Distributed Storage Completeness via Hash-Based Replica Verification
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Solution Overview
Problem
In distributed systems, particularly in multi-tenant environments, ensuring data completeness and integrity across geographically distributed tenant systems is challenging, with issues such as data replication failures, configuration errors, and inconsistent data access.
Innovation Solution
Implementing a data completeness process using an Accumulator and Verifier system, where the Accumulator computes hashes on centralized data changes and shares them with participant nodes, and the Verifier verifies the replicated data by comparing local hashes with shared hashes to ensure data accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is replicated across geographically distributed participant nodes, then data accessibility and system availability are improved, but data consistency and integrity across replicas deteriorate
Solution Approach 1:
The system implements a verification mechanism where the centralized node receives verification data from participant nodes containing hashes of replicated data. The centralized node compares these hashes against its own data state to detect inconsistencies. This feedback loop enables continuous monitoring and detection of replication failures, allowing the system to maintain data consistency awareness across distributed nodes.
Solution Approach 2:
The patent replaces traditional mechanical data synchronization verification with cryptographic hash functions. Instead of comparing entire data sets between centralized and distributed nodes, the system uses hash computations to create compact verification tokens. This substitution dramatically reduces communication overhead and verification complexity while maintaining strong integrity guarantees through cryptographic properties of hash functions.
2Device complexity
If traditional data replication methods are used without verification, then system complexity is reduced, but detection of replication failures and data inconsistencies becomes difficult
Solution Approach 1:
The system introduces cryptographic hashes as intermediary verification tokens between the centralized node and participant nodes. These hashes serve as compact representations of data state that can be easily transmitted and verified. The intermediary hash mechanism simplifies the verification process compared to direct data comparison, reducing communication overhead while enabling reliable detection of replication failures.
Solution Approach 2:
The patent transforms the verification problem by changing the parameter being transmitted from entire data sets to cryptographic hash values. This parameter transformation maintains the essential verification capability while dramatically reducing data transmission requirements. The hash function converts complex data states into compact, easily comparable values that enable efficient failure detection without proportionally increasing system complexity.
3Reliability
If comprehensive data verification is performed across all replicated data, then data integrity is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential verification information from complete data sets by computing cryptographic hashes. Instead of verifying entire data sets, the system extracts hash values that uniquely represent data state. This extraction approach maintains strong integrity verification capability while dramatically reducing the amount of data that must be transmitted and processed during verification operations.
Solution Approach 2:
The patent uses cryptographic hash copies as verification tokens instead of replicating entire data sets for verification purposes. The hash function creates a compact digital fingerprint that serves as a sufficient copy for integrity verification. This copying strategy enables comprehensive verification of data integrity across distributed nodes without the prohibitive time and resource costs of transmitting and comparing complete data sets.
Data Source
AI summary
Provided are processes, systems, and methods for replicating data and verification of replicated data. A centralized storage node may generate shared values by which participant nodes may verify completeness of replicated data. For example, a hash sum based on data changes within the centralized data store may be determined and shared with a participant node for verification of completeness of replicated data. Data changes performed within the centralized data store are also communicated to the participant node for implementation on replicated data, and a local hash sum is determined by the participant node based on the data changes. If the local hash sum and the shared hash sum agree, completeness of one or more data entries within the replica database may be confirmed.