Container Orchestration for Cross-Platform, Data-Local Processing
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Solution Overview
Problem
Existing software applications face challenges in being deployed across different operating environments within an enterprise system, as they may not be compatible with varying operating systems and hardware architectures, leading to inefficiencies and limitations in deployment and maintenance.
Innovation Solution
A container orchestration framework that encapsulates software applications, including dependencies and runtime environments, allowing deployment on any machine without modification, and dynamically deploys containers based on data location to enhance performance by keeping data local to the application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software applications are developed for specific operating environments, then they can run reliably on those environments, but they cannot be deployed on machines with different operating systems or hardware architectures
Solution Approach 1:
The patent creates a universal runtime environment (container) that can execute applications across different operating systems and hardware architectures. The container includes all necessary dependencies, libraries, and configuration files, allowing the same application package to run reliably on Windows, Linux, macOS, and various hardware platforms without modification.
Solution Approach 2:
The container acts as an intermediary layer between the application and the host operating system. It provides a standardized interface and environment that mediates interactions between the application and underlying system differences, enabling cross-platform deployment while maintaining application stability.
2Adaptability or versatility
If applications include all dependencies and runtime environments, then they can run on any machine, but the container size increases
Solution Approach 1:
The patent implements a nested container structure where smaller, lightweight runtime components are embedded within the container. This allows the container to include only the essential dependencies and libraries needed for execution, reducing overall size while maintaining portability across different environments.
3Ease of operation
If data is stored centrally, then it is easy to manage, but transmission time increases and performance decreases
Solution Approach 1:
The patent segments data storage and processing across multiple distributed containers rather than centralizing it. Each container can store and process data locally, eliminating the need for frequent data transmission to a central location and reducing transmission time while maintaining ease of management through standardized interfaces.
Solution Approach 2:
The patent transitions from a single-dimensional centralized data model to a multi-dimensional distributed data architecture. Data is organized and stored across multiple spatial dimensions (different containers, machines, and locations), allowing parallel access and processing while maintaining logical consistency through the orchestration layer.
4Productivity
If applications are deployed on multiple machines, then processing capacity increases, but coordination and orchestration become more complex
Solution Approach 1:
The patent implements self-service mechanisms where containers automatically discover available data and resources, register themselves with the orchestration system, and manage their own deployment and execution. This reduces orchestration complexity by eliminating manual configuration and coordination while enabling scalable multi-machine processing.
Data Source
AI summary
Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.