FaaS Feature Library Using DAG for Dynamic Output Generation
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Solution Overview
Problem
Existing FaaS platforms lack a unified location for feature logic codes and generate only standard outputs, failing to accommodate dynamic business requirements and feature modifications efficiently.
Innovation Solution
A FaaS-based feature library using a Directed Acyclic Graph (DAG) is provided, allowing dynamic selection and generation of outputs by storing logic codes for multiple features, enabling dynamic input and output generation based on user configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If FaaS functions implement only standard input and generate only standard output data, then the system maintains simplicity and standardization, but it fails to accommodate dynamic business requirements and feature modifications
Solution Approach 1:
The patent creates a unified feature library that serves multiple functions: storing feature logic codes, managing feature versions, supporting dynamic feature selection, and enabling reuse across different FaaS functions. This universal library resolves the contradiction by providing adaptability through a single standardized interface that handles diverse business requirements without increasing overall system complexity.
Solution Approach 2:
The feature library acts as an intermediary layer between standard FaaS functions and dynamic business requirements. It receives feature selection configurations, retrieves appropriate logic codes, and returns transformed outputs, thereby enabling standard functions to accommodate custom business logic without modifying the functions themselves, thus maintaining simplicity while gaining versatility.
2Productivity
If feature logic codes are stored in a unified location, then code reuse and maintenance efficiency improve, but the system requires additional infrastructure and management overhead
Solution Approach 1:
The patent merges the storage of feature logic codes, feature metadata, and version management into a single unified feature library infrastructure. This consolidation improves code reuse efficiency by providing centralized access to all feature logic while managing the infrastructure complexity through a unified management interface that handles versioning, retrieval, and deployment operations.
Solution Approach 2:
The feature library implements self-service capabilities through automated version management, dynamic retrieval based on feature selection configurations, and automatic deployment to the FaaS platform. This automation reduces the manual management overhead despite the added infrastructure, allowing the system to maintain high productivity through efficient code reuse.
3Ease of manufacture
If multiple functions are called from one to another in FaaS platform, then existing functions can be reused without rewriting code, but the functions are scattered and not available at one location
Solution Approach 1:
The feature library serves as an intermediary registry that catalogs all feature logic codes with their metadata and relationships. When functions need to call existing functionality, they can reference the feature library to locate and retrieve the required feature logic, eliminating the need to track scattered function locations manually while maintaining ease of reuse through standardized access patterns.
Data Source
AI summary
A method and a system for generating required feature output by providing a FaaS (Function-as-a-Service) based feature library using a DAG (Directed Acyclic Graph), are disclosed. A library is configured to store logic codes being generated for a plurality of features and the library is then deployed on a FaaS engine. At least one feature selection configuration from a user is received by the FaaS engine. The DAG implements at least one transformation based on the at least one feature selection configuration, and the FaaS engine thereby generates the required feature output for the user.