SPX: Collaborative Research: Enabling Efficient Computer Architectural and System Support for Next-Generation Network Function Virtualization

SPX：协作研究：为下一代网络功能虚拟化提供高效的计算机架构和系统支持

• 批准号: 1822989
• 负责人: Tao Li
• 金额: $ 52万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822989&HistoricalAwards=false
• 关键词: SPX; Collaborative; Research; Enabling; Efficient

Network Function Virtualization (NFV) has been widely adopted by telecommunication and internet service providers for greater performance, flexibility, and adaptability, and is treated as the most promising technology for the upcoming fifth generation (5G) wireless networks. However, ensuring that consolidated next-generation NFV workloads can efficiently run on current, commercially available servers and systems while maintaining optimal server/network utilization remains a challenge. The main reason is that existing solutions only serve as layer-specific optimizations. Due to the loose-coupled optimizations across the system and architectural layers, these solutions lack the holistic and synergistic view to guarantee the performance, resilience, and elasticity posed by the features of 5G NFV. This project aims to optimize the efficiency of consolidation of 5G NFV on commercially available server architectures and systems. The contributions of this project are: (1) rethinking the mechanisms employed in various layers of current NFV deployment and optimization, and (2) re-architecting the abstractions between the layers and applications. The impacts of this project will open the door for a new class of efficient scalable computing platforms for next-generation NFV in the 5G era. This project will also contribute to society through engaging under-represented groups, research infrastructure/tools/benchmarks dissemination for education and training, and technology transfer to industries.This project proposes to develop: system-wide profiling tools and an automatic, architectural statistics-aware NFV workloads orchestration and benchmarking framework; new techniques that allow NFV applications to leverage virtualized graphic processing units (GPU), and that improve the scheduling of data movement between GPU and smart network interface cards (NICs); new abstractions that allow NFV applications and building blocks to leverage emerging offloading techniques (e.g. smart NIC and GPU remote direct memory access) and a novel architecture to improve the consolidation efficiency, parallelism, and scalability; and novel algorithms and abstractions for operating systems and accelerators to improve the thread, cache and memory management and cross-layer parallelism.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.

网络功能虚拟化（Network Function Virtualization，NFV）已被电信和互联网服务提供商广泛采用，以获得更高的性能、灵活性和适应性，并被视为即将到来的第五代（5G）无线网络最有前途的技术。然而，确保整合的下一代NFV工作负载可以在当前商用服务器和系统上高效运行，同时保持最佳的服务器/网络利用率仍然是一个挑战。主要原因是现有的解决方案只能作为特定于层的优化。由于系统和架构层之间的松耦合优化，这些解决方案缺乏整体和协同视图，无法保证5G NFV功能所带来的性能、弹性和弹性。该项目旨在优化商用服务器架构和系统上5G NFV的整合效率。该项目的贡献是：（1）重新思考当前NFV部署和优化的各个层中采用的机制，以及（2）重新架构层和应用程序之间的抽象。该项目的影响将为5G时代下一代NFV的新型高效可扩展计算平台打开大门。该项目还将通过让代表性不足的群体参与、为教育和培训传播研究基础设施/工具/基准以及向行业转让技术来为社会做出贡献。新技术，允许NFV应用程序利用虚拟化图形处理单元（GPU），并改进GPU和智能网络接口卡（GPU）之间的数据移动调度;新的抽象，允许NFV应用程序和构建块利用新兴的卸载技术（例如智能NIC和GPU远程直接存储器访问）和新颖的架构以提高整合效率、并行性和可扩展性;以及用于操作系统和加速器的新颖算法和抽象来改进线程，高速缓存和内存管理以及跨该奖项反映了NSF的法定使命，并被认为是值得通过评估使用基金会的智力价值和更广泛的支持影响审查标准。

项目成果

Characterizing and Orchestrating NFV-Ready Servers for Efficient Edge Data Processing

• DOI: 10.1145/3326285.3329057
• 发表时间: 2019-06
• 期刊: 2019 IEEE/ACM 27th International Symposium on Quality of Service (IWQoS)
• 作者: Lu Zhang;Chao Li;Pengyu Wang;Yunxin Liu;Yang Hu;Quan Chen;M. Guo

5G NFV RAN Network Slicing Bench: The 5th-Generation Network Function Virtualization Radio Access Network Slicing Benchmarks

5G NFV RAN网络切片基准：第五代网络功能虚拟化无线接入网切片基准

• 发表时间: 2019
• 期刊: International Conference on Workload Characterization
• 作者: Jianda Wang, Yang Hu