Currently, there is a rapidly growing diversity in data processing workloads. Likewise, new advancements in persistent storage technologies are emerging. Therefore, it is important to have new techniques for benchmarking and appropriately configuring storage systems in order to obtain the best possible performance and reliability. This project proposes to derive new input/output (I/O) models to capture I/O behaviors accurately when running multiple applications with different workloads on storage systems such as flash-based solid-state drives (SSDs). In addition, this project develops new approaches to identify the most appropriate internal algorithm for different types of persistent storage devices and dynamically adjust the associated algorithm parameters according to I/O activities.This project makes empirical contributions to storage systems by addressing challenges issued by large-scale data-intensive applications. Specifically, it advances (1) how to analyze the impact of various system components while running multiple workloads on emerging storage systems; (2) how to design interactive frameworks that allow users to modify the internal algorithms and parameters of modern storage devices; (3) how to enable novices to configure storage systems with respect to their workloads and data processing requirements; and (4) how to derive I/O models to predict future I/O workload patterns and accordingly configure storage systems in advance for better performance.This project will lead to better storage systems design with high performance and reliability. The outcome of this project will bring a significant impact on many areas that are dependent on processing a large amount of data. This project will share the findings with undergraduate and graduate students through computer science and engineering programs and open up career opportunities to female students, underrepresented minorities, and first-generation college students. This project will disseminate the proposed techniques into the industry and foster technology transfer through new industrial collaborations. The developed infrastructure will be available to the research community through a web-based portal.All the publicly disclosable NSF funded work products developed under this project will be maintained at the project website (https://damrl.cis.fiu.edu/research/) at Florida International University (FIU) for at least five years beyond the end of the project. Data generated and collected as part of this project will be deposited into Digital Repository Service (DRS) (https://repository.library.northeastern.edu/) at Northeastern University (NEU) and maintained for at least 5 years beyond the end of the project. The developed software code and tools will be published in scholarly articles and be made available online via NEU's DRS, and FIU's project website.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.