Collaborative Research: CNS Core: Medium: Movement of Computation and Data in Splitkernel-disaggregated, Data-intensive Systems

资源分解有望改变我们设计和运营云基础设施的方式。但是，尽管分解对数据中心的运营和成本效益有积极影响，但每次访问远程内存、存储或加速器时引入PCIe和网络往返带来的开销--对于数据密集型应用尤其明显。这项提议将回答的核心研究问题是，分解的出现对数据密集型系统设计者几十年来所做的体系结构决策意味着什么，以及同样地，需要什么操作系统级原语来使这些系统适应分散的环境。为此，这项工作提出了一种针对数据密集型系统的分布式操作系统和中间件的联合设计，该系统实现了高性能和易用性，数据密集型系统就像这项工作所针对的系统一样，为当今世界的大部分地区提供动力，提供从商业信息学到互联网搜索和医疗保健的一切分析。拟议的工作将帮助数据中心运营商过渡其基础设施，以支持资源分解，同时将公共框架中编写的应用程序的性能开销降至最低。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Resource disaggregation promises to change the way in which we design and operate cloud infrastructure. But despite its positive impact on the operation and cost-efficiency of data centers, disaggregation comes with overheads that arise from introducing a PCIe and network round trip for each access to remote memory, storage, or accelerators -- overheads that are particularly pronounced for data-intensive applications. The core research question that this proposal will answer is what the advent of disaggregation implies for the decades of architectural decisions made by the designers of data-intensive systems, and likewise, what OS-level primitives are required to adapt these systems to a disaggregated setting. To that end, this work proposes a co-design of disaggregated operating systems and middleware for data-intensive systems that achieves both high performance and ease of use, Data-intensive systems like the ones targeted by this work power much of today’s world, providing analytics for everything from business informatics to Internet search and healthcare. The proposed work will assist data center operators in transitioning their infrastructure to support resource disaggregation while minimizing performance overheads to applications written in common frameworks.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.