Geographically Distributed Erasure Coding for Low-Risk Object Storage
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Solution Overview
Problem
Current digital data protection methods, such as traditional RAID storage, are inefficient in reconstructing data after corruption and do not effectively manage data distribution across geographically diverse locations to mitigate risks like natural disasters and political instability.
Innovation Solution
A geographically distributed erasure coding system that uses multiple storage memories to store and manage digital datasets, with an erasure coding policy that distributes object blocks based on current status parameters such as latency, availability, and risk metrics, ensuring data redundancy and integrity across diverse locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RAID storage is used for data protection, then data redundancy is achieved, but data reconstruction time and overhead are excessive
Solution Approach 1:
The patent segments data into multiple object blocks and distributes them across geographically dispersed storage locations. This segmentation enables parallel data reconstruction from multiple sources simultaneously, reducing overall reconstruction time compared to traditional RAID systems that rely on sequential or limited parallel reconstruction paths.
Solution Approach 2:
The patent transitions from traditional RAID's single-dimension (local) storage architecture to a multi-dimensional (geographically distributed) architecture. This dimensional change allows data to be reconstructed from multiple geographic locations in parallel, significantly reducing reconstruction time and overhead while maintaining data protection.
2Quantity of substance
If data is stored in single location or limited locations, then storage cost is reduced, but risk of data loss from natural disasters or political instability increases
Solution Approach 1:
The patent applies local quality by distributing data blocks to storage locations with different geographic and political characteristics. Each location has unique risk profiles (different natural disaster risks, political stability), and the system selectively places data blocks in locations that collectively minimize overall risk while optimizing storage cost efficiency.
Solution Approach 2:
The system dynamically changes the parameter of data distribution by considering multiple status parameters including latency, availability, and risk metrics for each storage location. This allows the system to adaptively allocate data blocks to locations that optimize the balance between storage cost and data loss risk mitigation.
3Reliability
If data is distributed across geographically diverse locations, then data loss risk is reduced, but storage complexity increases
Solution Approach 1:
The patent implements a universal erasure coding policy that handles multiple functions: data distribution, risk assessment, location selection, and data reconstruction coordination. This multi-functional policy framework simplifies the overall system complexity by providing a unified control mechanism that manages geographically distributed storage without requiring complex specialized subsystems.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor current status parameters (latency, availability, risk metrics) of storage locations and dynamically adjust data distribution decisions. This feedback loop automates the management of geographic distribution complexity, allowing the system to adapt to changing conditions without manual intervention while maintaining simplified operational procedures.
4Reliability
If data blocks are distributed based on multiple status parameters, then data protection optimization is achieved, but distribution complexity increases
Solution Approach 1:
The erasure coding policy is implemented as a dynamic system that continuously evaluates multiple status parameters (latency, availability, risk metrics) and adjusts data block distribution accordingly. This dynamic approach optimizes data protection by adapting to real-time conditions while using automated algorithms to manage the complexity of multi-parameter decision-making, preventing manual management burden.
Data Source
AI summary
A geographically distributed erasure coding system includes multiple computer readable, non-transitory storage memories capable of storing a digital dataset including multiple object blocks, where each storage memory is configured to store one or more of the object blocks of the dataset according to an erasure coding policy. The system includes one or more processors configured to implement the erasure coding policy by distributing the multiple object blocks of the dataset to the multiple storage memories according to distribution criteria of the erasure coding policy, and the distribution criteria include at least one status parameter associated with each storage memory. The multiple storage memories are geographically distributed at different locations from one another.