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

覆盖网络是分层的网络，其中覆盖层利用底层来将数据传递到目的地。覆盖网络出现在许多应用场景中，例如用于在Internet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遗留设备和协议不兼容的新技术。这种系统的一个关键挑战是覆盖层无法观察到底层的内部工作，这使得很难有效地使用底层。该项目将开发学习网络拓扑和底层拥塞的机制，以及跨覆盖网络有效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，当底层节点不能被充分观察和控制时，可能无法获得良好的性能。该项目将开发用于监测和控制部分可观察/可控覆盖-底层网络的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将开发利用覆盖层可观察到的测量和侧面信息的技术，以推断底层网络的结构和状态；推力2将开发算法，利用推断的信息来控制覆盖节点的操作，从而优化覆盖服务的性能。麻省理工学院（MIT）和宾夕法尼亚州立大学（PSU）之间的合作项目汇集了网络推理和网络控制方面的专业知识，以解决覆盖网络中联合推理和控制的问题。该项目的结果将使更有效地迁移到下一代网络，并将促进日益复杂的网络的系统可管理性。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PSU的暑期项目帮助代表性不足的群体扩大对计算机的参与。项目负责人将在项目期间维护一个项目网站：https://sites.psu.edu/nsrg/research/inference-and-control-in-overlay/。该网站将包含有关项目参与者、重要成果和出版物的信息。该奖项反映了美国国家科学基金会的法定使命，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。

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.