Compliance-Aware Replicator Metadata Segmentation
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Solution Overview
Problem
Existing data replication methods in multiple cluster systems are inefficient, as they require manual intervention and result in suboptimal use of computational resources due to the need for manual tracking and compliance with complex storage requirements.
Innovation Solution
A compliance-aware replicator (CAR) system that uses sequencing metadata and classification metadata to automatically replicate and store data chunks in designated data clusters, reducing the need for system-wide metadata mappings and enhancing computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used for data replication tracking, then compliance with storage requirements can be achieved, but computational resource efficiency deteriorates
Solution Approach 1:
The system implements self-service through automated metadata generation and tracking. The CAR generates sequencing metadata and classification metadata automatically, enabling the replication process to track and comply with storage requirements without manual intervention, thus improving both reliability and computational efficiency
Solution Approach 2:
The system uses feedback mechanisms through metadata repositories that store sequencing and classification information. This feedback loop allows the CAR to automatically adjust replication operations based on compliance requirements and resource availability, resolving the contradiction between manual tracking reliability and automated efficiency
2Loss of information
If system-wide metadata mappings are used for data replication, then comprehensive tracking is achieved, but device complexity increases
Solution Approach 1:
The system segments metadata into distinct types: sequencing metadata for tracking data chunk order and classification metadata for identifying target data clusters. This segmentation reduces overall complexity by organizing metadata into manageable, purpose-specific components while maintaining comprehensive tracking
Solution Approach 2:
The patent extracts essential tracking information into separate metadata structures (sequencing metadata and classification metadata) that are stored in dedicated repositories. This extraction simplifies the main replication process by isolating complex tracking logic into manageable metadata components
3Productivity
If automated replication is implemented, then operational efficiency improves, but compliance awareness requirements increase system complexity
Solution Approach 1:
The system performs preliminary actions by pre-generating sequencing metadata and classification metadata before replication operations begin. The CAR pre-identifies compliant data clusters using classification metadata, enabling automated replication to proceed efficiently without complex real-time compliance checking
Solution Approach 2:
The patent introduces metadata repositories as intermediaries between the replication process and compliance requirements. These repositories store pre-processed sequencing and classification metadata, acting as mediators that simplify the interaction between automated replication operations and compliance constraints
Data Source
AI summary
This application includes a method that is performed store data. The method includes obtaining, by a compliance aware replicator (CAR), a replication request to replicate data; and in response to the replication request: obtaining data chunks, associated with the data, using sequence identifiers of sequencing metadata; replicating the data chunks to obtain replicated data chunks; identifying, using classification metadata associated with the data, a first data cluster of data clusters to store the replicated data chunks; and sending the replicated data chunks to the first data cluster.