Group clustering using inter-group dissimilarities

Patent metadata

Field	Value
Publication number	US-2019228097-A1
Application number	US-201815877977-A
Country	US
Kind code	A1
Filing date	Jan 23, 2018
Priority date	Jan 23, 2018
Publication date	Jul 25, 2019
Grant date	—

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.

First claim

We claim:

1 . A method for improving performance of one or more machines within a system, the method comprising:
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; and 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.

2 . The method of claim 1 , further comprising:
receiving the performance-related data from the one or more machines in the system, prior to the forming.

3 . The method of claim 1 , further comprising:
comparing each group of the plurality of groups with all other groups of the plurality of groups.

4 . The method of claim 3 , further comprising:
generating, based on the comparing, a group of dissimilarity weights for each group of the plurality of groups prior to the clustering, each group of dissimilarity weights corresponding to dissimilarities between a corresponding group and all other groups in the plurality of groups.

5 . The method of claim 1 , further comprising:
comparing each group in the plurality of groups with each of the plurality of groups.

6 . The method of claim 5 , further comprising:
generating, based on the comparing, a group of dissimilarity weights for each group of the plurality of groups prior to the clustering, each dissimilarity weight in the group of dissimilarity weights corresponding to dissimilarities between a corresponding group and another group in the plurality of groups.

7 . The method of claim 1 , wherein adjusting the configuration of the system comprises adjusting a configuration of the one or more machines.

8 . The method of claim 1 , wherein adjusting the configuration of the system comprises changing a hardware configuration of or adding a hardware element to one or more machines in the system.

9 . A non-transitory computer readable medium comprising instructions to be executed in a computer system, wherein the instructions when executed in the computer system perform a method for improving performance of one or more machines within a system, the method comprising:
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; and 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.

10 . The non-transitory computer readable medium of claim 9 , wherein the method further comprises:
receiving the performance-related data from the one or more machines in the system, prior to the forming.

11 . The non-transitory computer readable medium of claim 9 , wherein the method further comprises:
comparing each group of the plurality of groups with all other groups of the plurality of groups.

12 . The non-transitory computer readable medium of claim 11 , wherein the method further comprises:
generating, based on the comparing, a group of dissimilarity weights for each group of the plurality of groups prior to the clustering, each group of dissimilarity weights corresponding to dissimilarities between a corresponding group and all other groups in the plurality of groups.

13 . The non-transitory computer readable medium of claim 9 , wherein the method further comprises:
comparing each group in the plurality of groups with each of the plurality of groups.

14 . The non-transitory computer readable medium of claim 13 , wherein the method further comprises:
generating, based on the comparing, a group of dissimilarity weights for each group of the plurality of groups prior to the clustering, each dissimilarity weight in the group of dissimilarity weights corresponding to dissimilarities between a corresponding group and another group in the plurality of groups.

15 . The non-transitory computer readable medium of claim 9 , wherein adjusting the configuration of the system comprises adjusting a configuration of the one or more machines.

16 . The non-transitory computer readable medium of claim 9 , wherein adjusting the configuration of the system comprises changing a hardware configuration of or adding a hardware element to one or more machines in the system.

17 . A computer system, wherein system software for the computer system is programmed to execute a method for improving performance of one or more machines within a system, the method comprising:
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; and 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.

18 . The computer system of claim 17 , wherein the method further comprises:
receiving the performance-related data from the one or more machines in the system, prior to the forming.

19 . The computer system of claim 17 , wherein the method further comprises:
comparing each group of the plurality of groups with all other groups of the plurality of groups.

20 . The computer system of claim 19 , wherein the method further comprises:
generating, based on the comparing, a group of dissimilarity weights for each group of the plurality of groups prior to the clustering, each group of dissimilarity weights corresponding to dissimilarities between a corresponding group and all other groups in the plurality of groups.

21 . The computer system of claim 17 , wherein the method further comprises:
comparing each group in the plurality of groups with each of the plurality of groups.

22 . The computer system of claim 21 , wherein the method further comprises:
generating, based on the comparing, a group of dissimilarity weights for each group of the plurality of groups prior to the clustering, each dissimilarity weight in the group of dissimilarity weights corresponding to dissimilarities between a corresponding group and another group in the plurality of groups.

23 . The computer system of claim 17 , wherein adjusting the configuration of the system comprises adjusting a configuration of the one or more machines.

24 . The computer system of claim 17 , wherein adjusting the configuration of the system comprises changing a hardware configuration of or adding a hardware element to one or more machines in the system.

Assignees

• Vmware Inc

Inventors

• Kassa Debessay Fesehaye
• Singaravelu Lenin
• Huang Xiaobo
• Banerjee Amitabha
• Zhou Ruijin

Classifications

• H04L41/0813

  characterised by the conditions triggering a change of settings · CPC title

• H04L67/1097

  for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS] · CPC title

• G06F16/285 Primary

  Clustering or classification · CPC title

• G06F16/256

  in federated or virtual databases · CPC title

• G06F16/283

  Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP · CPC title