Dynamic Container Orchestration for Real-Time Workload Migration
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Solution Overview
Problem
Enterprise organizations face underutilization of computing devices and overutilization of expensive servers due to inefficient use of resources, particularly during non-computationally intensive tasks, leading to higher operational costs.
Innovation Solution
A real-time dynamic container optimization computing platform that includes a real-time container management platform, orchestration platform, and machine-learning analysis platform to dynamically generate, deploy, and manage containers based on real-time monitoring and performance data, optimizing resource utilization across enterprise-associated computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If containers are deployed on enterprise-associated computing devices, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces a container orchestration platform as an intermediary layer between the computing devices and the containers. This platform manages container deployment, execution, and monitoring automatically, reducing the complexity burden on individual devices while improving overall resource utilization efficiency through centralized coordination
Solution Approach 2:
The system implements self-service mechanisms where the container orchestration platform automatically selects optimal computing devices for container deployment based on real-time resource availability, and automatically monitors and migrates containers when performance thresholds are breached, eliminating manual intervention and reducing operational complexity
2Productivity
If real-time monitoring and dynamic migration of containers is implemented, then resource allocation efficiency is improved, but measurement and detection difficulty increases
Solution Approach 1:
The patent implements a feedback mechanism where the container orchestration platform continuously monitors container performance metrics (CPU usage, memory consumption, network I/O) and automatically triggers container migration when predefined thresholds are breached. This closed-loop feedback system improves resource allocation efficiency while managing monitoring complexity through automated decision-making rules
Solution Approach 2:
The system performs preliminary actions by pre-defining performance thresholds and migration rules before container deployment. When monitoring detects that a container approaches these predefined thresholds, the system automatically initiates migration procedures, eliminating the need for complex real-time analysis and simplifying the monitoring process through rule-based automation
Data Source
AI summary
Aspects of the disclosure relate to a real-time dynamic container optimization computing platform. The real-time dynamic container optimization computing platform may receive a request to create a first processing block and first data associated with the first processing block. The real-time dynamic container optimization computing platform may utilize a plurality of models to select a first computing device for the first processing block. The real-time dynamic container optimization computing platform may generate and deploy a container to the first computing device. The real-time dynamic container optimization computing platform may monitor execution of the container on the first computing device. The real-time dynamic container optimization computing platform may migrate the container to the second computing device if an issue with execution of the container on the first computing device is detected.