Dynamic Memory Pool Tuning via Time-Series Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing memory management systems face challenges in dynamically optimizing memory pool allocation sizes, leading to inefficiencies and increased costs due to improper memory provisioning and manual tuning efforts.
Innovation Solution
A system and method that utilize machine learned models to predict memory allocation requests and cell sizes based on historical time-series data, allowing for dynamic and adaptive provisioning of memory pool cell sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual memory pool tuning is performed, then memory allocation performance can be optimized for specific applications, but significant manual effort and time are required
Solution Approach 1:
The system enables memory pool tuning to perform itself automatically by monitoring application memory usage patterns and self-adjusting pool parameters without human intervention. The memory management system serves itself by collecting performance data, analyzing usage patterns, and dynamically reconfiguring memory pools based on observed behavior
Solution Approach 2:
The system implements continuous feedback loops where memory usage data is collected from applications, analyzed to identify patterns and optimization opportunities, and used to adjust memory pool configurations. This closed-loop approach ensures that tuning decisions are based on actual performance data and usage patterns
2Ease of operation
If a fixed memory pool cell size is provisioned, then memory management is simplified, but the system cannot adapt to varying execution requirements of the same application
Solution Approach 1:
The system transitions from static fixed-size memory pools to dynamic memory pool configurations that can change during runtime. Memory pool cell sizes and allocation parameters are adjusted based on observed application behavior and usage patterns, allowing the system to adapt to varying execution requirements while maintaining ease of operation through automated management
Solution Approach 2:
The system performs preliminary monitoring and analysis of application memory usage patterns before making optimization adjustments. By collecting and analyzing usage data in advance, the system can proactively configure memory pools to match anticipated execution requirements rather than reacting to performance problems
3Reliability
If memory pool size is increased to handle all possible allocation requests, then allocation requests can be satisfied, but wasted memory allocation increases
Solution Approach 1:
The system dynamically changes memory pool parameters including cell size, total pool size, and allocation thresholds based on observed application behavior. By adjusting these parameters to match actual usage patterns rather than using fixed conservative estimates, the system ensures allocation requests are satisfied while minimizing wasted memory
Data Source
AI summary
A system and method for improving the performance and reducing costs of a program by automatically provisioning and managing proper memory pool cell size adaptive to each executing application. By collecting time series of historical data on the memory pool usage of applications over a period of time, respective time-series prediction models are used to process the data to predict the allocation size for applications and in particular, a predicted number of allocations and a respective predicted allocation cell size. A clustering-based method is further applied to predict the allocation size for applications, using real time execution to do scaling, complement and interpolation. A method runs a further time-series prediction model trained to predict, based on the predicted memory cell size and one or more application profile features associated with the requesting application, a tuning parameter to refine the memory pool storage area size used for handling memory allocation requests.