Storage Tier Data Movement Evaluation for Thin Devices
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Solution Overview
Problem
Current data storage systems lack efficient methods for optimizing data movement between different storage tiers based on varying performance and capacity limits, leading to suboptimal performance and resource utilization.
Innovation Solution
A method for evaluating data movement alternatives by receiving criteria such as capacity and performance limits, performing processing to determine promotion and demotion thresholds, and selecting optimal performance limits for data movement between storage tiers, including solid state, disk drive, and other storage devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored on high-performance storage devices (solid state), then response time is improved, but storage cost increases
Solution Approach 1:
The storage system is divided into multiple tiers with different performance characteristics (solid state, disk drive, other storage devices). Data is segmented and placed in appropriate tiers based on performance requirements and cost considerations, allowing the system to achieve high response times for critical data while using lower-cost storage for less time-sensitive data.
Solution Approach 2:
Different portions of data receive different quality of service based on their specific requirements. Hot data that requires fast access is stored on high-performance solid state devices, while cold data is stored on lower-cost devices. This local differentiation optimizes both response time and storage cost by matching data characteristics with appropriate storage media.
2Productivity
If automated data movement between storage tiers is implemented, then resource utilization is improved, but device complexity increases
Solution Approach 1:
The storage system implements automated data movement capabilities that operate without manual intervention. The system autonomously evaluates data access patterns, determines optimal storage tier placements, and executes data movement between tiers based on predefined criteria involving performance and capacity limits. This self-service approach improves resource utilization while minimizing the need for complex manual management.
Solution Approach 2:
The system dynamically adjusts data movement decisions based on changing parameters such as access frequency, workload characteristics, and storage capacity availability. By monitoring these parameters and adapting data placement strategies accordingly, the system optimizes resource utilization without requiring overly complex fixed-rule management systems.
3Productivity
If multiple sets of performance limits are evaluated for data movement, then performance optimization is improved, but processing time increases
Solution Approach 1:
The system evaluates multiple sets of performance limits and data movement alternatives, but applies intelligent pruning to avoid exhaustive analysis. By assessing alternatives in order of likelihood to improve performance and stopping when sufficient optimization is achieved, the system obtains good performance optimization without the full processing burden of evaluating every possible alternative in detail.
Data Source
AI summary
Described are techniques for evaluating data movement alternative. A set of criteria including capacity and performance limits is received. First processing is performed to evaluate a plurality of alternatives for use in data movement with respect to a set of logical devices having data stored on a set of physical storage devices. Each of the plurality of alternatives includes a different set of data movement criteria comprising capacity limits and a different set of performance limits. The set of physical storage devices includes at least a first physical device of one of a plurality of storage tiers and a second physical device of another one of the plurality of storage tier. One of the sets of performance limits is selected in accordance with the first processing.