Cloud Tuning Service for Post-Deployment Configuration Optimization
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Solution Overview
Problem
Existing solutions are inefficient and costly in automatically tuning hundreds to thousands of inter-dependent configuration parameters for applications post-deployment, risking performance disruptions due to manual exploration and dependency on machine learning algorithms that struggle with large parameter sets.
Innovation Solution
A cloud-hosted tuning service using reinforcement learning and online decision tree algorithms to automatically determine recommended configuration values for both numerical and categorical parameters, minimizing disruptions and optimizing performance by filtering impactful parameters and iteratively adjusting settings based on feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual exploration and testing of configuration parameters is performed post-deployment, then configuration tuning can be done, but application performance may be reduced or functions disrupted
Solution Approach 1:
The system enables self-service configuration tuning by automatically determining recommended configuration values using machine learning algorithms. The tuning service autonomously analyzes application context, traverses decision trees, and provides optimized configuration values without requiring manual intervention that could disrupt application performance
Solution Approach 2:
The system implements feedback mechanisms where configuration recommendations are based on application context and performance data. The decision tree traversal uses feedback from application metrics to dynamically determine optimal configuration values, ensuring that tuning actions are informed by actual performance characteristics rather than manual guesswork
2Extent of automation
If machine learning algorithms are used to automatically tune configuration parameters, then automation is improved, but the system becomes complex and costly when handling large parameter sets
Solution Approach 1:
The system segments the configuration tuning process by organizing parameters into hierarchical decision trees with multiple levels. Each level of the decision tree represents a dimension of configuration parameters, allowing the system to handle large parameter sets through structured segmentation rather than monolithic complexity
Solution Approach 2:
The system manages parameter complexity by dynamically changing the representation and organization of configuration parameters. Through decision tree traversal, parameters are transformed from flat lists to hierarchical structures that can be navigated based on application context, reducing the effective complexity of handling large parameter sets
3Adaptability or versatility
If configuration parameters are tuned after deployment, then adaptability to workload changes is improved, but the risk of performance disruption increases
Solution Approach 1:
The system performs preliminary analysis of application context and decision tree traversal before providing configuration recommendations. This preliminary action ensures that tuning recommendations are pre-validated against known performance characteristics, reducing the risk of disruption when configuration changes are applied
Solution Approach 2:
The decision tree serves as an intermediary between the raw configuration parameters and the application context. It mediates the relationship by translating contextual information into structured configuration recommendations, acting as a buffer that prevents direct harmful interactions between tuning operations and application performance
Data Source
AI summary
The present disclosure relates to methods and systems for post deployment configuration tuning of applications. The methods and systems automatically determine which configuration parameters of the applications to tune. The methods and systems provide recommended configuration values for the configuration parameters. The methods and systems manage the scope at which to tune to the configuration parameters for the application.