Automated API Model Generation from Cloud Network Traffic
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Solution Overview
Problem
Conventional applications are limited in their ability to efficiently instantiate, update, and test a large number of APIs in distributed and heterogeneous cloud computing environments.
Innovation Solution
Automatically generate API models and objects based on observed network traffic, facilitating the automation of API generation, testing, and validation operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional applications are used to instantiate and update APIs in distributed cloud environments, then system stability is maintained, but productivity and ease of operation deteriorate due to manual management overhead
Solution Approach 1:
The system performs self-service by automatically generating API models and code from observed network traffic without requiring manual intervention. The API model generation component analyzes traffic patterns and autonomously creates API definitions, while the code generation component automatically produces implementation code, eliminating the need for developers to manually create and update APIs in distributed environments.
Solution Approach 2:
The patent replaces manual mechanical processes with automated systems. Instead of developers manually analyzing network traffic and writing API code, the system uses automated components to observe traffic, generate API models, and produce code. This substitution of manual mechanical work with automated intelligence significantly improves productivity while managing complexity through systematic approaches.
2Loss of time
If manual API management is used in large-scale distributed environments, then control precision is maintained, but loss of time and productivity increase
Solution Approach 1:
The system performs preliminary action by continuously observing and analyzing network traffic in advance to build accurate API models before actual API deployment is needed. The API model generation component proactively captures traffic patterns and generates API definitions ahead of time, so when deployment is required, the process is already complete or near-complete, significantly reducing deployment time without sacrificing accuracy.
3Ease of operation
If automated API generation is implemented, then productivity and ease of operation improve, but reliability may deteriorate due to automatic code generation
Solution Approach 1:
The system implements feedback mechanisms where the API model generation component continuously monitors network traffic and compares observed patterns against generated API models. This feedback loop allows the system to validate automatically generated code against actual usage patterns, ensuring reliability while maintaining ease of operation. The feedback ensures that automated generation produces trustworthy results.
Data Source
AI summary
Systems, methods, and devices facilitate generation of application programming interface (API) objects. Methods may discover, using one or more components of a cloud computing platform, ingress and egress API traffic associated with one or more hosted applications executing on the cloud computing platform. Methods may collecting API traffic data for a service used by the one or more hosted applications, where the API traffic data is associated with calls to the service made by a first client application executing on a device external to the cloud computing platform. Methods may form one or more API objects based on the API traffic data, the one or more API objects being formed based, at least in part, on one or more API specifications. Methods may provide, based on a request from a second client application, the one or more API objects.