Edge Computing Network Software Architecture Generation via Design Studio
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Solution Overview
Problem
Generating Edge-to-Cloud (E2C) applications that split computational load across central data centers and remote edge compute nodes is complex, requiring specification of software components, configurations, and communication paths, and often necessitates operation without public cloud computation due to data privacy or connectivity issues.
Innovation Solution
A computer-implemented method and system using a design studio application to generate software architecture in edge computing networks by selecting components, categorizing them, positioning them on a canvas, creating communication routes, and processing to create installation plans and scripts for deployment, allowing for simulation or physical execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If computational logic is split across central data center and remote edge compute nodes, then latency is reduced and bandwidth usage is optimized, but system complexity increases
Solution Approach 1:
The system segments the E2C application into multiple independent features that can be selectively deployed to different compute nodes. Each feature is a self-contained functional unit that can run independently, allowing the computational logic to be divided across central data center and edge compute nodes without managing complex interdependencies
Solution Approach 2:
The system performs preliminary analysis of the E2C application to automatically identify suitable features for edge deployment and generate the necessary deployment artifacts before actual deployment. This includes analyzing application dependencies, identifying stateless features, and pre-configuring deployment packages, which simplifies the subsequent deployment process
2Ease of operation
If a user-friendly interface is provided for designing edge computing networks, then ease of operation improves, but system complexity increases
Solution Approach 1:
The system provides self-service capabilities through automated analysis and configuration. The backend automatically analyzes the E2C application, identifies suitable features for edge deployment, determines optimal compute node assignments, and generates deployment artifacts without requiring users to manually configure complex parameters. The user interface simply presents options and receives user preferences, while the system handles the complex configuration automatically
Data Source
AI summary
A computer-implemented method and system for generating software architecture in an edge computing network based on a design studio application, is disclosed. The computer-implemented method includes: receiving inputs from electronic devices associated with users to select components; categorizing components to break down the components into categories for search options; positioning the selected components on canvas using drag and drop capability of the design studio application; analyzing properties associated with the components to configure the edge computing network based on properties panels in GUI for each component; creating the routes by creating a line between icons corresponding to the components, on the canvas, to establish connections between components; generating the software architecture upon creation of routes between the components; processing the generated software architecture to generate installation plans and automatically create corresponding scripts, for installing the software architecture; and providing an output of the generated software architecture.