Automated Service Configuration Tuning via Load Testing
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Solution Overview
Problem
Manual tuning of service configurations in computer systems is time-consuming and inefficient, often leading to outdated settings that negatively impact performance, especially in heterogeneous multi-host web services, resulting in suboptimal resource usage and performance issues.
Innovation Solution
An automated system for service configuration tuning that includes a load generator module, load testing module, and performance monitoring module, which determines an optimal configuration on test computers and rolls back changes if they adversely affect production systems, ensuring user-specified performance goals are met without requiring manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of service configuration is performed, then performance can be improved through expert judgment, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs self-tuning by automatically generating tuning jobs, executing load tests, analyzing performance data, and applying configuration changes without manual intervention. The service monitors its own performance metrics and autonomously adjusts configuration parameters to optimize throughput, latency, and stability.
Solution Approach 2:
The patent replaces the manual mechanical process of tuning with an automated computational system. Instead of human operators manually adjusting parameters based on experience, the system uses algorithms to generate tuning jobs, execute tests, analyze results, and apply changes automatically.
2Productivity
If frequent manual tuning is performed to maintain optimal performance, then service performance can be maintained, but the complexity and time investment increase significantly
Solution Approach 1:
The system continuously monitors service performance metrics such as throughput, latency, and stability, compares actual performance against targets, and automatically generates tuning jobs when performance degradation is detected. This closed-loop feedback mechanism maintains optimal performance without requiring complex manual intervention.
Solution Approach 2:
The system performs preliminary testing and validation in a test environment before applying configuration changes to production. Tuning jobs are executed on test hosts first, and only after verifying performance improvement does the system deploy changes to production hosts, preventing adverse effects.
3Speed
If configuration changes are applied without thorough testing, then deployment speed increases, but the risk of adverse performance impacts increases
Solution Approach 1:
The system executes load tests and performance validation on test hosts before deploying configuration changes to production. This preliminary testing phase verifies that configuration changes improve performance without causing adverse effects, ensuring reliable deployment.
Solution Approach 2:
The system prepares contingency measures by maintaining the ability to roll back configuration changes if performance degradation is detected. The automated rollback mechanism acts as a cushion against potential adverse effects, protecting production system stability.
4Reliability
If manual performance analysis is performed regularly, then outdated settings can be identified and corrected, but the time and resource requirements become prohibitive
Solution Approach 1:
The system continuously collects and analyzes performance metrics from production hosts, automatically comparing actual performance against target metrics. When performance degradation is detected, the system autonomously generates tuning jobs without requiring manual analysis, maintaining continuous optimization.
Solution Approach 2:
The patent replaces manual performance analysis with automated monitoring and analysis systems that continuously track service metrics, identify performance issues, and generate appropriate tuning actions without human intervention.
Data Source
AI summary
Methods and systems for automated tuning of a service configuration are disclosed. An optimal configuration for a test computer is selected by performing one or more load tests using the test computer for each of a plurality of test configurations. The performance of a plurality of additional test computers configured with the optimal configuration is automatically determined by performing additional load tests using the additional test computers. A plurality of production computers are automatically configured with the optimal configuration if the performance of the additional test computers is improved with the optimal configuration.