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​​术进步的推动下，大型在线服务提供商构建了由数万甚至数十万台服务器组成的海量数据中心作为基础设施，其中数据中心网络的性能通常在数据中心网络（DCN）中起着至关重要的作用。在网络的不同位置、不同的应用程序之间、不同的公司实施之间，以及时不时地，当今的数据中心网络仍然缺乏足够的灵活性来有效处理如此巨大的异构性流量。方式。同时，随着虚拟化技术的深入发展，云应用程序不再直接运行在物理硬件或操作系统上，而是分配在由底层网络虚拟化的模拟操作系统层上，因此单个基础设施提供商就可以支持。为了应对挑战，本研究设计了一种面向不断增长的性能需求和异构流量特征的通用网络架构。当今的数据中心。该架构提供了一种自适应全栈范例，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，该研究重点关注几个紧密耦合的问题：(1) 设计一种新颖、经济高效的 DCN 架构，该架构可以动态地进行。系统地处理数据中心的流量异构性；（2）为单播和组播流量模型下的架构设计相应的路由算法；（3）在架构中开发高效的网络虚拟化，包括虚拟网络嵌入、虚拟网络功能算法放置和虚拟化。在地理上分布式DCN；（4）通过在现实网络原型中进行广泛的模拟和实施来进行全面的性能评估。该研究结合了理论分析、算法设计、网络优化、模拟和原型技术，以提供能够实现高性能的综合工作解决方案。这项研究预计将对未来 DCN 的基本设计原理产生深远的影响。这项研究的成果不仅将大大提高 DCN 的性能和能源效率，同时保持低成本，而且还将促进众多云计算的发展。目前托管在数据中心的计算应用程序。随着云计算正在渗透到社会的各个方面，这项研究将对社会产生更广泛的影响，帮助改变世界。

项目成果

Availability Aware Online Virtual Network Function Backup in Edge Environments

边缘环境中的可用性感知在线虚拟网络功能备份

• DOI: 10.1109/tmc.2023.3282156
• 发表时间: 2024-05-01
• 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
• 通讯作者: Yuanyuan Yang

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

移动边缘计算的能源感知在线任务卸载和资源分配

• DOI: 10.1109/icdcs57875.2023.00073
• 发表时间: 2023-07-01
• 期刊: 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Yu Liu;Yingling Mao;Xiaojun Shang;Z. Liu;Yuanyuan Yang
• 通讯作者: Yuanyuan Yang

Ant Colony based Online Learning Algorithm for Service Function Chain Deployment

基于蚁群的服务功能链部署在线学习算法

• DOI: 10.1109/infocom53939.2023.10229012
• 发表时间: 2023-05-17
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Yingling Mao;Xiaojun Shang;Yuanyuan Yang
• 通讯作者: Yuanyuan Yang

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

边云混合网络中 SFC 部署的联合资源管理和流量调度

• DOI: 10.1109/infocom48880.2022.9796884
• 发表时间: 2022-05-02
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
• 作者: Yingling Mao;Xiaojun Shang;Yuanyuan Yang
• 通讯作者: Yuanyuan Yang