US 11,900,248 B2
Correlating data center resources in a multi-tenant execution environment using machine learning techniques
James S. Watt, Austin, TX (US); Bijan K. Mohanty, Austin, TX (US); and Bhaskar Todi, Madhya Pradesh (IN)
Assigned to Dell Products L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on Oct. 14, 2020, as Appl. No. 17/070,498.
Prior Publication US 2022/0114437 A1, Apr. 14, 2022
Int. Cl. G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06F 16/953 (2019.01); H04L 65/60 (2022.01); H04L 41/16 (2022.01); G06F 18/2433 (2023.01); G06F 16/2455 (2019.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); H04L 65/80 (2022.01); G06N 3/042 (2023.01); G06F 18/211 (2023.01); G06F 18/2135 (2023.01); G06F 18/2413 (2023.01); H04L 65/75 (2022.01); G06Q 10/0637 (2023.01); G06Q 10/0639 (2023.01); G06Q 10/10 (2023.01)
CPC G06N 3/08 (2013.01) [G06F 16/24568 (2019.01); G06F 16/953 (2019.01); G06F 18/2433 (2023.01); G06N 3/04 (2013.01); G06N 3/042 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); H04L 41/16 (2013.01); H04L 65/60 (2013.01); H04L 65/80 (2013.01); G06F 18/211 (2023.01); G06F 18/2135 (2023.01); G06F 18/2413 (2023.01); G06Q 10/0639 (2013.01); G06Q 10/06375 (2013.01); G06Q 10/10 (2013.01); H04L 65/765 (2022.05)] 20 Claims
OG exemplary drawing
 
17. An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured:
to obtain multiple data streams pertaining to one or more data center resources in at least one multi-tenant executing environment;
to correlate one or more portions of the multiple data streams by processing at least a portion of the multiple data streams using at least one multi-tenant-capable search engine comprising at least one database capable of processing at least one of one or more cross-table queries and one or more cross-index queries;
to determine one or more anomalies within the multiple data streams by processing the one or more correlated portions of the multiple data streams using a machine learning-based anomaly detection engine; and
to perform at least one automated action based at least in part on the one or more determined anomalies, wherein performing at least one automated action comprises automatically training at least a portion of the machine learning-based anomaly detection engine using at least a portion of the one or more anomalies.