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

近年来，数据的指数增长已经刺激了具有不同性能、成本和容量的异构存储和存储器设备的发展。然而，传统的单片I/O软件，如文件系统和键值存储，面临着可扩展性和性能限制，使其无法充分利用各种存储技术的潜力。该项目旨在通过设计一个并行的、自适应的、资源高效的存储框架来利用异构存储的能力来解决这些问题。首先，它旨在将当今复杂的单片I/O堆栈分解为更简单和细粒度的I/O层，并将它们组合起来以满足应用程序的需求。这种方法提供了更大的灵活性和适应性。其次，该项目探索了新颖的系统抽象来捕获所有系统层的I/O数据，从而实现跨异构存储设备的智能数据放置。这通过将数据放置在可以最有效地访问的位置来优化性能和效率。最后，该项目开发了新的基于学习的技术，用于管理多租户系统中以性能和质量为中心的资源分配。通过革新I/O加速技术，该项目寻求利用现有和新的存储和内存技术的能力。该项目的基本思想可以通过减少I/O延迟、提高吞吐量和扩展容量，同时保持整体系统效率，使各种数据中心和HPC应用程序受益。最后，开发的概念和原型将有助于在硬件-软件协同设计领域教育后代。这项研究的成果，包括研究出版物，软件代码和再现系统测量的说明，将通过罗格斯大学的网站和Github页面公开。（网址：//github.com/RutgersCSSystems)该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.