Computer System Performance Management with Control Variables
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Solution Overview
Problem
Administrators of computer systems face difficulties in selecting optimal control variable settings to manage performance effectively, especially in high-dimensional systems, as they struggle to determine if chosen settings provide superior performance compared to other options, often relying on time-consuming trial-and-error methods.
Innovation Solution
A system and method that record performance metrics, modify control variable values, and determine relationships between these variables and a desirability metric, using experimental plans and visualizations to guide administrators in selecting optimal control settings for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If administrators manually select control variable values through trial-and-error methods, then they can make decisions about system configuration, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary experimentation and analysis to determine relationships between control variables and performance metrics before the administrator needs to make decisions. By pre-computing the impact of different control variable settings on performance, the system eliminates the need for time-consuming trial-and-error methods when the administrator actually needs to configure the system.
Solution Approach 2:
The system introduces an intermediary computational layer that analyzes the relationships between control variables and performance metrics. This intermediary process generates recommendations and predictions about optimal control variable settings, mediating between the administrator's needs and the complex system behavior, thereby reducing the time and effort required for manual experimentation.
2Adaptability or versatility
If the system uses many control variables to manage complex performance scenarios, then system management capability improves, but the problem space dimensionality increases making it difficult to determine optimal settings
Solution Approach 1:
The system segments the complex high-dimensional problem space into smaller, more manageable components by analyzing relationships between individual control variables and performance metrics separately. This segmentation allows the system to handle complexity systematically by breaking down the overall optimization problem into smaller sub-problems that can be analyzed and solved more easily.
Solution Approach 2:
The system changes the parameter representation by transforming the high-dimensional control variable space into a more manageable form through statistical analysis and relationship modeling. By analyzing how changes in individual control variables affect performance metrics, the system reduces the effective dimensionality of the problem while maintaining the ability to manage complex scenarios.
3Reliability
If administrators experiment with different control variable values to find optimal settings, then performance improvement may be achieved, but confidence in optimality remains low due to the large search space
Solution Approach 1:
The system implements feedback by continuously analyzing the relationships between control variable settings and performance metrics. By providing feedback about which control variable configurations lead to better performance and why, the system builds confidence in the optimality of recommendations. The feedback mechanism allows administrators to understand the rationale behind suggested settings rather than simply accepting trial-and-error results.
Solution Approach 2:
The system performs preliminary comprehensive analysis of the control variable space to establish relationships and patterns before making recommendations. This preliminary action ensures that when the system recommends optimal settings, it does so with high confidence based on pre-established knowledge about how control variables affect performance, rather than relying on incomplete trial-and-error exploration.
Data Source
AI summary
Embodiments of techniques and systems for managing performance of a computing system are described. In embodiments, parameters may be received describing control variables for a computing system, workloads that are performed on the computing system, and performance metrics for the computing system. An experimental plan may be generated for modifying the control variables during execution of the computing system and observing performance metrics during this execution. The recorded observations may then be used in to determine one or more relationships between the control variables and the performance metrics. Relationships between the control variables and a desirability function associated with performance of the computer system may also be determined. These relationships may be displayed in visualizations to a user, and may be used to by the user to select values for the computing system which increase the desirability function. Other embodiments may be described and claimed.