Hash Tree Validation for Network File System Data Integrity
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Solution Overview
Problem
Large and sophisticated storage systems, such as those in High Performance Computing, face increased data corruption risks due to the growing number of components and data volume, which existing distributed file systems struggle to effectively detect and prevent.
Innovation Solution
A method using hash trees to validate data integrity by generating leaf hashes for data segments, interior node hashes, and a root hash, which are then used to verify data integrity throughout the data pipeline from client memory to network file systems, allowing for end-to-end data integrity validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed file systems use traditional checksum methods to detect data corruption, then data integrity can be verified at individual stages, but end-to-end data integrity across the entire data pipeline cannot be effectively ensured
Solution Approach 1:
The patent segments the data pipeline into distinct stages (client memory, network transmission, server storage) and applies hash validation at each segment boundary. Leaf hashes validate individual data segments, interior node hashes validate intermediate aggregations, and root hashes validate complete data sets at each stage, enabling granular end-to-end integrity verification without requiring complete re-validation at each step.
Solution Approach 2:
The system performs preliminary hash computations at the client side before data leaves the client memory. Leaf hashes are computed for individual data segments, which are then aggregated into interior node hashes and finally into root hashes. These pre-computed hashes travel with the data through the network and are used for efficient verification at the server side, avoiding redundant computation.
2Reliability
If the system validates data at each stage of the data pipeline, then data corruption can be detected, but the validation process becomes computationally expensive and time-consuming
Solution Approach 1:
The validation process is segmented into hierarchical levels matching the data structure. Instead of validating all data segments individually at each stage, the system validates at multiple granularities: leaf level for individual segments, interior node level for groups of segments, and root level for complete data sets. This segmentation allows selective validation based on the specific needs of each pipeline stage.
Solution Approach 2:
The system performs partial validation at intermediate stages by validating only the aggregated interior node hashes rather than all individual leaf hashes. This partial action approach provides sufficient integrity verification for network transmission without the full computational cost of validating every single data segment at each stage, while still enabling complete end-to-end verification when needed.
3Adaptability or versatility
If multiple checksums are computed at different block sizes, then validation can be performed at multiple granularities, but the computational overhead increases significantly
Solution Approach 1:
The hash computation is segmented into a hierarchical tree structure where leaf nodes represent individual data segments at the finest granularity, interior nodes represent aggregated groups at intermediate granularities, and the root node represents the complete data set at the coarsest granularity. This segmentation enables the system to compute and store hashes at multiple granularities efficiently, reusing computations from parent nodes rather than recalculating from scratch.
Solution Approach 2:
The system merges hash computations across different granularities by combining leaf hashes into interior node hashes, and interior node hashes into root hashes using a consistent hashing algorithm. This merging approach allows a single computation pass to generate hashes at multiple levels of granularity simultaneously, rather than requiring separate computation passes for each granularity level, thereby reducing overall computational energy consumption.
Data Source
AI summary
A computer readable storage medium, embodying instructions executable by a computer to perform a method, the method including: validating a memory write of data segments using a first number of leaf hashes of a first hash tree, where each of the first number of leaf hashes is associated with one of the data segments of a first block size, generating interior node hashes based on the first number of leaf hashes, where each of the interior node hashes is associated with a second block size, generating a first root hash using the interior node hashes, where the first root hash is associated with a remote procedure call size, transmitting the first root rash and the data segments to a network file system, where the transmission is performed using the remote procedure call size, and validating the transmission of the data segments using the first root hash.