Non-volatile Memory Cluster Association Recognition
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Solution Overview
Problem
Non-volatile memory systems fail to store data sets in optimal locations and recognize associations between them, leading to suboptimal read performance, especially for data sets associated with common application files or fragmented due to drive wear, which results in slower data retrieval.
Innovation Solution
A non-volatile memory system comprising a filter module, address management module, and sequencer module that identifies cluster sequences of data sets, rearranges their storage to maximize read parallelism, and instructs the memory to sense associated data sets upon a host read request, using a cluster association recognition system and address data structure to optimize data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data sets are stored in non-volatile memory without association recognition, then storage simplicity is maintained, but read performance deteriorates due to suboptimal storage locations and inability to recognize associated data sets
Solution Approach 1:
The system performs preliminary actions by analyzing host read requests and predicting associated data sets before actual read operations occur. The address management module proactively identifies cluster sequences and pre-determines optimal storage locations for associated data, enabling the memory system to prepare multiple data sets in advance for simultaneous retrieval, thereby improving read performance without adding significant complexity during actual read operations.
Solution Approach 2:
The non-volatile memory system performs self-service by autonomously analyzing read requests, identifying data associations, and optimizing storage locations without requiring external intervention from the host system. The address management module and cluster association recognition system work independently to monitor read patterns, recognize associated data sets, and rearrange storage allocations to maximize read parallelism, thereby improving performance while maintaining host system simplicity.
2Speed
If data sets are treated as individual unassociated requests, then request processing simplicity is maintained, but data retrieval speed deteriorates due to lack of parallelism
Solution Approach 1:
The system performs preliminary analysis of read requests to identify cluster sequences and associated data sets before actual retrieval occurs. The address management module proactively determines which data sets should be retrieved simultaneously based on predicted host requests, enabling parallel data retrieval operations to be prepared in advance, thereby increasing data retrieval speed without adding significant complexity during the actual read operation.
Solution Approach 2:
The system merges multiple individual read requests into unified cluster sequences by recognizing associations between data sets. The cluster association recognition system combines related data retrieval operations into parallel batches, allowing the memory system to retrieve multiple data sets simultaneously rather than sequentially, thereby significantly improving data retrieval speed while managing request complexity through intelligent grouping.
3Productivity
If data sets are stored at different times without optimization, then storage flexibility is maintained, but read performance deteriorates due to fragmented storage locations
Solution Approach 1:
The system introduces dynamic storage location optimization by continuously monitoring host read requests and adapting data set placement in real-time. The address management module dynamically rearranges storage allocations based on observed read patterns and predicted associations, allowing the system to maintain storage flexibility for new data while optimizing existing data locations for improved read performance through adaptive cluster sequence recognition.
Solution Approach 2:
The system implements feedback mechanisms where the address management module analyzes actual host read requests and uses this information to refine future storage decisions. By monitoring which data sets are read together and adjusting storage locations accordingly, the system creates a feedback loop that continuously improves read performance while maintaining the ability to store data at various locations, balancing optimization with storage flexibility.
Data Source
AI summary
A cluster association recognition system and related method are described. The system may identify sequences of data clusters in compilations of cluster journals. The system may generate the compilations by populating the cluster journals with cluster identifications associated with host addresses identified in host read requests. Upon receipt of future read requests, the cluster sequences may be used to identify data sets that are associated with a cluster sequence in order to identify further data sets that a host is likely to request.