Data Placement Across Mixed Storage Systems
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Solution Overview
Problem
Current data placement techniques are not effective for storage devices within a storage center that have different storage types and implement various data reduction techniques, as they only address single shared storage types.
Innovation Solution
A computer-implemented method that identifies and characterizes multiple storage systems, performs simulations based on their data reduction techniques, and determines the optimal storage system for data volumes by evaluating capacity, data reduction, cost, and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current data placement techniques are used, then storage management is simple for single storage types, but they cannot operate with storage devices having different storage types and data reduction techniques
Solution Approach 1:
The patent changes the parameters of storage systems by introducing multiple data reduction techniques (compression, deduplication, erasure coding) with different strengths and characteristics. Each storage system is characterized by specific parameters including data reduction technique type, data reduction ratio, and processing overhead, allowing the system to adapt to heterogeneous storage environments while maintaining manageable complexity through standardized evaluation metrics
2Measurement precision
If storage simulations are performed for multiple storage systems, then optimal storage location is determined, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-characterizing storage systems with their data reduction techniques and performance metrics before actual data placement decisions are made. The system pre-evaluates multiple storage systems using standardized simulations with representative data patterns, storing the results for rapid retrieval and comparison when actual data placement is needed, thus reducing real-time decision latency
Solution Approach 2:
The patent changes parameters by evaluating storage systems across multiple dimensions including data reduction ratio, processing overhead, and capacity utilization. By varying simulation parameters such as data workload characteristics and storage system configurations, the system achieves comprehensive optimization accuracy while identifying patterns that reduce the need for exhaustive simulations in all scenarios
Data Source
AI summary
A computer-implemented method according to one embodiment includes identifying a plurality of storage systems within a storage environment, determining characteristics of each of the plurality of storage systems, the characteristics including one or more data reduction techniques implemented by each of the plurality of storage systems, performing a plurality of storage simulations of one or more data volumes, utilizing the characteristics of each of the plurality of storage systems, and determining one of the plurality of storage systems to store the one or more data volumes, based on results of the plurality of storage simulations.