Adaptive File System Parameter Tuning via Machine Learning
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Solution Overview
Problem
Existing clustered file systems face inefficiencies in optimizing parallelized file system operations due to static parameterization, leading to suboptimal performance in computing environments.
Innovation Solution
A computer-implemented method that uses machine learning to dynamically adjust parameters of file system operations based on performance metrics, enhancing the parameterization to improve processing efficiency within the computing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static parameterization is used for parallelized file system operations, then system simplicity is maintained, but performance optimization is limited
Solution Approach 1:
The patent implements dynamic parameterization by using machine learning models to continuously adjust file system operation parameters based on real-time performance metrics. The system transitions from static configuration to adaptive tuning, where parameters such as parallel operation counts, thread priorities, and I/O buffer sizes are dynamically modified to optimize performance under varying workloads and system conditions.
Solution Approach 2:
The patent establishes a feedback loop where performance metrics from parallelized file system operations are continuously monitored and fed back to the machine learning model. This feedback mechanism enables the system to learn from past performance and automatically adjust parameters to improve future operations, resolving the contradiction by making the system adaptive rather than static.
2Productivity
If machine learning is used to dynamically adjust parameters, then performance is improved, but computational overhead increases
Solution Approach 1:
The patent applies partial action by selectively tuning only the most impactful parameters rather than optimizing all possible parameters. The machine learning model identifies and adjusts key parameters that have the greatest influence on performance, reducing unnecessary computational overhead while maintaining efficiency improvements.
Solution Approach 2:
The patent focuses on changing specific critical parameters (such as parallel operation counts, thread priorities, and I/O buffer sizes) rather than all system parameters. This selective parameter adjustment reduces the computational burden of machine learning while still achieving significant performance improvements in file system operations.
3Adaptability or versatility
If adaptive parameterization is implemented, then performance optimization is achieved, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary layer between the file system operations and the underlying hardware resources. This intermediary automatically handles the complexity of adaptive parameterization, translating high-level performance goals into specific parameter adjustments without requiring direct complex control logic in the file system itself.
Solution Approach 2:
The patent implements self-service by enabling the file system to automatically tune its own parameters using machine learning. The system monitors its own performance metrics and autonomously adjusts parameters without external intervention, reducing the need for complex external control mechanisms while achieving adaptability.
Data Source
AI summary
Processing within a computing environment is facilitated by obtaining a parameterization for a parallelized file system operation of a file system of the computing environment, and executing, and determining performance of, the parallelized file system operation with the parameterization. The process further includes using machine learning to adjust one or more parameters of the parameterization based on performance of the parallelized file system operation to obtain a tuned parameterization, and executing the parallelized file system operation with the tuned parameterization, where the adjusting of one or more parameters of the parameterization enhances performance of the parallelized file system operation within the computing environment.