Grid Encoded Data Storage Rebundling for Availability
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Solution Overview
Problem
Modern network computing and data storage systems face challenges in balancing data availability and integrity, particularly in distributed environments, where existing error correction and redundancy schemes can lead to degraded performance and customer experience due to data retrieval delays.
Innovation Solution
The implementation of grid encoded data storage systems that use shard-based encoding techniques, including parity encoding and redundancy codes like Reed-Solomon codes, to distribute data across multiple storage devices and datacenters, ensuring data durability and availability while minimizing storage volume requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error correcting and error tolerance schemes are used to ensure data integrity, then data reliability is improved, but data retrieval time increases leading to degraded customer experience
Solution Approach 1:
The patent segments data into multiple shards distributed across different storage devices and datacenters. Each shard is independently stored and can be retrieved in parallel, reducing overall retrieval time while maintaining data integrity through distributed redundancy. The segmentation allows selective retrieval of only necessary shards rather than accessing entire data sets.
Solution Approach 2:
The patent introduces a spatial dimension to data storage by distributing shards across multiple geographic datacenters and storage devices. This multi-dimensional distribution enables parallel access paths and reduces retrieval bottlenecks, allowing the system to maintain high reliability through geographic redundancy while improving retrieval performance through concurrent access.
2Reliability
If data is distributed across multiple storage devices and datacenters, then data availability and durability are improved, but storage volume requirements increase
Solution Approach 1:
The patent dynamically adjusts redundancy parameters and shard distribution configurations based on actual storage capacity and performance requirements. By optimizing the number of replicas and their placement across storage devices, the system achieves high availability without unnecessarily increasing total storage volume. The parameters for data distribution can be tuned to match available resources.
Solution Approach 2:
The patent creates distributed copies of data shards across multiple storage devices and datacenters, but optimizes the copy strategy to minimize total storage overhead. Rather than simple full replication, the system uses intelligent placement and potential compression techniques to maintain availability while controlling storage volume consumption.
3Reliability
If extensive redundancy codes like Reed-Solomon codes are used, then data recovery capability from extensive losses is improved, but computational complexity and processing overhead increase
Solution Approach 1:
The patent segments the complex error correction process into distributed operations at individual storage nodes, where simpler local parity calculations are performed. This distributes the computational burden across many simple operations rather than requiring complex centralized processing, reducing per-node computational complexity while maintaining overall recovery capability.
Solution Approach 2:
The patent extracts and pre-computes certain redundancy elements at data ingestion time, separating the complexity of code generation from the simplicity of code verification during retrieval. This allows the system to use strong error correction codes like Reed-Solomon without burdening the retrieval path with complex computational overhead.
4Adaptability or versatility
If data is archived in distributed locations, then data accessibility to multiple consumers is improved, but data retrieval delays occur due to network latency
Solution Approach 1:
The patent segments data into distributed shards that can be accessed simultaneously from multiple locations. This segmentation enables parallel retrieval operations where different consumers can access different shards concurrently, reducing overall retrieval delay while maintaining broad accessibility. The segmented structure supports multi-consumer scenarios without sequential access bottlenecks.
Solution Approach 2:
The patent adds geographic and topological dimensions to data access by distributing shards across multiple datacenters and network paths. This multi-dimensional architecture provides multiple concurrent access routes, allowing consumers to retrieve data from the nearest or least-loaded location, thereby reducing network latency while maintaining universal accessibility.
Data Source
AI summary
Techniques for rebundling grid encoded data storage systems are described herein. A new grid of shards is created based in part on shards from a first grid of shards. The second grid of shards contains data shards, including one or more data shards from the first grid of shards, and derived shards that are indexed by row and column and is configured so that shards in the second grid are reproducible from shards in the same row using a first redundancy code and are also reproducible from shards in the same column using a second redundancy code. The derived shards of the second grid of shards are then derived from other shards in the second grid of shards using the first redundancy code and the second redundancy code.