US 11,659,026 B2
Service labeling using semi-supervised learning
Alok Tiagi, Sunnyvale, CA (US); Farzad Ghannadian, Burlingame, CA (US); Karen Hayrapetyan, Fremont, CA (US); Laxmikant Vithal Gunda, San Jose, CA (US); Sunitha Krishna, Saratoga, CA (US); Ashot Aslanyan, Palo Alto, CA (US); and Anirban Sengupta, Saratoga, CA (US)
Assigned to VMWARE, INC., Palo Alto, CA (US)
Filed by VMware, Inc., Palo Alto, CA (US)
Filed on Apr. 22, 2020, as Appl. No. 16/855,305.
Prior Publication US 2021/0336899 A1, Oct. 28, 2021
Int. Cl. H04L 47/78 (2022.01); H04L 47/125 (2022.01); G06K 9/62 (2022.01); H04L 9/40 (2022.01); H04L 41/22 (2022.01); H04L 67/01 (2022.01)
CPC H04L 47/781 (2013.01) [G06K 9/6257 (2013.01); H04L 41/22 (2013.01); H04L 47/125 (2013.01); H04L 63/20 (2013.01); H04L 67/01 (2022.05)] 17 Claims
OG exemplary drawing
 
1. A method of managing workloads, comprising:
from a plurality of workloads each comprising a virtual computing instance (VCI) running a plurality of processes, identifying a group of workloads that have similar respective values associated with a set of respective features;
displaying information about one of the workloads via a user interface, wherein the information comprises one or more of the values associated with the set of respective features;
receiving a label via the user interface in response to the displaying of the information about the one of the workloads;
associating the label with each workload of the group of workloads;
producing a training data set comprising the values associated with the features of each workload in the group of workloads and the associated label;
training a machine learning model using the training data set;
generating, using the machine learning model, a predicted label for a new workload of the plurality of workloads by inputting values associated with the features of the new workload to the machine learning model, wherein the new workload is not a member of the group of workloads; and
assigning the predicted label to the new workload and applying security policies to the new workload based on the assigned label.