Memory Controller Workload Clustering for NAND Read Performance
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Solution Overview
Problem
NAND flash storage systems face performance degradation due to fragmentation, as they lack sufficient resources to store historical data and perform calculations for merging logical block address ranges into sequential clusters, leading to suboptimal read performance.
Innovation Solution
A memory system with a memory controller that uses a hash table to identify and cluster sequential workload items based on logical block addresses and hit counts, merging them into single items and accepting clusters that appear more than a threshold number of times, thereby improving read performance through defragmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If NAND flash storage stores files as separate fragments (logical block address ranges), then it can accommodate diverse file sizes and access patterns, but read performance deteriorates because every fragment must be read separately instead of performing sequential reads
Solution Approach 1:
The patent merges multiple fragmented logical block address ranges into a single clustered range when they appear sequentially in the workload. The controller identifies fragments that are accessed in sequence and combines them into one contiguous address range, enabling sequential reads instead of multiple separate reads, thus improving read performance while maintaining adaptability to diverse file structures
Solution Approach 2:
The patent performs preliminary clustering of logical block addresses by analyzing the workload sequence in advance. The controller proactively identifies and merges fragments before actual read operations occur, using historical workload patterns to predict and pre-cluster frequently accessed fragments, thereby optimizing read performance for future operations
2Measurement precision
If the storage system stores entire historical command data to enable clustering, then clustering accuracy improves, but the limited storage and processing resources of NAND flash are insufficient
Solution Approach 1:
The patent extracts only the essential information needed for clustering—specifically, the sequence of logical block address ranges from the workload—rather than storing entire historical command data. The controller focuses on capturing the sequential access patterns of LBAs without retaining complete command histories, reducing storage and processing requirements while maintaining sufficient clustering accuracy
Solution Approach 2:
The patent applies partial action by selectively clustering only those logical block address ranges that exhibit sequential access patterns in the workload, rather than attempting to cluster all possible fragments. This approach uses limited resources efficiently by focusing computational effort only on identifiable sequential patterns rather than performing exhaustive clustering on all data
Data Source
AI summary
Memory systems and components thereof perform clustering on workload items. Such a memory system comprises a memory device from which data is read and to which data is written; and a memory controller that receives from a host workload items in a workload sequence, each workload item being defined by at least a start logical block address (LBA) and a length. The memory controller merges sequential workload items in the workload sequence to constitute a single workload item; identifies a start workload item for a candidate cluster; stores the LBA and a hit count of the start workload item in a hash table of the memory controller; identifies an end workload item for the candidate cluster; determines whether the candidate cluster is found in the workload sequence more than a threshold number of times; and, if so, accepts the candidate cluster.