Distributed Storage Nodes with Embedded CPUs for Data Availability
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Solution Overview
Problem
Existing storage systems face challenges in efficiently managing data storage and retrieval across multiple storage nodes, particularly in ensuring data redundancy, availability, and performance in distributed storage environments.
Innovation Solution
The proposed solution involves a distributed storage system architecture that utilizes non-volatile solid state storage units with embedded CPUs and flash memory, implementing erasure coding and redundancy schemes to ensure data availability across multiple storage nodes. Additionally, the system employs a cloud-based storage approach to provide scalable and resilient data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored across multiple storage nodes in a distributed system, then data availability and redundancy are improved, but system complexity increases
Solution Approach 1:
The patent divides the storage system into multiple independent storage nodes, each capable of storing portions of data. Data is segmented into chunks and distributed across these nodes, with each node managing its own local storage independently. This segmentation improves reliability through distribution while managing complexity by making each node self-contained and manageable.
Solution Approach 2:
The patent introduces a coordination layer or metadata service that acts as an intermediary between clients and storage nodes. This intermediary manages the complexity of distributed data retrieval by tracking data locations, handling node failures, and coordinating recovery operations, thereby shielding clients from system complexity while maintaining high availability.
2Reliability
If erasure coding and redundancy schemes are implemented, then data redundancy and fault tolerance are improved, but storage efficiency decreases
Solution Approach 1:
The patent implements erasure coding schemes where data is transformed into encoded chunks with redundant information. By adjusting the coding parameters (such as the ratio of data chunks to parity chunks), the system can optimize between fault tolerance and storage efficiency based on specific requirements. This allows flexible parameter tuning to balance reliability and storage capacity.
Solution Approach 2:
The patent applies redundancy selectively rather than uniformly across all data. Different data types or importance levels can receive different degrees of redundancy protection. Additionally, the system can adjust the level of erasure coding applied based on available storage capacity, implementing partial redundancy where full fault tolerance is not required for all data sets.
3Speed
If non-volatile solid state storage units with embedded CPUs are used, then data retrieval performance is improved, but device complexity and cost increase
Solution Approach 1:
The patent equips each storage node with an embedded CPU that independently manages local storage operations. This segmentation of processing responsibilities allows each node to handle data retrieval, encoding, and error correction locally without constant central coordination, improving retrieval performance while distributing computational complexity across manageable units.
Solution Approach 2:
The embedded CPUs in storage nodes enable self-service capabilities where each node can independently perform data retrieval, validate data integrity, execute erasure coding operations, and manage local cache memory without requiring constant intervention from central controllers. This autonomy improves retrieval speed by eliminating coordination bottlenecks while keeping each node's complexity self-contained and manageable.
Data Source
AI summary
An illustrative method includes a storage management system receiving, from a containerized application, a storage operation request associated with a storage volume in a storage system, the storage system including a plurality of volume instances of the storage volume that correspond to a plurality of data management models, identifying, from the plurality of volume instances, one or more particular volume instances based on the storage operation request and a data management model of each volume instance in the plurality of volume instances, and directing the storage operation request to the one or more particular volume instances to be performed on the one or more particular volume instances.