Process Optimization System for Server Configuration Accuracy
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Solution Overview
Problem
Current server monitoring systems face inefficiencies in configuring optimal server parameters due to the complexity of high-numbered impacting factors and unstructured data, leading to inaccurate and non-scalable process optimization.
Innovation Solution
A process optimization system utilizing artificial intelligence and cognitive learning operations to identify key factors, create data domains, and generate optimized results by deconstructing factor ranges and classifying data partitions, thereby neutralizing overlapping and stochastic effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional server monitoring approaches (base-line dependent comparative methods, simulation-based approaches, rule-based techniques) are used to configure server parameters, then manual configuration processes can be performed, but the accuracy and effectiveness of optimization deteriorates due to inability to account for ambiguity and data replication caused by overlapping factors
Solution Approach 1:
The patent segments the complex optimization problem into distinct components: data collection module, data analysis module, and optimization module. Each module handles specific aspects of the optimization process, allowing the system to manage high-dimensional parameter spaces and overlapping factors systematically rather than as a monolithic complex problem
Solution Approach 2:
The patent introduces cognitive computing models and machine learning algorithms as intermediary layers between raw server data and optimization decisions. These intermediaries process and interpret overlapping factors, stochastic data, and ambiguous information to generate accurate configuration recommendations without requiring manual analysis of complex interactions
2Productivity
If manual process optimization configuration is performed, then flexibility in parameter adjustment is maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and pre-processing server monitoring data, pre-training cognitive models on historical configuration data, and pre-establishing relationships between parameters and performance metrics. This preliminary preparation enables rapid optimization assessments when configuration changes are needed, eliminating the need for time-consuming manual break-even analyses
Solution Approach 2:
The system implements self-service optimization where the cognitive computing models automatically analyze server data, identify optimal configurations, and generate recommendations without human intervention. The system serves itself by continuously learning from operational data and autonomously determining parameter settings, dramatically reducing both time consumption and labor effort compared to manual optimization processes
3Adaptability or versatility
If existing optimization systems are deployed, then basic parameter monitoring is achieved, but scalability to handle high-numbered impacting factors and unstructured data deteriorates
Solution Approach 1:
The patent implements dynamic optimization capabilities where the system continuously adapts to changing server conditions, emerging patterns in unstructured data, and new impacting factors. The cognitive models are designed to dynamically learn from incoming data streams and adjust optimization strategies in real-time, enabling the system to scale to handle increasing numbers of parameters and data types while maintaining or improving reliability through continuous learning rather than static configuration rules
Data Source
AI summary
Examples of a process optimization system are provided. The system may obtain a query from a user and process data associated with the query from a plurality of data sources. The system may identify a plurality of factors and a target variable from the process data. The system may identify a factor range for the plurality of factors. The system may deconstruct the factor range to identify a plurality of data partitions. The system may identify a data pruning activator based on the plurality of data partitions and a preponderant data partition therefrom. The system may identify a plurality of clusters associated with the preponderant data partition. The system may identify a preponderant cluster from the plurality of clusters. The system may identify a confidence score associated with the preponderant cluster. The system may generate a process optimization result based on the preponderant cluster and the confidence score.