Erasure Code Priority Storage for Energy-Efficient Retrieval
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Solution Overview
Problem
Conventional data storage systems using erasure codes do not consider the complexity and efficiency of different types of erasure codes, leading to sub-optimal results in terms of energy conservation and storage efficiency, as they treat all redundancy equally without accounting for the type of erasure code or its energy requirements.
Innovation Solution
An improved generator matrix and method that prioritize different types of erasure codes based on metrics such as complexity, energy consumption, and likelihood of use, managing the generation, storage, and retrieval of erasure codes differently to optimize storage cost, energy efficiency, and retrieval time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If all erasure codes are stored with equal priority using sequential disk writes, then storage capacity is utilized, but energy consumption increases and retrieval efficiency decreases
Solution Approach 1:
The patent segments erasure codes into different priority categories (first priority, second priority, third priority) based on their type and likelihood of use. This segmentation allows the system to treat different erasure codes differently, storing high-priority codes in energy-efficient locations and low-priority codes in less critical storage areas, thereby reducing overall energy consumption while maintaining retrieval efficiency for important data.
Solution Approach 2:
The patent applies local quality by assigning different storage locations and access priorities to different types of erasure codes based on their specific characteristics. Systematic erasure codes and low-density parity-check codes are stored with higher priority in more energy-efficient locations, while other erasure codes are stored with lower priority. This localized optimization of storage quality reduces energy consumption without compromising overall system functionality.
2Ease of manufacture
If redundant information is grouped together and stored in one location, then storage organization is simplified, but energy conservation opportunities are lost
Solution Approach 1:
Instead of grouping all redundant information together, the patent segments it into different priority groups based on erasure code type. This segmentation enables differentiated storage strategies where high-priority redundant information is stored in energy-efficient locations while low-priority redundant information is stored elsewhere, thus achieving energy conservation without significantly complicating storage organization.
Solution Approach 2:
The patent changes the storage parameters (location, priority level) of redundant information based on the type of erasure code. By varying these parameters according to code characteristics, the system achieves both organized storage and energy conservation, as high-priority redundant data is placed in optimal locations while low-priority data uses less energy-intensive storage.
3Ease of operation
If erasure codes are stored without considering their type, then storage process is simplified, but retrieval complexity increases and energy efficiency decreases
Solution Approach 1:
The patent applies preliminary action by classifying and prioritizing erasure codes during the storage process itself, before retrieval is needed. The system identifies the type of erasure code (systematic, low-density parity-check, or other) and assigns appropriate priority levels and storage locations in advance. This preliminary classification simplifies the retrieval process and improves energy efficiency without adding significant complexity to the storage operation.
Solution Approach 2:
The patent changes storage parameters (priority level, location) based on erasure code type. By modifying these parameters during the storage process, the system achieves energy-efficient storage while maintaining operational simplicity. The parameter changes are automatically applied based on code classification, so the storage process remains straightforward despite the enhanced organization.
4Device complexity
If all encoded symbols are treated equally, then storage management is simplified, but storage cost and energy consumption increase
Solution Approach 1:
The patent segments encoded symbols into different priority categories based on their type and importance. This segmentation allows the system to manage storage resources more efficiently by allocating high-priority symbols to more expensive but energy-efficient storage locations and low-priority symbols to less expensive storage areas, thereby reducing overall storage cost without significantly increasing management complexity.
Solution Approach 2:
The patent applies local quality by assigning different storage qualities and costs to different encoded symbols based on their characteristics. High-priority systematic and low-density parity-check codes are stored with higher quality (and potentially higher cost) in energy-efficient locations, while other codes use lower-cost storage. This localized differentiation reduces total storage cost while maintaining necessary data protection.
Data Source
AI summary
Example apparatus and methods selectively generate and store erasure codes differently based on priorities associated with the erasure codes or based on conditions in a data storage system (DSS) that protects messages using erasure codes. Producing a systematic erasure code (EC) may be prioritized over producing a non-systematic EC. Producing an EC associated with correcting X erasures may be prioritized over producing an EC associated with correcting Y erasures, X and Y being numbers, X<Y. The priorities may depend on conditions in the DSS including an erasure code A/B policy, numbers of errors experienced by the DSS, types of errors experienced by the DSS, frequency of errors, an amount of power required to store or retrieve an EC in the DSS, or a network bandwidth required to store or retrieve an EC in the DSS. The priorities may be user configurable or self-adapting.