Neural Optimization of Grayscale Release Strategies Under Constraints
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Solution Overview
Problem
Existing grayscale release strategies for enterprise-level applications are cumbersome and time-consuming, requiring manual intervention due to complex microservice dependencies, making it difficult to dynamically adapt traffic grouping and resource allocation.
Innovation Solution
A method and system that employs neural networks to define state and action spaces, generate composite reward functions, and optimize grayscale release strategies based on multi-objective optimization, allowing for intelligent and automated traffic control without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual intervention is used to control grayscale release strategies, then flexibility and adaptability are maintained, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system enables self-service by allowing the grayscale release strategy to automatically adjust traffic ratios and deployment parameters based on real-time system state and predefined objectives, eliminating the need for continuous manual intervention while maintaining adaptive control capabilities
Solution Approach 2:
The patent replaces manual mechanical control with an automated neural network-based decision system that processes system state vectors and generates optimal release strategies through computational models, substituting human operational effort with intelligent automation
2Adaptability or versatility
If manual intervention is required for traffic grouping and resource allocation, then complex microservice dependencies can be managed, but the process becomes time-consuming and less dynamic
Solution Approach 1:
The system implements continuous feedback mechanisms where the neural network monitors system state vectors in real-time and automatically adjusts traffic grouping and resource allocation based on observed performance metrics, enabling dynamic adaptation without time-consuming manual review
Solution Approach 2:
The patent introduces dynamic adaptability by allowing the release strategy to automatically adjust parameters such as traffic ratios and deployment pacing based on real-time system conditions, transforming static manual plans into living, self-adjusting systems that respond instantly to changing microservice dependencies
3Productivity
If automated neural network optimization is applied, then efficiency and stability are improved, but system complexity increases
Solution Approach 1:
The patent manages complexity by parameterizing the system state as vectors and defining the action space through discrete traffic ratio adjustments, transforming complex microservice dependency management into a structured optimization problem with manageable parameters and clear state representations
Data Source
AI summary
An embodiment for dynamically generating grayscale release strategies based on multi-objective optimization. The embodiment may define current state vector spaces and an action space for a target system. The embodiment may generate a composite reward function based on one or more objective-based reward functions and one or more constraint-based reward functions. The embodiment may generate, using a first network, candidate action vectors based on the defined current state vector spaces and the defined action space, the candidate action vectors corresponding to action probabilities. The embodiment may calculate, using a second network, state value functions based on the candidate action vectors. The embodiment may execute, in training iterations, actions corresponding to the candidate action vectors to obtain environment feedback including observed rewards. The embodiment may optimize the first and second networks. The embodiment may generate gray release strategies including a series of optimized actions to be taken.