Server Self-Optimization via Performance Metric Normalization
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Solution Overview
Problem
Optimizing server environments for performance is a time-consuming task, as modifications can have unanticipated effects and require manual input, making it difficult to adapt to changing workloads and usage patterns.
Innovation Solution
A computer system performs self-optimization by collecting performance metrics, normalizing them for hardware resources and load levels, and automatically reconfiguring settings to achieve improved performance, using relative performance indices to identify the best settings combinations across different environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual modifications are made to server environment settings, then performance optimization can be achieved, but the process becomes time-consuming and requires continuous manual input
Solution Approach 1:
The system enables server environments to automatically optimize their own settings by collecting performance metrics, analyzing the data, and applying configuration changes without manual intervention. The server monitors its own performance characteristics and autonomously adjusts settings to maintain optimal operation, eliminating the need for continuous manual configuration management.
Solution Approach 2:
The system implements continuous monitoring of performance metrics and uses this feedback to automatically adjust server settings. By establishing a closed-loop control system where performance data feeds back into configuration decisions, the server can dynamically adapt to changing conditions and maintain optimal performance without manual input.
2Productivity
If server settings are modified to improve performance, then capabilities can be enhanced, but unanticipated effects may occur requiring careful manual management
Solution Approach 1:
The system collects and analyzes performance metrics over time to understand the relationships between different settings and their effects. By building this knowledge base in advance through continuous monitoring and analysis, the system can predict potential consequences of configuration changes before implementing them, preventing unanticipated negative effects.
Solution Approach 2:
The server autonomously monitors its own performance and automatically adjusts settings based on analyzed metrics, eliminating the need for manual configuration management. This self-service approach ensures that only data-driven, proven-effective changes are implemented, reducing the risk of unanticipated effects while maintaining system stability.
3Adaptability or versatility
If different settings combinations are tested across multiple environments, then optimal configurations can be identified, but the complexity of managing and analyzing data increases
Solution Approach 1:
The system implements a universal data collection and analysis framework that works across multiple server environments with different settings combinations. By creating a standardized methodology for metric collection, normalization, and analysis that can be applied universally across diverse environments, the system simplifies the management and comparison of configuration data while maintaining adaptability to various server types and workloads.
Solution Approach 2:
The system normalizes performance metrics to account for differences in hardware resources and load levels, transforming raw data into comparable parameters. By changing the representation of performance data through normalization and creating standardized performance indices, the system enables meaningful comparison across different environments without being overwhelmed by the complexity of raw heterogeneous data.
4Adaptability or versatility
If automatic reconfiguration is implemented to improve performance, then adaptability to changing workloads increases, but the need for manual configuration input decreases
Solution Approach 1:
The server environment automatically monitors its own performance metrics, analyzes the collected data, and reconfigures settings without any manual input. The system serves itself by autonomously identifying optimization opportunities and implementing configuration changes, completely eliminating the need for manual configuration effort while maintaining high adaptability to workload changes.
Solution Approach 2:
The system continuously collects and analyzes performance metrics in advance to identify optimization opportunities before they become critical. By performing preliminary analysis of performance data and pre-planning configuration adjustments, the system can proactively adapt to changing workloads automatically, reducing both manual effort and response time.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for self-optimization of computing environments. In some implementations, different combinations of settings used by one or more server environments are determined. Results achieved by the one or more server environments are monitored when using the different combinations of settings. Based on the monitored results, one or more performance measures are generated that correspond to each of the different combinations of settings. An updated set of settings are selected for a particular server environment based on the performance measures. The selected settings are provided for the particular server environment.