Automated Storage Function Selection Based on Data Growth
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Solution Overview
Problem
Current storage systems require manual intervention to determine and apply features like thin provisioning, deduplication, and compression, which can be inefficient and do not automatically adapt to changing data characteristics, leading to suboptimal storage capacity utilization.
Innovation Solution
A system and method that automatically selects storage functions based on computed statistics such as data growth rates and access characteristics, applying thin provisioning, deduplication, compression, or full allocation to optimize storage capacity and access time, allowing for dynamic feature migration and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual intervention is used to determine and apply storage features, then storage capacity can be reduced, but the system becomes inefficient and cannot automatically adapt to changing data characteristics
Solution Approach 1:
The storage system automatically monitors data characteristics and selects appropriate storage functions without manual intervention. The system self-configures by computing statistics on data growth rates and access characteristics, then autonomously determines which functions (thin provisioning, deduplication, compression, or full allocation) to apply to each volume, enabling adaptive optimization of storage capacity utilization.
Solution Approach 2:
The system continuously computes statistics on data growth rates and access characteristics as feedback mechanisms. Based on this computed feedback, the system dynamically adjusts and selects the most appropriate storage functions for each volume, ensuring optimal storage capacity utilization that automatically adapts to changing data characteristics over time.
2Productivity
If multiple storage functions are available, then storage efficiency can be optimized, but system complexity increases
Solution Approach 1:
The system changes operational parameters by computing statistics on data growth rates and access characteristics. Based on these parameter changes, the system automatically selects which storage function to apply to each volume, transforming the complex decision-making process into a parameter-driven automated selection mechanism that simplifies management while optimizing storage efficiency.
Solution Approach 2:
The storage system performs self-configuration by automatically determining which functions to apply to each volume based on computed data characteristics. This self-service approach eliminates the need for manual configuration and reduces management complexity, as the system autonomously optimizes storage efficiency across different volumes without user intervention.
3Quantity of substance
If thin provisioning is applied, then storage capacity is reduced, but access time may increase for frequently accessed data
Solution Approach 1:
The system applies different storage functions to different volumes based on local data characteristics. By computing statistics on data growth rates and access characteristics for each volume, the system locally optimizes each volume with the most appropriate function (thin provisioning, deduplication, compression, or full allocation), ensuring that frequently accessed data receives appropriate treatment while minimizing overall storage capacity requirements.
Data Source
AI summary
A plurality of functions to configure a unit of a storage volume is maintained, wherein each of the plurality of functions, in response to being applied to the unit of the storage volume, configures the unit of the storage volume differently. Statistics are computed on growth rate of data and access characteristics of the data stored in the unit of the storage volume. A determination is made as to which of the plurality of functions to apply to the unit of the storage volume, based on the computed statistics.