NeTS: Small: A Programmable Network Data Plane for Resource Management in Datacenters

NeTS：小型：用于数据中心资源管理的可编程网络数据平面

• 批准号: 1526791
• 负责人: Hari Balakrishnan
• 金额: $ 50万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526791&HistoricalAwards=false
• 关键词: NeTS; Small; Programmable; Network; Data

Datacenters have revolutionized computing, allowing small companies and research groups to harness the power of tens of thousands of computers, and large companies to provide a wide range of services to billions of users. Research on datacenter network architectures is important to meet the stringent performance requirements of many applications at acceptable cost. A key aspect of modern datacenter networks is their use of sophisticated resource management algorithms within the network, without which it would be prohibitively expensive or even impossible to accomplish many data-intensive computing tasks. Researchers, engineers, and network operators have been developing new methods for resource management at a healthy pace over the past two decades. Much of this work requires changes to network routers, whose data planes are typically implemented in hardware. Hardware is inflexible, so researchers cannot convincingly demonstrate their new ideas, and operators cannot evaluate them in practice. The two approaches available today for programmable networks---software routers and hardware routers with programmable elements---both have drawbacks. To date, software routers have failed to deliver on performance, being at least an order of magnitude slower than hardware, whereas programmable elements in hardware routers have not been able to provide a convenient platform to express most queue management, scheduling, and control algorithms. To address this problem, this proposal introduces Flexplane, a new way to architect a programmable network data plane capable of supporting a variety of resource management schemes. The goal is to support schemes specified in a high-level language like C++ as in a software simulator or a software router, but run them at hardware rates on real networks. The key idea is to use a centralized arbiter to emulate the behavior of an entire network with abstract packets, and reflect that behavior on to real packets sent by the endpoints over the actual network. With Flexplane, packets arrive at their destinations with the timings and headers that mimic on-path routers running the programmed schemes. Broader Impacts: Flexplane is of demonstrated interest to network infrastructure teams in companies like Facebook. The results of this research will directly benefit datacenters operated by companies like Facebook, Google, Microsoft, Amazon.com, and many other enterprises. The education plan includes the introduction of this research's findings into the undergraduate curriculum and offers undergraduates an opportunity to implement network protocols in a friendly programming environment, but have them run at high rates, a capability that was hitherto unavailable. The graduate-level education goals are to explore the limits of centralized arbitration and data-plane programmability. The course material will be made widely available through MIT OpenCourseWare and on the MITx MOOC.

数据中心使计算发生了革命性的变化，使小公司和研究小组能够利用数万台计算机的能力，使大公司能够为数十亿用户提供广泛的服务。对数据中心网络体系结构的研究对于以可接受的成本满足许多应用的严格性能要求非常重要。现代数据中心网络的一个关键方面是在网络中使用复杂的资源管理算法，如果没有这些算法，将无法完成许多数据密集型计算任务，成本高得令人望而却步。在过去的二十年里，研究人员、工程师和网络运营商一直在以健康的速度开发新的资源管理方法。这项工作的大部分需要对网络路由器进行更改，其数据平面通常在硬件中实现。硬件缺乏灵活性，因此研究人员无法令人信服地展示他们的新想法，运营商也无法在实践中对其进行评估。当今可用于可编程网络的两种方法-软件路由器和带有可编程元件的硬件路由器-都有缺点。到目前为止，软件路由器未能提供性能，至少比硬件慢一个数量级，而硬件路由器中的可编程元件无法提供一个方便的平台来表达大多数队列管理、调度和控制算法。为了解决这个问题，该方案引入了FlexPlane，这是一种新的方法来构建能够支持各种资源管理方案的可编程网络数据平面。其目标是支持在软件模拟器或软件路由器中以C++等高级语言指定的方案，但在真实网络上以硬件速率运行它们。其关键思想是使用集中式仲裁器来模拟具有抽象数据包的整个网络的行为，并将该行为反映到终端通过实际网络发送的实际数据包上。使用FlexPlane，数据包到达目的地时的定时和报头模仿运行编程方案的路径上的路由器。更广泛的影响：Facebook等公司的网络基础设施团队对FlexPlane表现出了兴趣。这项研究的结果将直接惠及Facebook、谷歌、微软、亚马逊和许多其他企业运营的数据中心。教育计划包括将这项研究的发现引入本科课程，并为本科生提供在友好的编程环境中实施网络协议的机会，但让它们以高速度运行，这是迄今为止无法获得的能力。研究生教育的目标是探索集中式仲裁和数据平面可编程性的局限性。课程材料将通过麻省理工学院开放式课程软件和MITx MOOC广泛提供。