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AUTOMATIC IDENTIFICATION OF WORKLOADS CONTRIBUTING TO SYSTEM PERFORMANCE DEGRADATION USING MACHINE LEARNING TECHNIQUES

机译:使用机器学习技术自动识别有助于系统性能劣化的工作量

摘要

Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to system performance degradation are provided herein. An example computer-implemented method includes obtaining, in connection with a system exhibiting performance degradation, a primary time series and a set of multiple candidate time series; calculating, using machine learning, similarity measurements between the primary time series and each time series in the set; for each measurement, assigning weights to the time series based on similarity to the primary time series relative to the other time series in the set; generating, for each time series in the set, a similarity score based on the weights assigned across the similarity measurements; and outputting, based on the similarity scores, identification of a candidate time series for use in automated actions.
机译:本文提供了用于自动识别有助于系统性能下降的工作负载的方法,装置和处理器可读存储介质。一种示例性计算机实现的方法包括与表现出性能劣化的系统,主要时间序列和一组多个候选时间序列的系统获得;计算,使用机器学习,主要时间序列和集合中的每个时间序列之间的相似性测量;对于每个测量,基于相对于集合中的另一个时间序列的主要时间序列的相似性为时间序列分配给时间序列;对于该组中的每个时间序列,生成基于相似度测量的重量的相似性分数;并根据相似性分数输出识别用于自动操作的候选时间序列。

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