Software-Defined System Performance Optimization via Client Configuration Comparison
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Solution Overview
Problem
Software-defined systems face sub-optimal performance due to varying configurations and workloads across client devices, which are not effectively addressed by existing technologies.
Innovation Solution
A system that monitors client system configurations and performance, using a server to compare properties with similar client systems and determine adjustments, such as changes in hardware settings or workloads, using machine-learning models or predefined rules to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If client systems have varying configurations and workloads to meet diverse client needs, then adaptability is improved, but system performance deteriorates due to sub-optimal configurations
Solution Approach 1:
The system dynamically changes configuration parameters of client systems by analyzing performance data and comparing with similar systems. The server automatically adjusts hardware settings, software configurations, and resource allocation parameters to optimize performance while maintaining adaptability to different client needs.
Solution Approach 2:
The system implements continuous feedback loops where performance metrics from client systems are collected, analyzed, and used to generate configuration adjustments. The server monitors performance data, compares it across similar systems, and feeds back optimized configuration recommendations to improve overall system performance while preserving adaptability.
2Productivity
If manual configuration optimization is performed for each client system, then performance is improved, but device complexity and operational effort increase
Solution Approach 1:
The system enables self-service optimization where client systems automatically receive configuration adjustments without manual intervention. The server autonomously analyzes performance data, identifies optimization opportunities, and implements configuration changes based on comparisons with similar high-performing systems, eliminating the need for manual optimization efforts.
Solution Approach 2:
The system creates a universal optimization platform that serves multiple client systems with varying configurations through a single automated mechanism. The server applies general optimization principles across diverse systems by comparing similar systems and transferring successful configurations, reducing complexity while maintaining performance improvements across the entire fleet.
3Productivity
If configuration adjustments are made based on individual system analysis, then performance is improved, but loss of time increases due to sequential optimization
Solution Approach 1:
The system merges optimization efforts across multiple client systems by collecting and analyzing performance data from the entire fleet simultaneously. The server compares configurations across similar systems and identifies optimization opportunities in parallel, allowing configuration adjustments to be made based on aggregated insights rather than sequential individual analysis, significantly reducing total optimization time.
Data Source
AI summary
Performance of devices can be evaluated and enhanced in software-defined systems. For example, a computing device can receive, at a server of a software-defined system, a first plurality of properties describing a client system in the software-defined system. The computing device can compare, by the server, the first plurality of properties to additional properties describing at least one additional client system in the software-defined system. The computing device can determine, by the server, an adjustment for the client system based on the comparison and a similarity of the client system to each of the at least one additional client system. The computing device can output, by the server, an indication of the adjustment to the client system.