US 12,443,500 B1
Anomaly detection for a microservices platform
Vinay Sawal, Fremont, CA (US); Jason Liu, Wellesley, MA (US); Amihai Savir, Newton, MA (US); Deepak Krishna, Newcastle, WA (US); Alice Jiang, Waltham, MA (US); and Jordan Leventis, College Station, TX (US)
Assigned to DELL PRODUCTS L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on Apr. 5, 2024, as Appl. No. 18/627,626.
Int. Cl. G06F 11/30 (2006.01); G06F 11/07 (2006.01); H04L 67/02 (2022.01)
CPC G06F 11/3006 (2013.01) [G06F 11/079 (2013.01); H04L 67/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A device, comprising:
at least one processor; and
at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, comprising:
receiving a dependency graph that characterizes microservices of a microservices platform as nodes of the dependency graph and associated interactions that occur during run time execution of the microservices as edges of the dependency graph;
receiving an anomaly pattern indicative of a node and edge pattern that was identified, according to a defined criterion, to be problematic for operation of the microservices platform;
generating an embedding space for the dependency graph, wherein the embedding space comprises dimensions representing different properties of the microservices, and wherein a subgraph, representing a portion of the dependency graph, is represented in the embedding space by a first multidimensional vector having first respective values for the dimensions;
transforming the anomaly pattern to a second multidimensional vector of the embedding space having second respective values for the dimensions; and
determining that the anomaly pattern exists in the dependency graph in response to a determination that each of the first respective values is each greater than an associated one of the second respective values.