Modelling the performance of an SSD-Aware storage system using least squares regression

Flash memory has lately been used as a cache located between the system main memory and the magnetic hard drives in order to create robust and cost effective hybrid storage systems. The reason comes from the growing density of Solid State Devices (SSDs) at lower prices with main advantage of high random read efficiency compared to magnetic hard drives. When predicting the performance of such hybrid storage systems, it is inevitable to study the trade-off in selecting the different storage elements such as the main memory and the SSD cache relative to the capacity of the magnetic hard drive. The parameters of such prediction model are determined based on the application that the storage system would serve. In this paper, a prediction model that uses experimental evaluation of a hybrid storage system and real applications/benchmarks is used. The model utilizes parameters of both the storage system and applications in order to predict system performance based on metrics that are commonly used in storage system evaluation. The model allows the designer to select the best hybrid storage system parameters that satisfy certain application performance requirements. The model is highly accurate with a minimal error and a high prediction confidence level (95%) in reference to the experimental data collected from real applications using the proposed SSD-aware hybrid storage system.