Policy Proposal System for Intelligent Storage Management
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Solution Overview
Problem
Conventional technologies lack an efficient method for creating optimized policies to manage storage space consumption across primary and secondary storage systems, relying on manual user configuration and outdated data analysis.
Innovation Solution
A policy proposal system that applies existing policies to identify matched data for transfer to secondary storage, trains a machine learning system with matched and unmatched data, and predicts new policies to optimize storage space consumption by identifying additional data for transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user configuration is used to create storage policies, then ease of operation is improved, but productivity and storage space optimization are worsened
Solution Approach 1:
The system performs self-service by automatically generating storage policies through machine learning algorithms without requiring manual user configuration. The policy proposal system analyzes storage data patterns and autonomously creates optimized policies for data tiering between primary and secondary storage systems.
Solution Approach 2:
The patent replaces the mechanical manual configuration process with an automated machine learning-based system. The trained machine learning model substitutes for human expert knowledge and manual policy creation, enabling automatic optimization of storage space consumption.
2Productivity
If machine learning system is trained with matched and unmatched data, then productivity and policy optimization are improved, but device complexity increases
Solution Approach 1:
The system segments the storage management function into distinct components: a policy application module that applies policies to identify matched data, a policy proposal system that analyzes unmatched data, and a machine learning training module. This segmentation allows each component to perform its specific function independently, reducing overall system complexity.
Solution Approach 2:
The machine learning model acts as an intermediary between the raw storage data and the policy generation process. It processes large volumes of unmatched data and transforms it into optimized policy recommendations, simplifying the complex task of storage optimization.
3Quantity of substance
If existing policies are applied to identify matched data, then storage space consumption is reduced, but data transfer optimization is worsened
Solution Approach 1:
The system implements feedback by analyzing the results of applying existing policies to identify matched data, then using this information to train machine learning models. The unmatched data serves as feedback that guides the creation of improved policies, creating a continuous optimization cycle that enhances both storage space consumption reduction and data transfer optimization.
Solution Approach 2:
The policy proposal system performs preliminary analysis on unmatched data before final policy generation. By pre-processing and analyzing this data in advance, the system can create more effective policies that optimize data transfer decisions without requiring real-time complex computations.
Data Source
AI summary
Methods, system, and non-transitory processor-readable storage medium for a policy proposal system are provided herein. An example method includes applying at least one policy to data stored on a storage system to identify matched data, where the matched data is data to be moved from the storage system to a secondary storage system. The policy proposal system identifies unmatched data stored on the storage system, where the unmatched data is data that is not identified as the data to be moved from the storage system to the secondary storage system. The policy proposal system trains a machine learning system with the matched data and the unmatched data. The policy proposal system predicts at least one new policy, where application of at least one new policy identifies at least a subset of the unmatched data to be moved from the storage system to the secondary storage system.