Random Read Performance Modeling Under Host and Storage Constraints
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Solution Overview
Problem
Existing methods for predicting random read performance in array-based storage devices fail to accurately account for the impact of host system and storage device resource constraints, leading to overly optimistic estimates.
Innovation Solution
A predictive model that incorporates the capabilities of storage media and system elements such as storage array utilization, communication channel contention, parallelism features, storage device firmware overhead, and host system overhead to improve the accuracy of random read performance estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional modeling methods are used to predict random read performance, then the prediction process is simple, but the prediction accuracy is low due to ignoring resource constraints
Solution Approach 1:
The predictive model is segmented into multiple independent modules: host system module (with host queue depth, host overhead), storage device module (with device queue depth, device overhead, firmware overhead), and storage array module (with die count, die utilization). Each module processes specific resource constraints separately, then their effects are combined to produce the final prediction. This segmentation allows the complex model to be built from manageable components while maintaining high prediction accuracy.
2Measurement precision
If resource constraints are incorporated into the predictive model, then prediction accuracy improves, but the computational complexity increases
Solution Approach 1:
The model performs preliminary calculations by pre-determining key parameters such as host queue depth, device queue depth, die utilization, and overhead factors before the actual prediction. These preliminary values are then reused in the main prediction formula, avoiding redundant computations. This approach allows the model to incorporate comprehensive resource constraints while minimizing the computational time required for each prediction.
3Reliability
If detailed system constraints are modeled, then the prediction reflects real-world performance better, but the model requires more input parameters
Solution Approach 1:
The predictive model is designed as a universal framework that can accommodate multiple types of resource constraints through a standardized structure. The same basic formula (Equation 1) handles different constraint types by substituting appropriate parameters: host queue depth for host limitations, device queue depth for device limitations, die utilization for array limitations, and various overhead factors for performance penalties. This multi-functionality allows the model to reliably predict performance across diverse system configurations without requiring fundamentally different approaches for each constraint type.
Data Source
AI summary
A computing system having a storage system that includes a storage device and a host device, where the host device is configured to issue memory access commands to the storage device. The computing system further includes a prediction system comprising processing circuitry that is configured to perform operations that cause the prediction system to identify one or more components of the storage system that limit random read performance of the storage system. The operations further cause the prediction system to obtain characterization data that is indicative of the impact of the one or more components on random read performance and generate a model based on the characterization data to predict random read performance of the storage system. The operations additionally cause the prediction system to execute the model in a simulation of the storage system to generate a random read performance parameter for the storage system.