ML API Request Screening for Crash and Freeze Prevention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing API management systems fail to adequately identify and prevent potentially problematic API requests that pass governance policies but may cause system freezes or crashes.
Innovation Solution
Implementing machine learning-based models, such as neural networks, to analyze API requests and system properties, predicting the likelihood of successful execution and providing scores or binary classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing API governance policies are used to manage API requests, then basic request filtering is achieved, but the system cannot sufficiently prevent problematic requests that pass the governance policy (e.g., requests that may cause freezing or crashing)
Solution Approach 1:
A machine learning-based predictive model is introduced as an intermediary component between the API governance policy and the actual API request processing. This model analyzes API requests and predicts their likelihood to cause system failures, providing an additional layer of protection without replacing the existing governance framework. The model processes requests in parallel and outputs risk assessments that can be used to block problematic requests before they reach the API gateway.
Solution Approach 2:
The patent replaces rigid rule-based API governance mechanisms with a flexible machine learning predictive model. Instead of relying solely on predefined governance policies that may not catch edge cases, the system uses ML to dynamically assess request risk based on learned patterns from historical data. This substitution enables the system to adapt to new failure modes without requiring updates to governance policies.
2Measurement precision
If machine learning models are introduced to predict API request failures, then detection accuracy of problematic requests is improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments the API request analysis into distinct components: the existing API governance policy handling for basic filtering, the machine learning predictive model for risk assessment, and the API gateway for final decision-making. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The machine learning model performs preliminary analysis of API requests before they reach the API gateway, predicting potential failures in advance. By conducting this risk assessment beforehand, the system can block problematic requests early in the processing chain, preventing system failures before they occur and reducing the computational burden on downstream components.
3Measurement precision
If machine learning models analyze API requests in detail, then prediction accuracy improves, but processing time and response speed decrease
Solution Approach 1:
The machine learning model performs risk assessment in parallel with other API processing operations, conducting analysis beforehand so that when the final decision is needed, the prediction is already available. This preliminary action eliminates sequential bottlenecks and allows the API gateway to make rapid decisions based on pre-computed risk scores without delaying request processing.
Solution Approach 2:
The system applies machine learning analysis selectively rather than universally - for example, focusing computational resources on requests that show signs of potential failure patterns or for high-value API endpoints. This partial application of ML analysis maintains high accuracy for critical requests while reducing overall processing time by avoiding exhaustive analysis of all requests.
Data Source
AI summary
Described herein are machine learning methods and systems for application programming Interface (API) management. One or more machine learning-based models, such as a neural network, may be trained to provide a prediction(s) that an API request is likely to succeed (e.g., not cause the API to crash) or to fail (e.g., cause the API to crash, freeze, etc.). The one or more machine learning-based models may be trained using historical API requests that were successful and historical API requests that are not successful. Various aspects of the API requests, as well as system properties associated with the machine(s) that executes/processes the historical API requests, may be used as well when training the one or more machine learning-based models.