Machine Learning Recommendation Engine for Storage Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional storage system monitoring and analysis approaches face challenges in determining and providing proactive recommendations across different storage systems and users, lacking effective methods to learn association rules and generate configuration-related recommendations.
Innovation Solution
A machine learning-based recommendation engine is implemented, processing input data from multiple storage systems to determine association rules using machine learning techniques and applying content filtering to generate configuration-related recommendations for storage system configuration actions and user-support actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional storage system monitoring and analysis approaches are used, then basic monitoring functionality is provided, but proactive recommendations cannot be determined and provided across different storage systems and users
Solution Approach 1:
A recommendation engine is introduced as an intermediary component between storage system monitoring data and users. This engine applies machine learning techniques to processed input data to learn association rules and generates proactive recommendations, enabling recommendation capabilities without directly modifying the core storage systems.
Solution Approach 2:
Conventional rule-based monitoring approaches are replaced with machine learning-based association rule learning. The system uses algorithms that automatically discover patterns and relationships in storage system data, substituting manual configuration with automated intelligent analysis.
2Productivity
If machine learning techniques are applied to learn association rules across storage systems, then proactive recommendations are generated, but processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary processing of input data before applying machine learning techniques. By pre-processing and structuring the data in advance, the system reduces the computational complexity of the subsequent association rule learning process while maintaining recommendation quality.
Solution Approach 2:
The recommendation engine applies content filtering techniques to a subset of learned association rules to generate recommendations. Rather than processing all possible rules, the system selectively filters and applies relevant rules to specific storage systems, reducing overall processing complexity while maintaining effectiveness.
3Measurement precision
If content filtering techniques are applied to generate configuration-related recommendations, then targeted recommendations are provided, but additional processing steps are required
Solution Approach 1:
Association rules are learned and stored in advance through machine learning processes. When recommendations are needed, the system simply filters and retrieves pre-computed rules rather than performing full analysis in real-time, reducing recommendation generation time while maintaining accuracy.
Solution Approach 2:
Content filtering is applied specifically to generate configuration-related recommendations for particular storage systems based on their unique characteristics and the learned association rules. Rather than uniform processing of all data, the system tailors the filtering process to local needs, improving relevance while optimizing processing efficiency.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for implementing a machine learning-based recommendation engine for storage system usage within an enterprise are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; determining association rules applicable to the multiple storage systems by applying machine learning techniques to the processed input data; generating configuration-related recommendations applicable to one or more of the storage systems by applying content filtering techniques to the determined association rules; and outputting, via user interfaces, the configuration-related recommendations to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.