Database Memory Prediction for Dynamic Allocation Stability
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Solution Overview
Problem
In-memory databases face fluctuating memory demands due to varying data storage and application requests, leading to potential performance degradation and crashes if available memory does not meet these demands, resulting in inefficient resource consumption and data loss.
Innovation Solution
Implementing a machine learning-based memory usage prediction system using a Long Short-Term Memory (LSTM) neural network to forecast memory requirements, allowing proactive adjustment of memory allocation to prevent crashes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If memory allocation is increased to meet fluctuating demands, then database reliability is improved, but resource efficiency deteriorates
Solution Approach 1:
The patent implements dynamic memory allocation by using a machine learning model to predict future memory usage and automatically adjusting memory allocation in real-time. This replaces static memory allocation with a dynamic system that adapts to changing application loads, ensuring reliability when needed while avoiding waste during low-demand periods
Solution Approach 2:
The system performs preliminary action by predicting future memory usage before actual memory shortages occur. The machine learning model analyzes historical memory usage patterns and forecasts future demands, allowing the system to proactively allocate memory before performance degradation or crashes occur, rather than reactively responding to memory shortages
2Loss of energy
If memory allocation is decreased to improve resource efficiency, then resource efficiency is improved, but database reliability deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously monitors actual memory usage, compares it with predictions, and uses this feedback to refine future predictions and adjust memory allocation. This closed-loop system ensures that memory allocation remains optimized while maintaining reliability, as the system learns from actual performance data and adapts its predictions accordingly
3Device complexity
If static memory configuration is used, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system implements self-service by enabling the database to automatically monitor its own memory usage, predict future needs, and adjust its own memory allocation without external intervention. The machine learning model runs autonomously, continuously learning from historical data and making real-time allocation decisions, eliminating the need for manual configuration while maintaining high adaptability
Data Source
AI summary
Methods, systems, and computer-readable storage media for machine learning database memory use prediction and adaptation. An example method includes determining a sampling interval for an application for sampling memory use by a database for the application. A plurality of historical memory use samples of amounts of memory used by the database are determined for the application based on the sampling interval. The plurality of historical memory use samples are provided for training of a machine learning model to predict memory use for the application by the database for a future time period. A set of current memory use samples are provided to the machine learning model and a memory use prediction for the application for an upcoming time period is received from the machine learning model. A determination is made as to whether to extend memory of the database for the application based on the memory use prediction.