Automated Data Placement Across Storage Tiers
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Solution Overview
Problem
Current data storage systems lack an efficient method to dynamically optimize data placement across different storage tiers based on performance and capacity limits, leading to suboptimal performance and resource utilization.
Innovation Solution
A method that determines scores for data portions using metrics like expected I/O size, random read miss, sequential read miss, and write operations, and applies promotion and demotion thresholds to move data between storage tiers, ensuring higher performance data is stored on higher performance devices and less active data is stored on lower performance devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored on high-performance storage devices, then system performance is improved, but storage cost increases
Solution Approach 1:
The patent applies local quality by assigning different data portions to different storage tiers based on their access patterns and performance requirements. Hot data (frequently accessed) is placed on high-performance SSDs, while cold data (less frequently accessed) is placed on lower-performance HDDs. This selective placement optimizes system performance for critical operations while reducing overall storage costs by not placing all data on expensive high-performance devices.
Solution Approach 2:
The patent implements dynamic data placement by continuously monitoring data access patterns and automatically migrating data between storage tiers. The system dynamically adjusts which data resides on which storage device based on changing workload requirements, ensuring that high-performance storage resources are utilized only when needed for performance-critical operations, thereby optimizing the balance between performance and cost.
2Productivity
If automated data placement algorithms are implemented, then data optimization is improved, but computational complexity increases
Solution Approach 1:
The patent applies self-service by implementing automated evaluation and placement algorithms that independently analyze data access patterns, calculate optimization scores, and migrate data between storage tiers without requiring manual intervention. The system monitors its own performance metrics and autonomously makes data placement decisions, reducing the computational burden on external systems while maintaining continuous optimization.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring data access patterns, read/write operations, and storage system performance metrics. This feedback information is used to dynamically adjust data placement decisions, allowing the system to learn from actual usage patterns and optimize data placement automatically. The feedback loop enables the system to adapt to changing workload requirements without increasing overall computational complexity.
Data Source
AI summary
Described are techniques for configuring data storage. Criteria identifying limits is received for a plurality of storage tiers for one or more logical devices. The storage tiers include a first storage tier and one or more other storage tiers where the first storage tier is higher performing than the other storage tiers. First processing is performed to determine a first set of data portions of the logical devices to be stored on physical devices of the first storage tier subject to limits included in said criteria. First processing includes determining scores for data portions of the logical devices. Each score is calculated using one or more metrics including a first metric weighted based on an expected I/O size. The data portions are ranked in accordance with the scores. The first set of data portions stored in the first storage tier is selected based on the score ranking.