Apparatus and method for dynamic containerization deployment based on runtime characteristic awareness

CN120743441BActive Publication Date: 2026-01-16INSPUR GENERSOFT CO LTD
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Patent Information

Application Number
CN202511269385.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-09-08
Publication Date
2026-01-16
Estimated Expiration
2045-09-08

AI Technical Summary

Technical Problem

Existing containerization deployment technologies lack real-time awareness and dynamic adjustment of container runtime characteristics, resulting in insufficient or wasted resource allocation, slow fault recovery, underutilization of heterogeneous hardware performance, and low image deployment efficiency.

Method used

A dynamic containerized deployment device based on runtime feature awareness is constructed. The runtime feature awareness module collects multi-dimensional dynamic feature data, the intelligent decision-making module performs resource prediction and fault self-healing decisions, the dynamic deployment execution module realizes dynamic adjustment of containers and hardware adaptation, and the heterogeneous hardware adaptation module provides data interaction interface.

Benefits of technology

It achieves deep awareness and intelligent response to container runtime characteristics, improves resource utilization, fault recovery speed and heterogeneous hardware performance, significantly reduces resource allocation errors, and enables millisecond-level fault self-healing and efficient image deployment.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application belongs to the technical field of cloud computing and container deployment, and provides a dynamic container deployment device and method based on runtime feature perception, which acquires multi-dimensional dynamic feature data of a container and its running environment, receives the multi-dimensional dynamic feature data of the container and its running environment, performs resource prediction based on the multi-dimensional dynamic feature data to obtain a dynamic resource demand prediction vector, judges whether a fault occurs based on a constructed fault judgment function, generates a fault self-recovery strategy when the fault occurs, generates an environment deployment optimization decision scheme in combination with the dynamic resource demand prediction vector and the constructed constraint optimization model, performs dynamic deployment of the container based on the dynamic resource demand prediction vector, the fault self-recovery strategy and the environment deployment optimization decision scheme, constructs an adaptation protocol of the container orchestration and the heterogeneous hardware facility, and performs data interaction based on the adaptation protocol. The application effectively improves resource utilization, fault recovery speed and heterogeneous hardware performance.
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