Distributed Storage Controller Mapping Engine
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Solution Overview
Problem
Conventional enterprise data storage solutions require substantial and expensive dedicated resources for tracking and mapping data locations, leading to increased complexity and disruption, and often fail to scale dynamically with demand.
Innovation Solution
A distributed data storage system utilizing a data mapping engine, placement engine, and map authority to provide scalable and flexible data access through a computing resource provider, implementing logical block addressing (LBA) maps and replication techniques to ensure data integrity and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated computing resources are used for tracking and mapping data locations, then data access reliability is improved, but system complexity and cost increase
Solution Approach 1:
The patent implements self-service by enabling storage nodes to autonomously track and map data locations using distributed ledgers and consensus algorithms. Each node maintains its own mapping information and validates it through peer consensus, eliminating the need for dedicated central tracking resources while maintaining reliability through distributed verification.
Solution Approach 2:
The patent applies universality by designing storage nodes that simultaneously perform data storage, data location tracking, and mapping functions. Each node is multi-functional, serving both as a data repository and as a component of the distributed mapping system, thereby reducing overall system complexity while maintaining reliable data access.
2Measurement precision
If dedicated computing resources are allocated for data tracking, then data location mapping accuracy is improved, but resource cost increases
Solution Approach 1:
Storage nodes autonomously maintain accurate data location mappings by continuously updating distributed ledgers with their own storage operations. Each node self-validates its mapping accuracy through consensus mechanisms with peer nodes, achieving high mapping precision without requiring additional dedicated computing resources for tracking.
Solution Approach 2:
The patent merges data tracking and mapping functions with the primary data storage operation. The same storage nodes that hold data also maintain and validate location information, combining multiple functions into a unified distributed system that achieves accurate mapping without proportionally increasing computing resource requirements.
3Productivity
If additional computing resources are added for maintenance, then system performance is improved, but productivity decreases due to disruption
Solution Approach 1:
The patent implements dynamic resource allocation where computing resources for tracking and mapping are distributed across active storage nodes rather than being fixed in dedicated maintenance systems. This allows the system to adapt resource usage to actual storage operations, improving performance while minimizing disruption through continuous, distributed maintenance rather than periodic batch processing.
Solution Approach 2:
The distributed ledger and consensus mechanisms operate continuously across all storage nodes, providing ongoing data location tracking and mapping validation without interruption. This continuous distributed maintenance eliminates the need for disruptive periodic maintenance cycles, maintaining both high system performance and continuous productivity.
4Quantity of substance
If conventional storage solutions are implemented, then data storage capacity is achieved, but scalability is limited
Solution Approach 1:
The patent segments the data storage system into independent, identical storage nodes that each maintain local distributed ledger copies. This segmentation allows individual nodes to be added or removed without affecting the entire system, enabling linear scalability while maintaining consistent data capacity and tracking capabilities across the distributed architecture.
Solution Approach 2:
Each storage node is designed as a universal, multi-functional unit that can independently perform storage, tracking, mapping, and validation operations. This universality allows the system to scale by simply adding more identical nodes, as each new node immediately contributes to both storage capacity and the distributed tracking infrastructure, enhancing scalability without requiring specialized components.
Data Source
AI summary
A storage controller is implemented for controlling a storage system. The storage controller may be implemented using a distributed computer system and may include components for servicing client data requests based on the characteristics of the distributed computer system, the client, or the data requests. The storage controller is scalable independently of the storage system it controls. All components of the storage controller, as well as the client, may be virtual or hardware-based instances of a distributed computer system.