US 12,333,398 B2
Integration optimization using machine learning algorithms
Navin Kumar Neithalath, Bangalore (IN); Navnit Varu, Austin, TX (US); Venkata Kishore Kumar Reddy Chintham, Kadapa (IN); Bijan Kumar Mohanty, Austin, TX (US); and Hung Dinh, Austin, TX (US)
Assigned to Dell Products L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on Sep. 18, 2020, as Appl. No. 17/025,023.
Prior Publication US 2022/0092481 A1, Mar. 24, 2022
Int. Cl. G06N 5/01 (2023.01); G06N 5/04 (2023.01); G06N 7/01 (2023.01); G06N 20/20 (2019.01)
CPC G06N 20/20 (2019.01) [G06N 5/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus comprising:
at least one processing platform comprising a plurality of processing devices;
said at least one processing platform being configured:
to train one or more machine learning models with data comprising a plurality of runtime metrics associated with at least one process;
to receive configuration data comprising a plurality of parameters of at least one computing environment, wherein one or more of the plurality of parameters correspond to integration of one or more elements of the at least one computing environment with one or more other elements at least one of within and external to the at least one computing environment;
to analyze the plurality of parameters to detect one or more anomalies in the configuration data;
to input the configuration data and input the one or more detected anomalies to the one or more machine learning models;
to determine, using the one or more machine learning models, one or more modifications to at least one of the plurality of analyzed parameters based on the inputted configuration data and the inputted one or more detected anomalies;
to transmit the determination comprising the one or more modifications to a user over a communications network;
to receive feedback data about a viability of the one or more modifications;
to retrain the one or more machine learning models with the received feedback data;
to cause reconfiguration of one or more of the plurality of analyzed parameters from a first configuration corresponding to a source operating system to a second configuration corresponding to a target operating system, wherein the second configuration is based at least in part on differences in at least one of protocols and code between the source operating system and the target operating system; and
to automatically initiate one or more changes corresponding to reconfiguring at least one of the one or more elements and the one or more other elements based on the second configuration to proactively eliminate one or more runtime errors during the integration.