Geographically Distributed Erasure Coding for Risk-Aware Object Storage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital object storage systems face challenges in efficiently managing and distributing data across geographically dispersed locations to ensure data integrity and availability while balancing cost and risk, particularly in the context of natural disasters and political instability.
Innovation Solution
A geographically distributed erasure coding system that uses multiple computer-readable storage memories and processors to implement an erasure coding policy, distributing object blocks based on current status parameters such as latency, availability, and risk metrics to ensure data redundancy and integrity across diverse locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in a single location or closely clustered locations, then storage cost is reduced, but data availability and integrity are compromised due to risks from natural disasters and political instability
Solution Approach 1:
The patent segments data into multiple object blocks and distributes them across geographically dispersed storage locations. This segmentation allows the system to maintain data availability (improving reliability) while storing blocks in cost-effective locations, resolving the contradiction between data availability and storage cost
Solution Approach 2:
The system applies local quality by assigning different characteristics to different storage locations based on risk profiles. Critical data blocks are stored in geographically dispersed locations with lower risk exposure, while less critical blocks may be stored in more cost-effective locations, optimizing both availability and cost
2Reliability
If data is geographically distributed across multiple locations, then data integrity and availability are improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms that automatically monitor storage location status, data integrity, and risk conditions. This feedback enables the system to dynamically adjust data block distribution and reconstruct data as needed, maintaining data integrity while managing system complexity through automation
Solution Approach 2:
The erasure coding system provides self-service capabilities where the system automatically performs data reconstruction and redistribution without manual intervention. This self-service approach maintains data integrity across distributed locations while minimizing the operational complexity burden on users
3Ease of manufacture
If traditional RAID storage is used, then implementation is simpler, but data reconstruction time and overhead are increased
Solution Approach 1:
The system changes the fundamental parameters of data protection by using erasure coding instead of traditional RAID parity schemes. This parameter change enables more efficient data reconstruction algorithms that reduce reconstruction time and overhead while maintaining implementation feasibility through standardized libraries and protocols
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.