US 12,461,765 B2
Container name identification processing
Kuo-Liang Chou, New Taipei (TW); Zhan Peng Huo, Beijing (CN); Jun Zhu, Shanghai (CN); Yu Zui You, Beijing (CN); Xuan Feng, Beijing (CN); and Jun Hao, Beijing (CN)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Dec. 9, 2022, as Appl. No. 18/063,736.
Prior Publication US 2024/0192950 A1, Jun. 13, 2024
Int. Cl. G06F 9/455 (2018.01); G06F 8/36 (2018.01); G06F 8/71 (2018.01)
CPC G06F 9/455 (2013.01) [G06F 8/36 (2013.01); G06F 8/71 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A container-based computing environment method comprising:
training a machine learning component recognition model to identify software components extracted from layer files of container images;
generating, within the container-based computing environment, an executable container from a container image, the executable container implementing a computing service, and the generating comprising:
extracting multiple layer files from the container image via intelligent image scan processing of layer files of the container image;
determining, via natural language processing and the of a trained machine learning component recognition model, frequency of occurrence of software components in the multiple layer files of the container image;
using the determined frequency of occurrence of software components in the multiple layer files of the container image in generating a digital identifier for the executable container that reflects, at least in part, the software components in the multiple layer files of the container image and reflects, at least in part, a component ordering based on a weight score sequence of the software components;
generating, from the container image, the executable container and linking within the container-based computing environment the generated digital identifier to the executable container; and
deploying the executable container within the container-based computing environment with the generated digital identifier, wherein problem analysis of the executable container is facilitated within the container-based computing environment via the linked digital identifier being based, at least in part, on the frequency of software components in the multiple layer files of the container image from which the executable container is generated.