Distributed Data Redundancy Using Shards for Multi-Failure Recovery
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Solution Overview
Problem
Current redundancy and recovery methods in distributed data storage systems lack the speed, consistency, and efficiency required to address diverse failure scenarios, particularly in multi-tenant cloud environments where physical and virtual resources are spread across various locations, leading to inadequate fault tolerance and recovery.
Innovation Solution
Implementing a multi-layer redundancy scheme that combines local and geographic distributions, where data objects are divided into shards encoded with error-correcting codes and stored across diverse nodes, using a centralized coordinator for optimized shard placement and recovery, without relying on separate technologies like RAID on a single server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored using traditional redundancy methods in distributed storage systems, then fault tolerance is provided, but recovery speed and efficiency are insufficient when addressing diverse failure scenarios
Solution Approach 1:
The patent segments data into multiple shards and distributes them across different storage nodes and geographic locations. This segmentation enables parallel recovery operations where multiple shards can be retrieved and reconstructed simultaneously, significantly improving recovery speed while maintaining fault tolerance through distributed redundancy
Solution Approach 2:
The patent introduces geographic distribution as an additional dimension beyond traditional local redundancy. By storing shards across multiple geographic locations with different failure modes, the system achieves both local and geographic fault tolerance, allowing recovery from various failure scenarios including regional outages while maintaining efficient recovery through multiple available sources
2Adaptability or versatility
If resources are spread across various locations in multi-tenant cloud environments, then system capacity and availability are improved, but complexity of managing redundancy and recovery increases
Solution Approach 1:
The patent implements a universal shard management system that handles multiple failure types and recovery scenarios through a single coordinated mechanism. The same shard distribution infrastructure serves both local and geographic redundancy requirements, eliminating the need for separate management systems and reducing overall complexity despite the distributed architecture
Solution Approach 2:
The system employs feedback mechanisms where the coordinator monitors shard availability and node status across distributed locations, automatically adjusting recovery operations based on real-time system state. This feedback loop simplifies management by enabling autonomous recovery decisions without complex manual intervention across multiple locations
3Reliability
If local and geographic distributions are implemented, then fault tolerance for diverse failure scenarios is enhanced, but resource usage efficiency may be compromised
Solution Approach 1:
The patent applies local quality by implementing tiered redundancy where frequently accessed data maintains higher availability through local replication, while less critical data uses geographic distribution. This differentiated approach ensures fault tolerance for diverse failure scenarios while optimizing resource usage by not over-provisioning redundancy for all data equally
Solution Approach 2:
The system implements partial redundancy by storing only the necessary number of shards required for recovery (k out of n) rather than full replication. This partial action approach provides adequate fault tolerance for both local and geographic failures while minimizing the total resource consumption compared to complete duplication of data across all locations
Data Source
AI summary
The present disclosure presents systems and methods for distributed data storage with enhanced redundancy to ensure high fault tolerance and data availability. A multi-layer scheme can be leveraged that combines local redundancy within datacenters and geographic redundancy across multiple locations. Data objects to be stored can be divided into shards and encrypted using error-correcting codes, such as Reed-Solomon codes, and distributed across a swarm of nodes. These nodes can be organized into nexuses and rings to attempt to optimize for factors such as redundancy and fault recovery. A centralized coordinator can oversee tasks such as shard placement, system health monitoring, and failure recovery. Such approaches can help to ensure seamless data restoration during device, node, or regional failures while providing a scalable and efficient framework for robust distributed storage.