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公开(公告)号：US10706079B2
公开(公告)日：2020-07-07
申请号：US15877977
申请日：2018-01-23
Applicant: VMware, Inc.
Inventor： Debessay Fesehaye Kassa , Lenin Singaravelu , Xiaobo Huang , Amitabha Banerjee , Ruijin Zhou
Abstract: Certain embodiments described herein are generally directed to improving performance of one or more machines within a system by clustering multidimensional datasets relating to the performance of the machines using inter-group dissimilarities between groups of the dataset. The method for improving performance of one or more machines within a system, includes forming a multidimensional dataset having a plurality of groups using performance related data associated with one or more machines in the system, clustering the plurality of groups into one or more clusters based on intergroup dissimilarities between the plurality of groups, identifying one or more anomalous clusters from among the one or more clusters, identifying the one or more anomalous groups in the one or more anomalous clusters, and adjusting a configuration of the system to improve the performance of the one or more machines corresponding to the one or more anomalous groups.
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公开(公告)号：US10554514B2
公开(公告)日：2020-02-04
申请号：US15335310
申请日：2016-10-26
Applicant: VMware, Inc.
Inventor： Chien-Chia Chen , Lenin Singaravelu , Ruijin Zhou , Xiaobo Huang
IPC: H04L12/26
Abstract: Exemplary methods, apparatuses, and systems include receiving time series data for each of a plurality of performance metrics. The time series data is sorted into buckets based upon an amount of variation of time series data values for each performance metric. The time series data in each bucket is divided into first and second clusters of time series data points. The bucket having the greatest distance between clusters is used to determine a performance metric having a greatest distance between clusters. The performance metric having the greatest distance between clusters is reported as a potential root cause of a performance issue.
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