Workshop on Self-Driving Networks

这次研讨会汇集了来自计算机科学领域多个学科的顶尖研究人员，以确定网络测量和数据分析方面的新研究议程，目标是探索如何设计能够自我管理的网络。这些专家将探索利用包括机器学习、分布式系统和正式方法在内的学科的进步来解决现代网络应用程序日益增长的需求和限制。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，以及网络上的流量持续增长，对我们通信网络的性能、可靠性和安全性的期望比以往任何时候都更高。为了满足这些期望，网络运营商不知疲倦地不断地从网络中收集各种数据，分析这些数据以推断有关网络的特性，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)改变网络的配置。今天，这三个步骤是分离的：操作员分别在不同的时间尺度上执行它们，通常是以依赖直觉的缓慢或手动方式执行，而不是数据、分析和推理。这个研讨会的愿景是，有朝一日，网络可能能够通过查询驱动的网络测量、自动推理技术和编程控制的组合来在很大程度上管理自己。智力价值：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：1.优化有限资源用于复杂任务的分布式系统，包括支持多个同时查询；支持硬件中的可编程测量的新体系结构；跨集中式流处理器和分布式交换机和网络中间盒划分网络分析查询的算法。2.利用具有P4能力的数据面的能力(例如，带内遥测)的新的测量技术(超越“ping”和“Traceroute”)；为更好的网络测量而进行的软件/硬件联合设计；为可能解决网络测量中已被证明回避的问题(例如，应用程序体验质量)的新的网络测量工具进行干净的、问题驱动的设计；对统一计算、存储和网络基础设施的测量，包括对基于容器的系统的监控；3.机器学习和用于自动故障排除和“假设”情景评估的新算法；开发可在交换机硬件上以线速(至少部分)实施的简约模型；对非平稳数据集进行预测和推断以适应不断变化的交通模式。4.通过可扩展的算法和系统实现安全和隐私，用于检测从拒绝服务到数据外泄的各种攻击；无需对流量执行中间人攻击即可更好地监控应用程序性能。更广泛的影响：将广泛分发这次研讨会的成果，以便上述所有领域的研究人员都能从研讨会产生的讨论、结论和建议中受益。受研讨会启发的研究可以帮助网络运营商设想如何集成测量、数据分析和配置决策，并迈向自动化网络控制，从而产生广泛的社会影响。

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.