CNS Core: Small: Redesigning I/O Across Heterogeneous Systems

Rutgers University New Brunswick, New Brunswick NJ

Investigators

Abstract

The exponential growth of data in recent years has spurred the development of heterogeneous storage and memory devices with varying performance, cost, and capacity. However, traditional monolithic I/O software, such as file systems and key-value stores, face scalability and performance limitations, preventing them from fully utilizing the potential of diverse storage technologies. This project aims to address these issues by designing a parallel, adaptive, and resource-efficient storage framework to leverage the capabilities of heterogeneous storage. The project focuses on three key research thrusts. First, it aims to disaggregate today’s complex monolithic I/O stacks into simpler and fine-grained I/O layers, composing them to meet the demands of applications. This approach provides greater flexibility and adaptability. Secondly, the project explores novel system abstractions to capture I/O data across all system layers, enabling intelligent data placement across heterogeneous storage devices. This optimizes performance and efficiency by placing data where it can be accessed most effectively. Lastly, the project develops novel learning-based techniques for managing performance and quality-centric resource allocation in multi-tenant systems. By revolutionizing I/O acceleration techniques, the project seeks to harness the capabilities of existing and new storage and memory technologies. The fundamental ideas of this project can benefit a wide range of datacenter and HPC applications by reducing I/O latency, increasing throughput, and scaling capacity while maintaining overall system efficiency. Finally, the concepts and prototypes developed will contribute to the education of future generations in the field of hardware-software co-design. The outcomes of this research, which includes research publications, software code, and instructions for reproducing system measurements, will be made publicly available through Rutgers University's website and Github pages (https://github.com/RutgersCSSystems). 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.