Storage Controller File System Integration via Association Rule Mining
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Solution Overview
Problem
Conventional storage devices lack the ability to recognize host commands effectively due to a lack of context about the files associated with these commands, leading to suboptimal operations such as delayed read latencies and impaired throughput.
Innovation Solution
A storage device equipped with a controller that utilizes an Association Rule Mining (ARM) model to parse file system data from a host, associate attributes with files, and perform commands based on these associations, optimizing operations like garbage collection, read look-ahead, and precaching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional storage devices operate without file system context, then device complexity is reduced, but read latency increases and throughput is impaired
Solution Approach 1:
The storage device performs preliminary actions by parsing file system data and building an association rule mining model in advance. The controller proactively analyzes file attributes, access patterns, and data relationships before host commands are issued, enabling optimized data retrieval and reduced read latency without significantly increasing operational complexity.
Solution Approach 2:
The association rule mining model acts as an intermediary layer between the host file system and the storage device controller. This model translates file system context into actionable insights about data relationships and access patterns, enabling the controller to make intelligent decisions about data placement and retrieval without requiring complex file system interpretation.
2Productivity
If the storage device parses file system data using ARM model, then throughput is improved, but device complexity increases
Solution Approach 1:
The storage device performs self-service by autonomously parsing file system data and building the ARM model without requiring external intervention. The controller automatically analyzes access patterns and data relationships, making intelligent decisions about data management that improve throughput while keeping the complexity contained within the storage device itself.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based data management approaches with an association rule mining model. This data-driven approach substitutes complex mechanical decision-making processes with statistical patterns and relationships extracted from file system data, improving throughput through intelligent optimization rather than brute-force methods.
3Ease of operation
If the storage device lacks context of host commands, then ease of operation is maintained, but productivity decreases due to suboptimal operations
Solution Approach 1:
The association rule mining model serves as an intermediary that captures file system context without requiring the storage device to understand complex file system operations. The model translates contextual information into simplified rules that guide data management decisions, maintaining ease of operation while significantly improving throughput through optimized data placement and retrieval.
Data Source
AI summary
Aspects of a storage device including a memory and a controller are provided. The controller may collect, by an association rule mining (ARM) model, file system data from a host file system, the file system data defining at least one attribute of a file. The controller may receive, from the host, a memory command associated with the file. The controller can associate, by the ARM model, the at least one attribute with the file. The controller may perform the memory command based on the association of the at least one attribute with the file.