Collaborative Research: CNS Core: Medium: Inference and Control in Overlay Networks

合作研究： CNS 核心：媒介：覆盖网络中的推理与控制

• 批准号: 2106268
• 负责人: Eytan Modiano
• 金额: $ 30万
• 依托单位: Massachusetts Institute of Technology
• 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: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106268&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

An overlay network is a layered network, where the overlay layer makes use of the underlay layer in order to deliver data to its destinations. Overlay networks arise in many application scenarios such as content distribution networks that are used to distribute content over the Internet (e.g., movies, music, etc.), and are often used to deploy new technology that is incompatible with legacy devices and protocols. A key challenge in such systems is that the overlay cannot observe the innerworkings of the underlay, making it difficult to use the underlay efficiently. This project will develop mechanisms to learn the network topology and congestion of the underlay, and network algorithms for routing messages efficiently across overlay networks. Existing overlay systems mostly rely on simple models of the underlay network, and may fail to achieve good performance when the underlay nodes cannot be fully observed and controlled. This project will develop fundamental limits and practical algorithms for monitoring and controlling partially observable/controllable overlay-underlay networks. This will be accomplished through two interdependent thrusts: thrust 1 will develop techniques that utilize measurements and side information observable to the overlay in order to infer the underlay network structure and state; and thrust 2 will develop algorithms that utilize the inferred information to control the operation of overlay nodes so as to optimize the performance for overlay services.This collaborative project between Massachusetts Institute of Technology (MIT) and The Pennsylvania State University (PSU) brings together expertise on network inference and network control to tackle the problem of joint inference and control in overlay networks. The results of this project will enable a more efficient migration to next-generation networks and will facilitate system manageability for increasingly complex networks. The project will also support educational activities at the participating institutions and help broadening participation in computing of under-represented groups through summer programs at MIT and PSU. A project website will be maintained by the PIs for the duration of the project at: https://sites.psu.edu/nsrg/research/inference-and-control-in-overlay/. The website will contain information about project participants, significant results, and publications.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.

覆盖网络是分层网络，其中覆盖层利用参考图层将数据传送到其目的地。覆盖网络出现在许多应用场景中，例如用于在互联网上分发内容(例如，电影、音乐等)的内容分发网络，并且通常用于部署与传统设备和协议不兼容的新技术。此类系统中的一个关键挑战是，覆盖无法观察参考底图的内部工作，从而难以有效地使用参考底图。该项目将开发各种机制，以了解底层网络的网络拓扑和拥塞情况，以及在覆盖网络中高效地路由消息的网络算法。现有的覆盖系统大多依赖于简单的底层网络模型，当不能完全观察和控制底层节点时，可能无法达到良好的性能。该项目将为监测和控制部分可观察/可控制的覆盖-衬底网络开发基本限制和实用算法。这将通过两个相互依赖的推力来完成：推力1将开发利用对覆盖可观察到的测量和辅助信息来推断底层网络结构和状态的技术；推力2将开发利用推断的信息来控制覆盖节点的操作从而优化覆盖服务性能的算法。这个由麻省理工学院(MIT)和宾夕法尼亚州立大学(PSU)合作的项目汇集了网络推理和网络控制方面的专业知识，以解决覆盖网络中的联合推理和控制问题。该项目的结果将使向下一代网络的迁移更加有效，并将促进日益复杂的网络的系统管理。该项目还将支持参与机构的教育活动，并通过麻省理工学院和巴黎州立大学的暑期项目帮助扩大代表不足群体在计算方面的参与。在项目期间，项目主管将维护一个项目网站：https://sites.psu.edu/nsrg/research/inference-and-control-in-overlay/.该网站将包含有关项目参与者、重大成果和出版物的信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。