Deep Neural Network Cache for Data Latency Reduction
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Solution Overview
Problem
As data volumes increase, existing systems face latency and infrastructure bottleneck issues when managing large datasets distributed across multiple storage facilities, with traditional caching solutions becoming inefficient due to increased computational resources needed for larger caches.
Innovation Solution
A system utilizing a deep learning neural network with Long Short-Term Memory (LSTM) cells to dynamically update a feature catalog within a cache, allowing for real-time relevance assessment and optimized data retrieval, reducing the need for extensive computational resources by leveraging a multi-dimensional feature attribute matrix and caching strategy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across multiple storage facilities to provide redundancy and spread load, then reliability is improved, but latency and infrastructure bottleneck issues worsen
Solution Approach 1:
The system pre-loads and caches frequently accessed data from distributed storage facilities into local memory before it is actually needed. The neural network analyzes access patterns and proactively retrieves data, so when a request arrives, the data is already available in fast memory, eliminating latency without sacrificing the distributed redundant storage architecture
Solution Approach 2:
A cache memory system acts as an intermediary layer between the distributed storage facilities and the processing units. This intermediate cache holds copies of frequently accessed data, allowing fast retrieval without directly accessing the slow distributed storage, thus resolving the latency issue while maintaining the reliability benefits of distributed storage
2Speed
If a cache is used to reduce latency and infrastructure issues, then speed is improved, but computational resources increase as cache size grows
Solution Approach 1:
The cache system dynamically adjusts its content and size based on real-time access patterns analyzed by the neural network. The cache doesn't maintain a static large memory allocation but instead adaptively loads and evicts data based on predicted access needs, maintaining high speed access for relevant data while minimizing the computational resources required for cache management
Solution Approach 2:
The system changes the parameters of what data to cache and for how long based on neural network predictions of access patterns. By dynamically adjusting cache retention policies and selection criteria based on learned patterns, the system achieves high access speeds for predicted data while reducing the overall computational overhead compared to static caching strategies
3Measurement precision
If deep learning neural network with LSTM cells is used to update feature catalog in real-time, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system extracts only the essential features and patterns needed for cache optimization from the complex neural network computations. Rather than implementing the full deep learning model, the system extracts the relevant predictive capabilities and applies them to feature catalog updates, achieving high measurement precision for data relevance while reducing device complexity by removing unnecessary computational components
Data Source
AI summary
Embodiments of the invention are directed to a system, method, and computer program product for processing data analytics within memory. The system first receives a request for data associated with a database. The system utilizes a feature catalog and a deep learning neural network to determine the data that comprises the one or more desired features. The system can also provide the requested data to a computing device.