Adaptive Configuration Recommendation Engine
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Solution Overview
Problem
Existing network management systems are reactive and require expertise to evaluate configuration changes, limiting their ability to proactively improve system performance and failing to causally link performance changes with configuration modifications, thus not allowing for efficient knowledge sharing across customer sites.
Innovation Solution
A method and system that identifies and assesses the effectiveness of system configuration changes by collecting and analyzing performance metrics before and after implementation, weighting the relative value of these changes, and automatically adjusting recommendations based on observed performance data, using a configuration tracker and performance monitor to prioritize effective configuration adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If threshold-based performance monitoring is used, then system administrators are notified when performance crosses defined thresholds, but the system is reactive and does not provide proactive performance optimization
Solution Approach 1:
The system performs preliminary actions by proactively identifying and recommending configuration changes before performance degradation occurs. The advice generation engine analyzes configuration data and performance metrics to suggest optimizations in advance, rather than waiting for threshold breaches.
Solution Approach 2:
The system implements feedback by tracking the implementation status of advice recommendations and measuring their impact on performance metrics. This feedback loop allows the system to learn from actual outcomes and continuously improve its recommendation quality, transitioning from reactive to proactive optimization.
2Measurement precision
If experts are assigned to evaluate configuration changes, then performance problems can be diagnosed, but the limited number of experts restricts the number of customers that can be helped
Solution Approach 1:
The system enables self-service by providing automated advice generation that does not require expert intervention. The advice generation engine autonomously analyzes configuration data and performance metrics to generate recommendations, allowing any customer to benefit from expert-level analysis without consuming expert resources.
Solution Approach 2:
The system creates copies of expert knowledge by encoding diagnostic and optimization expertise into the advice generation engine. This digital representation of expert knowledge can be replicated and distributed to serve unlimited customers simultaneously, eliminating the bottleneck of human expert availability.
3Ease of operation
If static sets of recommendations are provided, then corrective actions can be suggested, but the recommendations cannot be dynamically adjusted based on actual performance outcomes
Solution Approach 1:
The system introduces dynamics by making recommendations adaptive rather than static. The advice generation engine continuously updates its recommendations based on real performance metrics and the measured effectiveness of previous advice, allowing the system to evolve its guidance based on actual outcomes and changing system conditions.
Solution Approach 2:
The system applies parameter changes by adjusting recommendation parameters such as effectiveness weights and priority scores based on observed performance data. The system modifies these parameters dynamically as it learns from the impact of implemented recommendations, optimizing its advice over time.
4Reliability
If performance monitoring is localized to each customer site, then site-specific performance can be monitored, but knowledge cannot be shared across different customer sites
Solution Approach 1:
The system achieves universality by designing the advice generation engine to handle multiple customer sites with a single unified system. The engine can process configuration data and performance metrics from any site and generate site-specific recommendations, while also learning from aggregated data across all sites to improve overall recommendation quality.
Solution Approach 2:
The system merges localized monitoring capabilities with centralized knowledge sharing by combining site-specific performance data with aggregated insights from multiple customers. This integration allows the system to maintain site-specific optimization while leveraging collective knowledge across the entire customer base.
Data Source
AI summary
A method and system are provided for proposing advice consisting of corrective actions and enhancements to address a detected problem or measured degradation in the operation of a computer based on collected configuration and performance data. After the advice is proposed, the method and/or system automatically detects when and in what form the advice was implemented, and rates the efficacy of the implementation action based on subsequent collection and measurement of performance. The method and/or system is then able to adjust the importance of the advice relative to other advice.