Edge Computing Local Containers for CI Bottlenecks
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Solution Overview
Problem
Centralized continuous integration systems in software development lead to slow response times and bottlenecks, increasing end-to-end software development times due to dependency on a single point of failure.
Innovation Solution
Implementing edge computing with a local container-based technology that instantiates a continuous integration system on a developer's computer, allowing for local compilation, testing, and deployment of applications, reducing reliance on centralized infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a centralized continuous integration system is used, then resource sharing and centralized management are improved, but response time and development efficiency deteriorate
Solution Approach 1:
The centralized continuous integration system is segmented into distributed edge computing nodes that can operate independently. Each developer's device becomes a self-contained build and test environment, eliminating the single point of failure and reducing response time while maintaining centralized management capabilities through coordination protocols.
Solution Approach 2:
The system transitions from a single-dimensional centralized architecture to a multi-dimensional distributed architecture. By adding the dimension of local execution on developer devices, the system achieves both centralized management (through coordination) and fast local response times simultaneously.
2Ease of operation
If a centralized build environment is used, then standardization is improved, but system reliability deteriorates due to single point of failure
Solution Approach 1:
The centralized build environment is segmented into multiple independent edge nodes distributed across developer devices. Each node maintains standardized build capabilities locally, eliminating the single point of failure while preserving standardization through consistent toolchain images and configurations deployed to each node.
Solution Approach 2:
The system changes the operational parameter from centralized execution to distributed execution. By deploying standardized container images to multiple edge nodes, the system maintains standardization while improving reliability through distribution and redundancy.
3Productivity
If code is compiled and tested centrally, then resource utilization is improved, but build and test time increase
Solution Approach 1:
Instead of the conventional approach where code is sent to a centralized server for building and testing, the system inverts the flow by bringing the build and test environments to the developer's local device. This inversion enables parallel execution across multiple devices, dramatically reducing build and test time while maintaining efficient resource utilization.
Solution Approach 2:
The system performs build and test actions locally on each developer's device rather than waiting for centralized processing. This partial execution model allows developers to immediately test code changes without waiting for centralized queue processing, reducing overall build and test time.
Data Source
AI summary
Systems, methods, and computer program products are described for edge computing for cloud application development. Data having at least one image of a continuous integration system is received. The at least one image can be locally instantiated within a local container. Developmental code associated with an application can be retrieved from a code repository. The application is compiled, built, and tested within the local container based on the developmental code. The application is deployed to a production environment.