CNS Core: Small: Exploring the Benefits on Non-Work-Conserving Networking

CNS 核心：小型：探索非工作保护网络的好处

• 批准号: 2006530
• 负责人: Roch Guerin
• 金额: $ 45.86万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006530&HistoricalAwards=false
• 关键词: CNS; Core; Small; Exploring; Benefits

Metering gates that pace traffic at highway entrances during peak hours have become common practice. They prevent multiple cars from entering back-to-back, which can create bubbles of congestion that propagate and generate broad disruptions. A similar principle applies in the data networks that make up the modern communication infrastructure. This project explores the use of such metering/pacing options to improve the performance of those networks. In particular, can latency-sensitive traffic such as interactive transactions, games, audio and video, etc., be paced, i.e., slowed down on entry, while improving the network's ability to deliver it in time? Answering this question is the goal of this project.Most end-points feeding the Internet are today located in datacenters overseen by large-scale providers. This offers the opportunity for better control of the traffic they generate. This project aims to develop a principled approach for selecting how to best pace end-point traffic as a function of traffic profiles and sensitivity to delay towards minimizing the network resources (and therefore cost) required. This can be formulated as an optimization problem whose complexity, however, grows rapidly with network size. The project targets practical guidelines to realize near-optimal pacing together with software solutions to orchestrate the resulting controls, and an evaluation of the benefits they afford.The recent COVID-19 crisis and the shift to working remotely and to online learning has triggered an unprecedented growth in the volume of "real-time" traffic. This has in turn stressed the network infrastructure responsible for carrying it, e.g., forcing some providers to limit the amount of traffic they allow. The approaches this project seeks to develop have the potential to allow those same networks to carry substantially more real-time traffic while preserving performance and without requiring costly upgrades. The work will be carried out in collaboration with a large-scale cloud provider to ensure it captures the practical constraints that networks connecting cloud sites face.The project's website is in the form of a wiki hosted by the Open Science Foundation and accessible at https://osf.io/mh4eg/. The wiki serves as an entry point for results produced by the project. It includes a section devoted to publications produced under the project's auspices and setup to ensure open-access to those works. Other sections will provide either repositories or links to external repositories for data sets and software produced as part of the project. Repositories will be selected based on their accessibility and ability to ensure long-term access to hosted information. Selected repositories will ensure preservation of the data for at least five years beyond the project's end, and preferably longer.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.

在高峰时段，在高速公路入口处设置计量门来调节交通流量已经成为一种普遍的做法。 它们可以防止多辆汽车背靠背进入，这可能会产生泡沫的拥堵，传播和产生广泛的中断。 类似的原理也适用于构成现代通信基础设施的数据网络。 该项目探讨了使用这种计量/调步选项来提高这些网络的性能。 特别地，可以将诸如交互式事务、游戏、音频和视频等对延迟敏感的业务，定节奏，即，进入速度减慢，同时提高网络及时交付的能力？解决这个问题是本项目的目标。今天，大多数向互联网提供服务的端点都位于由大型供应商监督的数据中心。 这为更好地控制它们产生的流量提供了机会。 该项目旨在开发一种原则性的方法，用于选择如何根据流量配置文件和对延迟的敏感性来最大限度地减少所需的网络资源（从而降低成本）来最佳地调整端点流量。 这可以用公式表示为一个优化问题，其复杂性随着网络规模的增长而迅速增长。 该项目的目标是实现接近最佳节奏的实用指南，以及协调由此产生的控制的软件解决方案，并评估它们所带来的好处。最近的COVID-19危机以及向远程工作和在线学习的转变引发了“实时”流量的前所未有的增长。 这反过来又强调了负责承载它的网络基础设施，例如，迫使一些提供商限制他们允许的流量。 该项目寻求开发的方法有可能允许这些相同的网络承载更多的实时流量，同时保持性能，而不需要昂贵的升级。 这项工作将与一家大型云提供商合作进行，以确保它能够捕捉到连接云站点的网络所面临的实际限制。该项目的网站采用维基的形式，由开放科学基金会托管，可在https://osf.io/mh4eg/上访问。 wiki作为项目产生的结果的入口点。 它包括一个专门介绍在该项目主持下制作的出版物的部分，并确保开放获取这些作品。 其他部分将提供作为项目一部分制作的数据集和软件的储存库或与外部储存库的链接。将根据可访问性和确保长期访问托管信息的能力来选择储存库。 选定的存储库将确保数据在项目结束后至少保存五年，最好更长。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Adaptive Edge Offloading for Image Classification Under Rate Limit

• DOI: 10.1109/tcad.2022.3197533
• 发表时间: 2022-07
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Jiaming Qiu-;Ruiqi Wang;Ayan Chakrabarti;R. Guérin;Chenyang Lu

Minimizing network bandwidth under latency constraints: The single node case

在延迟限制下最小化网络带宽：单节点情况

• 发表时间: 2021
• 期刊: Proceedings 33rd International Teletraffic Congress (ITC 33
• 作者: Jiayi Song, Roch Guérin