Network Upgrade Recommender Using Usage Pattern Analysis
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Solution Overview
Problem
Conventional network hardware and software upgrade systems often rely on limited criteria, such as current software version comparisons, failing to provide comprehensive and data-driven recommendations for network-wide upgrades, which may not account for device usage patterns or hardware-software compatibility.
Innovation Solution
A method and system that generate hardware and software upgrade recommendations by integrating network environment, hardware configuration, software configuration, and product information, including usage patterns and compatibility data, to identify devices that can be upgraded for new functionality, with rankings and supporting data for each recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems make recommendations based on limited criteria such as current software version compared to current release version, then the recommendation process is simple and quick, but the comprehensiveness and accuracy of upgrade recommendations deteriorates
Solution Approach 1:
The recommendation system is segmented into multiple independent analysis modules: usage pattern analysis, compatibility analysis, functionality assessment, and prioritization ranking. Each module processes specific aspects of upgrade evaluation separately, then integrates results to provide comprehensive recommendations without overwhelming complexity
Solution Approach 2:
The system merges multiple data sources including network management system output, hardware configuration data, software configuration data, and product information into a unified recommendation framework. This integration enables comprehensive evaluation while managing complexity through centralized processing
2Loss of information
If the system integrates multiple data sources including usage patterns and compatibility data to generate comprehensive recommendations, then the quality of recommendations improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing and storing usage patterns, compatibility data, and product information in structured formats before generating recommendations. This advance preparation reduces processing time during actual recommendation generation while maintaining data completeness
3Ease of operation
If the system provides detailed rankings and supporting data for each recommendation, then the usefulness for network administrators improves, but the complexity of the recommendation output increases
Solution Approach 1:
The recommendation output provides different levels of detail for different recommendations based on their priority and characteristics. High-priority recommendations receive detailed rankings and supporting data, while lower-priority ones provide summarized information. This localized quality approach enhances usability without uniformly increasing output complexity
Data Source
AI summary
Methods, systems and computer readable media for generating hardware and software upgrade recommendations for a managed network of devices are described.