SHF: Small: A Versatile, Adaptive Network Architecture for Combating Traffic Heterogeneity in Data Centers

SHF：小型：用于应对数据中心流量异构性的多功能自适应网络架构

• 批准号: 1717731
• 负责人: Yuanyuan Yang
• 金额: $ 45万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-15 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717731&HistoricalAwards=false
• 关键词: SHF; Small; Versatile; Adaptive; Network

Driven by technology advances, massive data centers consisting of tens or even hundreds of thousands servers have been built as infrastructures by large online service providers, in which the performance of data center networks plays a critical role. Traffic in data center networks (DCNs) generally exhibits tremendous heterogeneous characteristics, at different locations in the network, among various applications, between distinct companyís implementations, and from time to time. On the other hand, today's data center networks still lack sufficient flexibility to handle such huge heterogeneity of traffic in an efficient way. Meanwhile, as virtualization techniques undergo a profound development, cloud applications do not directly run on physical hardware or operating systems. Instead, they are assigned on a simulated operating system layer which is virtualized from a substrate network. Thus, a single infrastructure provider can support numerous virtual machines/networks for different users while keeping each user with a vision that the entire infrastructure is uniquely occupied by the user. To meet the challenges, this research designs a versatile network architecture oriented to the ever-increasing performance demands and heterogeneous traffic characteristics of today's data centers. The architecture provides an adaptive full-stack paradigm which includes interconnect topologies, addressing and routing schemes, and virtualization algorithms. More specifically, the research focuses on several closely coupled issues: (1) design a novel, cost-efficient DCN architecture that can dynamically and systematically handle traffic heterogeneity in data centers; (2) design the corresponding routing algorithms for the architecture under unicast and multicast traffic models; (3) develop efficient network virtualization algorithms in the architecture, including virtual network embedding, virtual network function placement and virtualization over geographically distributed DCNs; (4) conduct a comprehensive performance evaluation through extensive simulations and implementation of the schemes in a realistic network prototype. The research combines theoretical analysis, algorithm design, network optimization, simulation and prototyping techniques to provide a comprehensive working solution that enables high performance next generation DCNs. This research is expected have a profound impact on fundamental design principles of future DCNs. The outcome of this research will not only greatly boost the performance of DCN flexibility and energy efficiency while keeping low cost, but also facilitate numerous cloud computing applications currently hosted in data centers. As cloud computing is penetrating into all aspects of the society, this research will have a broader impact on the society and help change the world.

在技​​术进步的驱动下，大型数据中心由大型在线服务提供商建造为基础架构的数十甚至数十万服务器，其中数据中心网络的性能起着至关重要的作用。数据中心网络中的流量（DCN）通常在网络中的不同位置，各种应用，不同的公司实施之间和时不时表现出巨大的异质特征。另一方面，当今的数据中心网络仍然缺乏足够的灵活性，无法有效地处理如此巨大的流量。同时，由于在深刻开发下的虚拟化技术时，云应用程序不会直接在物理硬件或操作系统上运行。取而代之的是，它们是在模拟操作系统层上分配的，该操作系统层是从基板网络虚拟化的。这是一个单个基础架构提供商可以为不同用户支持众多虚拟机/网络，同时使每个用户都以用户独特地占用整个基础架构的愿景。为了应对挑战，这项研究设计了一种通用的网络体系结构，该网络体系结构面向当今数据中心的不断增长的性能需求和异质交通特征。该体系结构提供了一种自适应的全栈范式，其中包括互连拓扑，寻址和路由方案以及虚拟化算法。更具体地说，研究重点是几个紧密耦合的问题：（1）设计一种新颖的，具有成本效益的DCN体系结构，可以动态和系统地处理数据中心的流量异质性； （2）在单播和多播流量模型下设计架构的相应路由算法； （3）在体系结构中开发有效的网络虚拟化算法，包括虚拟网络嵌入，虚拟网络函数放置和虚拟化在地理上分布的DCN上； （4）通过广泛的模拟和实施现实的网络原型中的方案进行全面的绩效评估。该研究结合了理论分析，算法设计，网络优化，模拟和原型制作技术，以提供全面的工作解决方案，以实现高性能下一代DCN。预计这项研究将对未来DCN的基本设计原理产生深远的影响。这项研究的结果不仅会极大地提高DCN的灵活性和能源效率的性能，同时保持低成本，还促进了目前在数据中心托管的众多云计算应用程序。随着云计算渗透到社会的各个方面，这项研究将对社会产生更大的影响并帮助改变世界。

项目成果

Availability Aware Online Virtual Network Function Backup in Edge Environments

• DOI: 10.1109/tmc.2023.3282156
• 发表时间: 2024-05
• 期刊: IEEE Transactions on Mobile Computing
• 影响因子: 7.9
• 作者: Yu Liu;Xiaojun Shang;Yingling Mao;Z. Liu;Yuanyuan Yang

Joint Resource Management and Flow Scheduling for SFC Deployment in Hybrid Edge-and-Cloud Network

• DOI: 10.1109/infocom48880.2022.9796884
• 发表时间: 2022-05
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
• 作者: Yingling Mao;Xiaojun Shang;Yuanyuan Yang

Ant Colony based Online Learning Algorithm for Service Function Chain Deployment

• DOI: 10.1109/infocom53939.2023.10229012
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Yingling Mao;Xiaojun Shang;Yuanyuan Yang

Energy-Aware Online Task Offloading and Resource Allocation for Mobile Edge Computing

• DOI: 10.1109/icdcs57875.2023.00073
• 发表时间: 2023-07
• 期刊: 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Yu Liu;Yingling Mao;Xiaojun Shang;Z. Liu;Yuanyuan Yang