Dynamic Erasure Code Strategy for Edge Storage Encoding
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Solution Overview
Problem
Current distributed storage systems struggle to dynamically meet the varied data protection requirements in edge network environments, where erasure code strategies are statically configured and not easily adaptable, leading to inefficiencies and limitations in resource utilization due to heterogeneous hardware and unstable conditions.
Innovation Solution
An encoding method that determines an erasure code strategy based on user-provided configuration data and health state information of entities, allowing for flexible and dynamic encoding of data, enabling efficient data protection with lower overhead and improved user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static erasure code configuration is used based on fixed databases, then system simplicity is maintained, but adaptability to dynamic edge network conditions deteriorates
Solution Approach 1:
The patent implements dynamic erasure code strategies that automatically adjust encoding parameters based on real-time health state information of storage entities and current workload conditions. The system transitions from static configuration to dynamic adaptation by continuously monitoring entity health states and adjusting erasure code parameters accordingly, enabling the system to respond to changing edge network conditions while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously collecting health state information from storage entities and using this information to adjust erasure code strategies. The feedback loop monitors entity reliability, storage capacity, and computational resources, then feeds this information back to the erasure code manager which adjusts encoding parameters in real-time, enabling adaptive response to dynamic edge network conditions without requiring complex manual reconfiguration.
2Adaptability or versatility
If erasure codes are configured based on logical partition units, then data protection is achieved, but flexibility to meet varied user requirements deteriorates
Solution Approach 1:
The patent applies local quality by allowing different erasure code strategies to be configured for different data types, users, or storage locations within the same system. Instead of applying a uniform erasure code configuration across all logical partitions, the system enables customized encoding parameters (such as different redundancy levels or code types) for specific data sets or user requirements while maintaining data protection reliability through appropriate local optimization.
Solution Approach 2:
The system segments the erasure code configuration into multiple independent strategy profiles that can be selectively applied to different data sets, users, or storage locations. This segmentation allows each segment to have optimized protection parameters tailored to its specific requirements, enabling flexible user-specific configurations while maintaining overall system reliability through diversified protection strategies.
3Productivity
If fixed erasure code strategies are used, then implementation simplicity is maintained, but resource utilization efficiency in heterogeneous edge environments deteriorates
Solution Approach 1:
The patent implements self-service by enabling the erasure code system to automatically select and adjust encoding strategies based on real-time monitoring of entity health states, storage capacity, and computational resources. The system autonomously optimizes resource utilization by dynamically allocating encoding parameters without requiring external intervention or complex manual configuration, thereby improving productivity in heterogeneous edge environments while keeping the operational complexity manageable through automated decision-making.
Solution Approach 2:
The system dynamically changes erasure code parameters (such as redundancy factor, code type, and encoding strength) based on real-time system conditions and entity health states. By adjusting these parameters adaptively rather than using fixed strategies, the system optimizes resource utilization efficiency across heterogeneous edge devices while managing complexity through parameter-based flexibility rather than structural complexity.
Data Source
AI summary
An encoding method includes: receiving configuration data related to encoding with a predetermined encoding mode; determining an encoding strategy based on the configuration data, wherein the encoding strategy includes parameters associated with encoding the data on an entity; and causing the data to be encoded on the entity based on the encoding strategy.