Data Placement Across Storage Tiers and Compression Options
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Solution Overview
Problem
Existing storage solutions lack an efficient method for multi-dimensional placement of data sets across different storage devices and compression algorithms, which limits optimal resource utilization and service level objectives.
Innovation Solution
A method that orders data sets based on need and defines storage areas based on device type, compression algorithm, and associated parameters, using a formula to determine the best combination for placement, ensuring optimal resource utilization and service level objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data sets are placed in storage areas based on single-dimensional criteria (e.g., only compression algorithm or only device type), then placement simplicity is maintained, but storage efficiency and resource utilization are suboptimal
Solution Approach 1:
The patent transitions from single-dimensional placement criteria to multi-dimensional placement by considering multiple parameters simultaneously (compression algorithm, device type, data set properties, service level objectives). This dimensional expansion enables more nuanced and efficient data placement decisions that optimize storage resource utilization across heterogeneous storage environments.
Solution Approach 2:
The system dynamically adjusts placement parameters based on data set characteristics and storage area properties. By changing and optimizing multiple parameters (compression ratio, device performance metrics, service level thresholds), the system achieves superior storage efficiency without requiring overly complex manual configuration.
2Quantity of substance
If multiple compression algorithms are used across different storage devices, then storage capacity is optimized, but determining the best placement combination becomes complex
Solution Approach 1:
The system implements automated placement determination that evaluates multiple compression algorithms and device types independently, then self-selects the optimal combination based on predefined criteria and service level objectives. This self-service approach eliminates manual intervention complexity while maximizing storage capacity utilization across heterogeneous resources.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor storage area performance and data set characteristics, then use this information to refine placement decisions. By continuously evaluating the effectiveness of different compression and device combinations, the system optimizes storage capacity utilization while simplifying the determination process through learned patterns and performance data.
3Productivity
If data sets are ordered and placed based on multiple parameters including service level objectives, then resource utilization is optimized, but placement processing time increases
Solution Approach 1:
The system performs preliminary ordering and categorization of data sets based on key parameters and service level objectives before actual placement. By pre-processing and organizing data sets according to their characteristics and requirements, the system reduces the computational complexity of the final placement decision, thereby optimizing resource utilization while minimizing additional processing time.
Data Source
AI summary
A multi-dimensional placement methodology, system and computer readable medium is presented. A plurality of data sets is ordered by need. A plurality of storage areas are defined based on a storage device type, an associated compression algorithm, and a plurality of parameters associated with different properties of the particular storage device and the compression algorithm being used. A data set is placed in a selected storage area based on a determination of which storage area provides a desired combination of the storage device type and compression.