Optimizing software upgrade processes in data center architectures

A computer-implemented method, comprising:

• obtaining version information for a plurality of software and firmware sub-components of a service pack installed on a computer system;
• inputting the obtained version information into a trained neural network model;
• receiving, from the trained neural network model, a prediction of a service pack version corresponding to the plurality of software and firmware sub-components;
• determining, based on the predicted service pack version, a compatible target service pack for the computer system;
• and
• initiating an upgrade process to install the compatible target service pack on the computer system.

A computer-implemented method, comprising:

• fetching a list of discrete sub-component versions from a server node using an Application Programming Interface (API), the server node being part of a disaggregated hyper-converged infrastructure (dHCI) system;
• inputting the fetched sub-component versions into a trained three-channel convolutional neural network (CNN) model;
• predicting a base service pack version installed on the server node using the trained three-channel CNN model;
• determining a target service pack version and a supported upgrade path to reach the target service pack version based on the predicted base service pack version;
• and
• updating the server node by installing the target service pack version on the server node.