International Business Machines Corporation

Madhusmita Patil

Harish Bharti

Siddhartha Sood

Shweta Vohra

Atul Misra

An approach is provided for upgrading containerized applications in cluster(s) in a container orchestration system. State information about the containerized applications is identified and analyzed. Based on the state information, an upgrade path for an upgrade of the containerized applications is determined. Using an artificial intelligence (AI) based container orchestration platform upgrade engine that employs continuous learning for upgrading data and algorithmic models to upgrade containers, a recommendation is generated that the upgrade be a full upgrade or a partial upgrade. A confirmation of the full upgrade or the partial upgrade is received from a user. Components for the full upgrade or the partial upgrade are created. Using the upgrade path and based on the full upgrade or the partial upgrade, the upgrade is performed.

Intelligent upgrade workflow for a container orchestration system

Managing data element caching is provided. Non-functional requirements of a system running an application are mapped to resource utilization and system performance metric values corresponding to each of a plurality of caching parameters for each of data elements corresponding to the application suitable for caching. A caching decision is generated for each of the data elements corresponding to the application suitable for caching by identifying certain ones of the data elements for the caching to improve at least one of performance and throughput of the system based on the mapping. A data element caching decision recommendation is generated for the application based on the caching decision. The data element caching decision recommendation corresponding to the application is output to a client device of a customer via a network.

Growing community of inventors

Hyderabad Telangana, India

Madhusmita Patil