Distributed File System Latency Reduction via ML Path Tuning
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Solution Overview
Problem
Conventional distributed file systems face inefficiencies in optimization due to varying factors such as intended use, file types, and memory types, leading to tedious tuning processes that fail to maintain optimal performance.
Innovation Solution
A system utilizing machine learning to determine optimal settings for distributed file systems by analyzing historical metrics, predicting ranks for different settings, and applying those settings to reduce latency or increase throughput, while also updating settings for future improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional distributed file systems use manual tuning methods, then optimization can be achieved, but the tuning process becomes tedious and highly inefficient
Solution Approach 1:
The system employs machine learning models that automatically analyze workload characteristics and file system metrics to self-determine optimal tuning parameters without human intervention. The ML models continuously learn from historical data and automatically adjust settings, enabling the system to serve itself rather than requiring manual tuning expertise.
Solution Approach 2:
The patent replaces manual mechanical tuning processes with machine learning-based automated systems. Instead of human operators manually adjusting parameters based on experience, ML algorithms automatically analyze system state and determine optimal settings, substituting human cognitive processes with computational intelligence.
2Manufacturing precision
If distributed file systems apply path-level settings, then performance optimization becomes more precise, but the complexity of managing multiple settings increases
Solution Approach 1:
The system applies different optimal settings to different paths within the file system based on their specific workload characteristics. Each path receives customized tuning parameters determined by ML analysis of its unique access patterns, file types, and performance requirements, rather than applying uniform settings system-wide.
Solution Approach 2:
The patent dynamically changes multiple tuning parameters simultaneously based on ML predictions, including but not limited to I/O scheduling policies, cache sizes, and buffer allocations. The system adjusts these parameters automatically based on real-time workload analysis, transforming static configuration into dynamic adaptive tuning.
3Productivity
If machine learning models analyze all historical metrics to determine optimal settings, then performance optimization improves, but the computational resources required increase
Solution Approach 1:
The system extracts and focuses only on the most relevant historical metrics and features that have the greatest impact on performance prediction. Rather than analyzing all available data equally, the ML models identify and prioritize key indicators such as I/O patterns, access frequencies, and workload types, filtering out less significant data to reduce computational overhead.
Solution Approach 2:
The patent implements a tiered analysis approach where the system performs partial analysis on all paths and excessive (detailed) analysis only on high-priority paths that significantly impact overall performance. This selective deep-dive approach ensures optimal settings are determined for critical paths while maintaining reasonable resource consumption across the entire system.
Data Source
AI summary
A non-transitory machine-readable medium can comprise executable instructions that, when executed by a processor, facilitate performance of operations, comprising: determining a workflow comprising a group of files of a distributed file system, determining a type of the workflow, determining a group of historical metrics associated with the type of the workflow, using a model generated based on machine learning applied to the group of historical metrics, determining respective predicted ranks for different settings that are able to be applied to the workflow, based on the respective predicted ranks, determining a setting, of the different settings, to use to apply to the workflow and that has a predicted rank of the respective predicted ranks that satisfies a defined criterion, and applying the setting to at least one of the group of files to decrease a latency associated with processing the workflow.