Workshop on Self-Driving Networks

自动驾驶网络研讨会

• 批准号: 1748793
• 负责人: Nicholas Feamster
• 金额: $ 5万
• 依托单位: Princeton 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: 2017-08-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1748793&HistoricalAwards=false
• 关键词: Workshop; Self; Driving; Networks

This workshop brings together leading researchers from a range of disciplines across computer science to define a new research agenda in network measurement and data analytics with the goal of exploring how to design networks that manage themselves. These experts will explore taking advantage of advances in disciplines including machine learning, distributed systems, and formal methods to address growing requirements and constraints of modern networking applications. Because of the proliferation of applications and services that now run over the Internet ranging from video streaming to Internet-connected smart home devices to augmented reality---the expectations for the performance, reliability, and security of our communications networks are greater than ever, as the number and diversity of applications that run on top of the network continue to proliferate, and as the volume of traffic on the network continues to grow. To meet these expectations, network operators work tirelessly to continuously collect troves of heterogeneous data from the network, analyze this data to infer characteristics about the network, and decide whether to change the network's configuration in response to network conditions (e.g., a shift in traffic demand or a cyber attack). Today, these three steps are decoupled: operators perform them separately, on different timescales, often in a slow or manual fashion that relies on intuition, as opposed to data, analysis, and inference. The vision for this workshop is that networks might one day be able to largely manage themselves through a combination of query-driven network measurement, automated inference techniques, and programmatic control. Intellectual Merit: The research agenda lends itself to research problems that will foster advances in computer science, including the following areas: 1. Distributed systems that optimize the use of limited resources for complex tasks, including support for multiple simultaneous queries; New architectures to support programmable measurement in hardware; Algorithms that partition a network analytics query across a centralized stream processor and the distributed switches and network middleboxes. 2. New measurement techniques (beyond "ping" and "traceroute") that leverage the capabilities of P4-capable data planes (e.g., in-band telemetry); Software/hardware co-design for better network measurements; Clean-slate, problem-driven designs for new network measurement tools that might tackle problems in network measurement that have proved evasive (e.g., application quality of experience); Measurement of unified compute, storage, and networking infrastructure, including monitoring of container-based systems 3. Machine Learning and new algorithms for automated troubleshooting and "what-if" scenario evaluation; Development of parsimonious models that could be implemented (at least partially) at line rate on switch hardware; Prediction and inference over non-stationary datasets to changing traffic patterns. 4. Security and privacy through scalable algorithms and systems for detecting a broad range of attacks, from denial of service to data exfiltration; Better ways to monitor application performance without having to perform man-in-the-middle attacks on traffic. Broader Impacts: Results from this workshop will be broadly distributed so that researchers in all of the areas noted above will benefit from the discussions, conclusions and recommendations resulting from the workshop. Research inspired by the workshop could have broad societal impacts by helping network operators envision how to integrate measurement, data analysis, and configuration decisions and move toward automated network control.

本次研讨会汇集了来自计算机科学各个学科的领先研究人员，以探索如何设计自我管理的网络为目标，定义了网络测量和数据分析的新研究议程。这些专家将探索利用学科的进步，包括机器学习、分布式系统和形式化方法，以解决现代网络应用日益增长的需求和约束。由于现在在互联网上运行的应用程序和服务的激增，从视频流到连接互联网的智能家居设备再到增强现实，随着在网络上运行的应用程序的数量和多样性不断增加，以及网络上的流量不断增长，人们对通信网络的性能、可靠性和安全性的期望比以往任何时候都要高。为了满足这些期望，网络运营商不知疲倦地不断从网络中收集大量异构数据，分析这些数据以推断网络特征，并决定是否根据网络条件（例如，流量需求的变化或网络攻击）改变网络配置。今天，这三个步骤是解耦的：操作人员在不同的时间尺度上分别执行它们，通常以缓慢或手动的方式依赖于直觉，而不是数据、分析和推理。本次研讨会的愿景是，网络可能有一天能够通过查询驱动的网络度量、自动推理技术和编程控制的组合在很大程度上管理自己。智力优势：研究议程有助于研究将促进计算机科学进步的问题，包括以下领域：分布式系统可以优化有限资源对复杂任务的使用，包括支持多个同时查询；支持硬件可编程测量的新架构；跨集中式流处理器、分布式交换机和网络中间体划分网络分析查询的算法。2. 新的测量技术（超越“ping”和“traceroute”），利用了p4数据平面的能力（例如，带内遥测）；软件/硬件协同设计，实现更好的网络测量；为新的网络测量工具设计全新的、问题驱动的设计，这些工具可能会解决网络测量中被证明是难以规避的问题（例如，应用程序的体验质量）；统一计算、存储和网络基础设施的度量，包括对基于容器的系统的监控。用于自动故障排除和“假设”场景评估的机器学习和新算法；开发可以在交换机硬件上以线速率实现（至少部分实现）的简约模型；非平稳数据集对交通模式变化的预测和推断。4. 通过可扩展的算法和系统检测广泛的攻击，从拒绝服务到数据泄露的安全性和隐私性；更好的监控应用程序性能的方法，而不必对流量执行中间人攻击。更广泛的影响：本次研讨会的成果将广泛传播，以便上述所有领域的研究人员都能从研讨会的讨论、结论和建议中受益。受研讨会启发的研究可以通过帮助网络运营商设想如何集成测量、数据分析和配置决策并走向自动化网络控制，从而产生广泛的社会影响。