Vmware, Inc.

Xiaobo Huang

Lenin Singaravelu

Ruijin Zhou

Amitabha Banerjee

Chien-Chia Chen

Debessay Fesehaye Kassa

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.

Group clustering using inter-group dissimilarities

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.

Growing community of inventors

Palo Alto, CA, United States of America

Xiaobo Huang