ANN-Based Storage QoS Control for Dynamic Caching and Maintenance
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Solution Overview
Problem
Autonomous vehicle data storage devices face challenges in maintaining consistent quality of service due to varying workload patterns and operational conditions, affecting latency and processing efficiency.
Innovation Solution
Implementing an Artificial Neural Network (ANN) within the data storage device to predict and optimize caching and background maintenance processes, such as garbage collection and wear leveling, based on real-time sensor data and operating conditions, thereby enhancing the quality of service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fixed-parameter caching and maintenance operations are used in data storage devices, then device complexity is reduced, but quality of service consistency deteriorates under varying workload patterns
Solution Approach 1:
The storage device employs an Artificial Neural Network that operates autonomously within the device to predict optimal caching parameters and maintenance timing. The ANN analyzes workload patterns and sensor data to self-determine cache size, garbage collection timing, and wear leveling frequency without external intervention, enabling the system to adapt to varying workloads while maintaining QoS consistency
Solution Approach 2:
The patent dynamically adjusts multiple operational parameters including cache size, garbage collection timing, and wear leveling frequency based on predictions from the ANN. These parameter changes are made in response to detected workload patterns and sensor data, allowing the storage device to optimize performance across different operating conditions while maintaining consistent quality of service
2Reliability
If dynamic parameter adjustment based on sensor data is implemented, then quality of service is improved, but processing time and computational overhead increase
Solution Approach 1:
The Artificial Neural Network continuously monitors workload patterns and sensor data to predict future storage operations and performance requirements. By performing predictions in advance based on detected patterns, the system can proactively adjust caching and maintenance parameters before performance degradation occurs, reducing reactive processing delays
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the ANN continuously receives sensor data and performance metrics, adjusts parameters accordingly, and monitors the effects. This feedback loop enables the storage device to adapt to changing workload patterns in real-time, maintaining optimal performance while minimizing processing overhead through learned patterns
3Productivity
If larger cache sizes are used to improve read/write performance, then processing speed increases, but memory resource consumption increases
Solution Approach 1:
The patent implements dynamic cache size adjustment based on workload patterns detected by the Artificial Neural Network. The cache size is not fixed but adapts in real-time to match actual storage demands, expanding when performance is critical and contracting when resources are needed elsewhere, thereby optimizing the balance between processing speed and memory resource consumption
Data Source
AI summary
Systems, methods and apparatuses to control quality of service of a data storage device. For example, the data storage device receives an input data stream and provides an output data stream. Based at least in part on the input data stream and/or the output data stream, the data storage device determines a quality of service configuration using an artificial neural network. A controller of the data storage device uses the quality of service configuration to control operations of the data storage device that are relevant to quality of service of the data storage device. For example, the configuration identifies optimized strategies and parameters of caching or buffering, and optimized timing and frequency of background maintenance processes, such as garbage collection, wear leveling, etc.