Abstract

With the advent of social networking and cloud data-stores, user data is increasingly being stored in large capacity and high performance storage systems, which account for a significant portion of the total cost of ownership of a datacenter (DC) [3]. One of the main challenges when trying to evaluate storage system options is the difficulty in replaying the entire application in all possible system configurations. Furthermore, code and datasets of DC applications are rarely available to storage system designers. This makes the development of a representative model that captures key aspects of the workload’s storage profile, even more appealing. Once such a model is available, the next step is to create a tool that convincingly reproduces the application’s storage behavior via a synthetic I/O access pattern.