Predictive Model API Validation for Crash-Resistant Request Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing software solutions face challenges such as system crashes due to invalid inputs and resource monopolization, leading to inefficiencies and slow response times when processing electronic requests using predictive computer models.
Innovation Solution
A system architecture with separate API and model execution layers, where the API layer validates requests against model specification files and imputes valid values for invalid inputs, while leveraging an integrated development environment for efficient resource allocation and load balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the server executes multiple predictive computer models simultaneously in the same environment, then the system can satisfy more electronic requests, but the system may experience resource monopolization and system crashes
Solution Approach 1:
The system divides the server environment into separate execution environments, with each predictive computer model running in its own isolated environment. This segmentation prevents resource monopolization and system crashes by containing each model's resource usage independently, allowing multiple models to execute simultaneously without interfering with each other's stability.
2Quantity of substance
If the API layer loads models from a cross-platform database when not in memory, then the system can maintain a smaller memory footprint, but the request processing duration increases significantly
Solution Approach 1:
The system pre-loads predictive computer models into memory during system initialization or idle periods before they are actually needed for request processing. This preliminary action ensures that when requests arrive, the models are already available in memory, eliminating the time delay associated with loading from database and avoiding request timeouts.
3Device complexity
If the conventional software solution places the responsibility of sending valid requests on users, then the system architecture remains simple, but the system experiences segmentation faults and crashes due to invalid inputs
Solution Approach 1:
The system performs input validation in advance, before executing the predictive computer models. By checking and validating user inputs against expected formats and ranges prior to model execution, the system prevents segmentation faults and crashes caused by invalid inputs, while maintaining a relatively simple architecture through centralized validation logic in the API layer.
Data Source
AI summary
Disclosed herein are embodiments of systems, methods, and products comprises a server for efficiently processing electronic requests. The server receives a plurality of predictive computer models and generates a specification file for each model by parsing the source code of each model. When the server receives an electronic request, the API layer of the server validates the request by verifying the inputs of the request satisfying validation codes in the specification file of the corresponding model. If the electronic request is invalid, the server imputes valid values for the request and sends the imputed values to the model execution layer. Within the model execution layer, the server utilizes an integrated development environment of a third-party server to call the function of the corresponding model. The model execution layer transmits the function's output results back to the API layer, which transmits the output results to the user device.