Automated Configuration Recommendation Service for Cloud Applications
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Solution Overview
Problem
The increasing complexity of provisioning, administering, and managing resources in large-scale distributed systems, such as cloud-based provider networks, makes it challenging to optimize application configurations efficiently, as existing methods lack automation and often require manual intervention.
Innovation Solution
An automated configuration recommendation service that analyzes potential configurations using machine learning techniques, such as nearest neighbor, linear regression, and multi-arm bandit analysis, to select an optimized configuration based on cost, performance, and successful execution metrics, allowing for programmatic deployment of applications with recommended resource allocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual methods are used for provisioning and managing resources in distributed systems, then flexibility and control are maintained, but the complexity and time required for optimization increase significantly
Solution Approach 1:
The patent introduces a configuration recommendation service as an intermediary between the application deployment process and the provider network resources. This service automatically analyzes application requirements, evaluates multiple configuration options, and recommends optimized configurations, thereby automating the complex task of resource provisioning while managing complexity through a dedicated intermediary component.
Solution Approach 2:
The system enables self-service by allowing the configuration recommendation service to autonomously analyze application descriptions, evaluate potential configurations using scoring functions, and generate optimization recommendations without requiring manual intervention. The service independently performs resource analysis, configuration evaluation, and recommendation generation, reducing the need for human operators in the provisioning process.
2Manufacturing precision
If comprehensive analysis of multiple configuration options is performed, then optimization quality improves, but the time and computational resources required increase
Solution Approach 1:
The patent applies preliminary action by having the configuration recommendation service analyze and evaluate multiple potential configurations before actual application deployment. The service pre-computes scoring functions, evaluates resource allocations, and generates recommendations in advance, allowing developers to make informed decisions before committing resources, thus achieving precise optimization without delaying the actual deployment timeline.
Solution Approach 2:
The system implements partial action by evaluating a selected subset of the most promising configurations rather than exhaustively analyzing every possible configuration option. The scoring function prioritizes and ranks configurations, allowing the system to focus computational resources on evaluating the most relevant options, thereby achieving high optimization quality within reasonable time constraints.
3Loss of energy
If resource allocations are optimized for cost, then economic efficiency improves, but performance may be compromised
Solution Approach 1:
The patent utilizes parameter changes by incorporating multiple scoring criteria that balance cost and performance parameters. The scoring function evaluates configurations based on both economic factors (resource costs) and performance factors (execution reliability, speed, and quality). By adjusting and optimizing these parameters simultaneously, the system identifies configurations that achieve the best trade-off between cost efficiency and performance reliability.
Solution Approach 2:
The configuration recommendation service performs multiple functions simultaneously: it evaluates both cost metrics and performance metrics, analyzes different resource allocation scenarios, and generates comprehensive recommendations that consider both economic and technical requirements. This multi-functional approach ensures that the recommended configurations satisfy both cost optimization and performance reliability requirements.
Data Source
AI summary
Methods, systems, and computer-readable media for optimizing application configurations in a provider network are disclosed. An application description is determined that comprises one or more resource utilization characteristics of an application. Automated analysis is performed of a plurality of potential configurations for the application based at least in part on the application description. The automated analysis comprises scoring at least a portion of the potential configurations based at least in part on a scoring function. A recommended configuration for the application is determined based at least in part on the automated analysis. The recommended configuration comprises a type and number of computing resources in a multi-tenant provider network.