Structured State Index for Storage System I/O Optimization
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Solution Overview
Problem
Large storage systems face challenges in optimizing performance due to the complexity of characterizing current operational states and predicting the impact of adjusting storage policies, as raw I/O access data is too sparse and lacks meaningful insight.
Innovation Solution
A method is introduced to create a structured state index from raw I/O traces using machine learning techniques, such as deep neural networks, to represent the state space of a storage system, allowing for the extraction of relevant features and validation against target metrics, enabling predictive modeling and optimization of storage policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If raw I/O access data is used to characterize storage system state, then the data volume is large and detailed, but the data is too sparse and lacks meaningful insight for optimization
Solution Approach 1:
The patent extracts relevant features from raw I/O access data by creating a structured state index that aggregates I/O operations into bins of contiguous memory address ranges. This extraction process transforms sparse raw data into concentrated, meaningful state representations that capture essential storage system characteristics without retaining all raw data details.
Solution Approach 2:
The patent composes a structured state index by aggregating multiple I/O operations into a composite representation. The state index combines information about I/O counts, memory address ranges, and time windows into a unified structure that provides dense, meaningful characterization of storage system state, analogous to creating composite materials with enhanced properties.
2Loss of information
If a detailed state representation is created from raw I/O traces, then more information is available for analysis, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the state representation into discrete bins of contiguous memory address ranges, with each bin containing aggregated I/O operation counts. This segmentation allows the system to process and analyze storage state in manageable units rather than handling complete raw I/O trace data, significantly reducing processing time while preserving essential information about access patterns.
Solution Approach 2:
The patent uses a sliding time window approach that only considers recent I/O operations within a specified time window, rather than processing all historical I/O data. This partial action approach focuses computational resources on the most relevant recent state information, reducing processing time while maintaining sufficient information for effective storage optimization decisions.
3Measurement precision
If machine learning models are trained on raw I/O data, then the models can capture detailed patterns, but the training complexity and data requirements increase significantly
Solution Approach 1:
The patent performs preliminary processing of raw I/O data by creating the structured state index before feeding data to machine learning models. This preliminary action aggregates and structures the data into meaningful features (I/O counts per memory range, temporal patterns) that are ready for model consumption, reducing the complexity of model training while preserving the detailed patterns needed for accurate prediction of storage system behavior.
Data Source
AI summary
A method for optimizing performance of a storage system includes creating a structured state index from a universe of I/O traces of memory access operations in a storage system. The structured state index is validated against a target metric operational parameter of the storage system. If the structured state index has correlation against the target metric operational parameter of the storage system, the structured state index is used as input to a decision-making task. The decision-making task may be implemented as a deep neural network and the structured state index is used as input training data for the deep neural network. Once the decision-making task has been trained using the structured state index, the decision-making task is used in a predictive manner to generate a predicted target metric operational parameter of the storage system given a proposed storage policy.