Java Off-Heap Memory Management via Time Series Forecasting
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Solution Overview
Problem
Current management of off-heap memory by operating systems fails to meet high requirements in real-use scenarios, leading to inefficiencies such as fixed module sizes, isolated memory allocation, and lack of dynamic scaling.
Innovation Solution
Implementing a time series model training method that uses historical memory sequences to forecast future memory use, allowing for elastic scaling and improved memory management efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If off-heap memory is managed by operating system with fixed module sizes, then memory allocation is simple, but memory management efficiency is low and cannot meet high requirements in real use scenarios
Solution Approach 1:
The patent implements dynamic memory scaling by using time series models to forecast future memory usage and automatically adjusting memory allocation accordingly. This transitions from static fixed-size memory modules to dynamic memory management that adapts to changing workloads, resolving the contradiction between simple allocation and efficient management.
Solution Approach 2:
The system performs self-managed memory scaling through automated forecasting and adjustment mechanisms. The time series model continuously monitors memory usage patterns and autonomously determines optimal memory allocation without requiring complex manual configuration or external control, improving efficiency while managing complexity internally.
2Adaptability or versatility
If fixed module sizes are used for memory allocation, then device complexity is reduced, but adaptability to varying memory requirements is poor
Solution Approach 1:
The patent replaces fixed module sizes with dynamic memory allocation that automatically adjusts based on forecasted memory usage patterns. The time series model enables the system to adapt to varying memory requirements by predicting future usage and proactively adjusting capacity, achieving high adaptability through automated decision-making.
Solution Approach 2:
The system performs preliminary memory capacity adjustment by forecasting future memory usage before actual demand occurs. This proactive approach allows the system to prepare appropriate memory allocation in advance, improving adaptability to varying requirements while managing complexity through automated prediction rather than reactive adjustment.
3Productivity
If elastic scaling is implemented through time series forecasting, then memory resource utilization is improved, but computational complexity increases
Solution Approach 1:
The patent replaces complex manual memory management mechanisms with automated time series forecasting models. By substituting mechanical/algorithmic forecasting approaches with statistical/time-series-based prediction, the system achieves improved resource utilization through automated, data-driven decision-making that handles complexity internally.
Solution Approach 2:
The system implements continuous feedback loops where memory usage data is collected, processed through time series models, and used to adjust memory allocation. This feedback mechanism improves resource utilization by continuously optimizing memory management based on actual usage patterns, while the automated nature of the feedback loop manages computational complexity.
4Loss of time
If proactive memory adjustment is performed, then temporary memory allocation overheads are minimized, but measurement and detection complexity increases
Solution Approach 1:
The patent performs preliminary memory capacity adjustment by forecasting future memory usage before actual demand occurs. This proactive approach minimizes temporary allocation overheads by avoiding reactive memory adjustments. The time series model continuously monitors memory usage patterns, enabling the system to predict and prepare appropriate memory allocation in advance, reducing time loss while managing detection complexity through automated pattern recognition.
Data Source
AI summary
A time series model training method, includes: obtaining a historical memory sequence collected by using a memory management module, wherein the historical memory sequence includes a plurality of memory use amounts arranged in a time sequence, each memory use amount is a maximum memory use amount in a corresponding time period, and the maximum memory use amount is obtained through collection for an off-heap memory of a Java virtual machine; and training a time series model based on the historical memory sequence, to obtain the trained time series model to forecast a memory use amount in a future time period.