Thin-Provisioned Storage Container Selection Using Historical Usage Trends
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Solution Overview
Problem
In thin-provisioned data storage systems, identifying optimal placement for new data volumes to meet storage capacity demands is complex due to the need to manage both real and virtual capacities, as well as over-allocation, which can lead to inefficient resource utilization and potential data access issues.
Innovation Solution
A method that determines real data storage capacity, over-allocation information, and future anticipated use from historical data to select candidate containers for new volumes, ensuring sufficient virtual and real capacity, optimal utilization, and compliance with policy thresholds, using a memory management arrangement and persistent memory for data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If thin-provisioning is used to provide virtual storage capacity greater than real capacity, then storage utilization efficiency is improved, but complexity of managing real and virtual capacities increases
Solution Approach 1:
The patent introduces a storage manager as an intermediary component that handles the complexity of managing thin-provisioned storage. The storage manager receives volume creation requests, determines appropriate containers using multiple criteria (current usage, historical trends, over-allocation policies), and manages the mapping between virtual volumes and physical storage containers, thereby shielding users from the underlying complexity.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring container usage metrics, historical data storage patterns, and over-allocation status. This feedback is used to dynamically adjust container selection decisions and provide insights to administrators about storage utilization, enabling informed management of thin-provisioned resources.
2Measurement precision
If multiple criteria are used to select candidate containers, then optimal placement accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the container selection process into distinct evaluation criteria: current usage metrics, historical growth patterns, over-allocation status, and policy constraints. Each criterion is evaluated separately and combined to form an overall selection decision, making the complex process more manageable and interpretable.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing historical usage data, container capacity metrics, and policy rules in advance. When a volume creation request arrives, the system queries these pre-prepared data structures rather than computing everything from scratch, reducing real-time computational complexity.
3Measurement precision
If historical data is analyzed for future anticipation, then storage planning accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary analysis of historical data storage patterns and stores the results in advance. Trend analysis and capacity projection calculations are pre-computed based on historical usage data, so when volume placement decisions need to be made, the system can quickly retrieve pre-analyzed trends rather than performing complex temporal analysis in real-time.
Data Source
AI summary
Various embodiments storing volumes of data in a data storage system, including one or more data storage containers, the data storage containers being thin-provisioned to provide virtual data storage capacity which is greater than a real data storage capacity of the data storage hardware are provided. In one embodiment, by way of example only, a real data storage capacity of the data storage system for accommodating new volumes is determined. Over-allocation information relating to one or more data storage containers is determined. Extrapolated future anticipated use of one or more containers of the data storage system from historical data storage use information is determined. One or more candidate data storage containers on the basis of information from the determining the real data storage capacity, over-allocation information, and the extrapolated future anticipated use is selected. Additional system and computer program product embodiments are disclosed and provide related advantages.