NeTS: Small: Collaborative Research: Distributed Approximate Packet Classification

NeTS：小型：协作研究：分布式近似数据包分类

• 批准号: 1829349
• 负责人: Minlan Yu
• 金额: $ 14.95万
• 依托单位: Harvard University
• 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: 2017-10-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829349&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Distributed

Network traffic classification - assigning incoming packets to classes for processing based on pattern-matching rules - is critical for many network management tasks, including performance monitoring and fault diagnosis. However, as the number of classification tasks grows, the resources required to store and apply the rules (switch memory in particular) can become scarce. This project takes an end-to-end view of traffic classification, observing that in addition to the memory usage at switches, other, cheaper resources are involved in packet processing, specifically bandwidth to transfer selected packets to the receivers, and the receiver systems themselves. The proposed approach trades off memory for bandwidth by introducing overselection, in which approximate classification made in reduced memory introduces additional unwanted traffic. The idea is that in many applications, the side-effects of overselection are quite manageable; the project will investigate multiple types of applications such as middleboxes and packet scrubbers that can tolerate overselection. The approach involves a Cuckoo-filter-based approximate prefix matcher at switches, which stores classification rules with different prefix lengths. The key challenges in supporting distributed approximate packet classification include: (1) new data structures that best support the tradeoffs between memory and overselection; (2) new algorithms and systems that handle traffic dynamics and network dynamics with overselection; (3) systematic analysis of the impact of overselection on different network functions; and (4) spatial and temporal coordination of switches and receivers. This project will enable innovations and redesign of these management applications relying on traffic classification in software defined networks (SDN), and thus lead to more efficiently managed enterprise and datacenter networks. Moreover, the work will facilitate interactions among theoretical and systems research. Finally, the principal investigators have a record of engaging underrepresented groups and undergraduates in research and plan to continue these engagements as part of this project.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.

网络流量分类 - 将传入数据包分配给基于模式匹配规则处理的类，对于许多网络管理任务，包括性能监控和故障诊断至关重要。 但是，随着分类任务的数量的增加，存储和应用规则所需的资源（尤其是切换内存）可能会变得稀缺。 该项目对流量分类进行了端到端的视图，观察到，除了交换机的内存使用情况外，其他资源还参与数据包处理，特别是带宽以将选定的数据包传输到接收器以及接收器系统本身。所提出的方法通过引入过度选择来将内存换成带宽，其中减少内存中进行的近似分类引入了其他有害的流量。这个想法是，在许多应用程序中，过度选择的副作用是相当可管理的。该项目将研究多种类型的应用程序，例如可以忍受过度选择的中间箱和数据包洗涤器。该方法涉及开关处的基于杜鹃滤波器的近似前缀匹配器，该匹配器以不同的前缀长度存储分类规则。支持分布式近似数据包分类的主要挑战包括：（1）最能支持内存和过度选择之间权衡取舍的新数据结构； （2）通过过度选择处理流量动态和网络动态的新算法和系统； （3）对过度选择对不同网络功能的影响的系统分析； （4）开关和接收器的空间和时间协调。 该项目将依靠软件定义网络（SDN）中的流量分类来实现这些管理应用程序的创新和重新设计，从而导致更有效地管理企业和数据中心网络。此外，这项工作将有助于理论和系统研究之间的相互作用。最后，首席研究人员的记录记录了将代表性不足的群体和本科生参与研究并计划继续作为该项目的一部分。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来评估的。