CAREER: Machine Learning Driven Cross-Layer Optimizations for Storage

点击翻译按钮获取中文摘要

Recent advancements in computer science have enabled an exponential data growth outpacing technology scaling. As an increasing number of mobile devices, sensors and data acquisition systems is producing exabytes of information, analyzing the obtained data are becoming unfeasible, requiring novel approaches to store and compute on this vast amount of data. Improving the performance and efficiency of storage systems is of paramount importance to enable scientific progress, to improve the cost and energy consumption of IT systems and to enable analysis of large amounts of data. To achieve this goal, as part of this grant, novel approaches on the hardware, operating systems, data-center and application level will be developed. Enabling such new techniques will provide significant benefit for society. First, the new approaches developed in this project will improve efficiency and utilization of storage systems reducing the carbon footprint on our world. Secondly, improving the performance of storage devices enables novel applications such as new treatments leveraging storage and compute intensive genomics.As part of this project, cross layer optimizations will be developed to improve the hardware and software stack of storage systems using machine learning techniques. One main challenge of existing block storage devices is their transparency of internal state to software. This work addresses this shortcoming by extending storage devices with comprehensive data monitoring capabilities as well as with control knobs to optimize devices in an application specific way. The telemetry data obtained from these smart storage devices will be utilized to train machine learning models to optimize for application specific behavior as well as for determining optimal configurations of heterogeneous storage and compute environments.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.