理器上的跨核性能干扰

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摘要

Despite their widespread adoption in cloud computing, multicore processors are heavily under-utilized in terms of computing resources. To avoid the potential for negative and unpredictable interference, co-location of a latency-sensitive application with others on the same multicore processor is disallowed, leaving many cores idle and causing low machine utilization. To enable co-location while providing QoS guarantees, it is challenging but important to predict performance interference between co-located applications. We observed that the performance degradation of an application can be represented as a piecewise predictor function of the aggregate pressures on shared resources from all cores. Based on this observation, we propose to adopt regression analysis to build a predictor function for an application. Furthermore, the prediction model thus obtained for an application is able to characterize its contentiousness and sensitivity. Validation using a large number of single-threaded and multi-threaded benchmarks and nine real-world datacenter applications on two different platforms shows that our approach is also precise, with an average error not exceeding 0.4 percent.
机译：尽管多核处理器已在云计算中得到广泛采用，但在计算资源方面仍未得到充分利用。为了避免潜在的负面影响和不可预测的干扰，不允许将对延迟敏感的应用程序与其他应用程序放在同一多核处理器上，这可能会导致许多内核处于空闲状态并降低机器利用率。为了在提供QoS保证的同时启用同一地点，对同一地点的应用程序之间的性能干扰进行预测很困难，但很重要。我们观察到，应用程序的性能下降可以表示为所有核心对共享资源的总压力的分段预测函数。基于此观察，我们建议采用回归分析为应用程序构建预测函数。此外，由此获得的针对应用程序的预测模型能够表征其争议性和敏感性。在两个不同平台上使用大量单线程和多线程基准测试以及9个实际数据中心应用程序进行的验证表明，我们的方法也很精确，平均误差不超过0.4％。

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