Data Verification for Network Storage Integrity
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Solution Overview
Problem
Conventional tools for testing network storage servers lack data validation capabilities, particularly in verifying data integrity and identifying errors provided by storage servers, which is crucial for deduplication, compression, caching, and tiering functionalities.
Innovation Solution
A data verification application generates a pseudo-random test data stream with variable compressibility, using a seed value and compressibility parameter, allowing for verifiable and repeatable testing of data integrity across network storage systems, including deduplication, compression, caching, and WAN optimization devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional testing tools are used for network storage servers, then testing can be performed, but data validation and integrity verification capabilities are lacking
Solution Approach 1:
The patent segments the testing system into distinct functional modules: a test data generation component that creates structured test data with known characteristics, a storage server component that processes the data through compression/deduplication/caching operations, and a verification component that validates data integrity. This modular segmentation enables comprehensive data validation without requiring a single complex testing tool, thereby improving reliability while managing complexity.
2Quantity of substance
If compression and deduplication algorithms are implemented to save storage space, then storage efficiency improves, but data verification and error identification become more difficult
Solution Approach 1:
The patent implements feedback mechanisms where the testing system generates test data with known patterns and characteristics, stores it through the compression and deduplication processes, then retrieves and verifies the data. The verification component compares the retrieved data against the original test data patterns, providing feedback on whether compression and deduplication operations maintained data integrity. This feedback loop enables accurate error identification despite the complexity introduced by compression algorithms.
Solution Approach 2:
The testing system utilizes parameter changes by varying test data characteristics (such as compressibility patterns, data types, and access frequencies) to stress-test different compression and deduplication scenarios. By changing parameters like compression ratios and data repetition patterns, the system can verify that error identification remains accurate across different storage optimization conditions, thereby maintaining measurement precision while achieving efficient storage utilization.
3Speed
If caching and tiering operations are performed to improve access time, then data retrieval speed improves, but data verification across different storage tiers becomes complex
Solution Approach 1:
The patent implements a universal verification mechanism that can operate across multiple storage tiers (cache, tiered storage, and primary storage). The test data generation and verification components are designed to be tier-agnostic, enabling the same verification logic to function whether data is accessed from fast cache memory or slower tiered storage. This multi-functionality allows data verification across different storage tiers without proportionally increasing complexity, as the core verification algorithm remains consistent regardless of the storage tier involved.
Data Source
AI summary
A data verification application receives a test configuration data, the test configuration data comprising a seed value and a parameter. The data verification application generates a pseudo-random test data stream comprising a plurality of words, wherein a value of each of the plurality of words is based on the seed value, the parameter and an offset of each word within the pseudo-random test data stream.