Contextual API Gateway Routing Using Trained Models
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Solution Overview
Problem
Existing API gateways apply predefined rules without considering contextual data, leading to inefficient resource consumption and limited flexibility at backend systems.
Innovation Solution
Implementing a trained computerized model within the API gateway to determine routing actions based on contextual data such as originating user, network load, geographic location, and application version, among others.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If predefined rules are applied without contextual data, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent implements dynamic routing by transitioning from static predefined rules to a trained computerized model that adapts routing decisions based on real-time contextual data including user profiles, device information, network conditions, and content characteristics. This enables the system to optimize routing dynamically rather than following fixed predetermined paths.
2Adaptability or versatility
If contextual data is considered for routing decisions, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent introduces a trained computerized model as an intermediary component that processes contextual data and generates routing decisions. This model acts as a mediator between the complex contextual information and the routing logic, simplifying the overall system architecture while enabling sophisticated adaptability through the model's pattern recognition and decision-making capabilities.
3Ease of operation
If predefined rules are used, then ease of operation is improved, but productivity deteriorates
Solution Approach 1:
The patent implements self-service routing by enabling the trained computerized model to automatically analyze contextual data and make optimal routing decisions without requiring manual configuration or intervention. The system autonomously optimizes resource allocation and routing paths based on real-time conditions, improving productivity while maintaining ease of operation through automated decision-making.
4Loss of time
If static rules are applied, then loss of time in decision making is reduced, but adaptability deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the computerized model on historical data and patterns before deployment. This pre-training enables the model to quickly process contextual data and generate routing decisions in real-time without requiring complex runtime analysis, thus reducing decision time while maintaining high adaptability to varying conditions.
Data Source
AI summary
Various examples are directed to systems and methods for implementing an Application Programming Interface (API) gateway. The API gateway may receive an API call directed to an exposed API associated with a backend system. The API gateway may execute a trained computerized model based at least in part on the API call, and at least in part on API call contextual data associated with the API call. The API gateway may determine a routing action for the API call based at least in part on an output of the trained computerized model perform the routing action for the API call.