CAREER: A Scalable Multiplane Data Center Network

大型互联网数据中心提供商，无论是公共的还是私有的，都必须支持数十万台服务器之间不断增长的数据速率，以满足处理和存储需求。 运营商依赖于类似的横向扩展网络结构（通常是折叠Clos拓扑）来构建其网络。 自2000年代中期部署以来，这些横向扩展设计利用了基于互补金属氧化物半导体（CMOS）的开关硅的稳定增长的性能和降低的成本，以跟上需求的步伐。 不幸的是，这些趋势无法继续下去：网络交换机面临着目前阻碍中央处理器（CPU）制造商的CMOS工艺扩展限制。 正如CPU已经转向多核设计以避开其扩展限制一样，数据中心运营商也需要采用替代架构来扩展到下一代链路速率。该项目将演示一种称为SelectorNet的混合电气/光学网络拓扑，该拓扑可扩展到数十万台服务器，链路速率达到每秒1.6太比特。 不像最近的建议，利用二维或三维微机电系统（2D或3D-MEMS）光交叉开关，SelectorNet依赖于一种新颖的光学设备，放弃了交叉抽象。相反，它依赖于间接传递主机之间的数据包没有直接连接我们的新颖的“选择器”交换机。 其结果是，到2020年，网络结构不仅在成本上与最先进的基于Clos的设计相比具有竞争力，而且在成本、能源、性能和可靠性方面继续扩展，因为链路速率超过每秒400千兆比特。更广泛的影响：确保这项工作的好处超出传统研究指标的影响是其设计的一部分。 这项研究的结果将使设计和构建可扩展、高效和高可用的云和数据中心服务变得更加容易。 通过降低部署云基础设施的成本，研究人员希望降低最大运营商的成本，同时降低较小组织进入云的障碍。 他们将进一步扩展研究生和本科生的研究技能，以实际操作的方式解决必要的数据中心效率和云计算研究挑战。 让本科生在他们的课程中接触云计算技术，并通过指导研究，将提高他们在毕业时的市场竞争力，并有可能激发他们的好奇心，鼓励他们追求研究生学业。 教学生如何建立国家的最先进的网络系统，是基于严格的分析和实际的限制是必不可少的，在我们日益网络化的世界。 这项研究的另一个组成部分将是创建和传播视频，这将扩大公众对大规模计算，机器学习和互联网系统所面临的科学和工程挑战的认识和理解。

Large Internet data center providers, both public and private, must support ever-increasing data rates between literally hundreds of thousands of servers to meet processing and storage demand. Operators have relied on similar scale-out network fabrics (typically folded-Clos topologies) to construct their networks. Since their deployment in the mid-2000s, these scale-out designs have leveraged the steadily increasing performance and decreasing cost of complementary metal-oxide semiconductor (CMOS)-based switching silicon to keep pace with demand. Unfortunately, these trends cannot continue: network switches face the same CMOS process-scaling limitations that currently hamper central processing unit (CPU) manufacturers. Just as CPUs have moved to multi-core designs to side-step their scaling limitations, so too will data center operators need to adopt alternative architectures to scale to next-generation link rates.This project will demonstrate a hybrid electrical/optical nework topology, called SelectorNet, which scales to hundreds of thousands of servers at link rates reaching 1.6 terabits per second. Unlike recent proposals which utilize two dimensional- or three-dimensional microelectromechanical systems (2D or 3D-MEMS) optical crossbar switches, SelectorNet relies on a novel optical device that abandons the crossbar abstraction. Instead, it relies on indirection to deliver packets between hosts that are not directly connected by our novel "selector" switches. The result is a network fabric that is not only cost-competitive with state-of-the-art Clos-based designs in 2020, but continues to scale in terms of cost, energy, performance, and reliability as link rates surpass 400 gigabits per second.Broader Impact: Ensuring that the benefits of this work have impact beyond the traditional metrics of research is integral to its design. The results of this research will make it easier to design and build scalable, efficient, and highly-available cloud and data center services. By reducing the cost to deploy cloud infrastructure, the researchers hope to lower costs for the largest operators, while reducing the barrier to entry of the cloud for smaller organizations. They will further expand the research skills of graduate and undergraduate students to address necessary datacenter efficiency and cloud computing research challenges in a hands-on manner. Exposing undergraduate students to cloud computing technologies in their courses and through mentored research will enhance their marketability at graduation and has the potential to inspire their curiosity and encourage the pursuit of graduate studies. Teaching students how to build state-of-the-art networked systems that are grounded in rigorous analysis and practical constraints is essential in our increasingly networked world. An additional component of this research will be the creation and dissemination of videos that will broaden public awareness and appreciation of the science and engineering challenges facing large-scale computing, machine learning, and Internet systems.