AitF: Theory and Practice of Probabilistic Network Programming

AitF：概率网络规划的理论与实践

• 批准号: 1637532
• 负责人: John Foster
• 金额: $ 79.9万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637532&HistoricalAwards=false
• 关键词: AitF; Theory; Practice; Probabilistic; Network

A common objective in many computer networks is to balance traffic across multiple paths in order to improve throughput, reduce congestion, and achieve higher utilization. Although there is an extensive theoretical literature on how best to route traffic through a capacitated network, most networks today rely on schemes that are easy to implement but do not always perform well in practice. This is due, in part, to the gap between the community of theory researchers who study advanced routing schemes and the community of systems researchers who design, build, and operate networks. It is also partly due to the lack of high-level programming languages that make it easy to program advanced routing functionalities in networks and to reason about the performance of such programs.This project will advance the theory and practice of routing by developing oblivious and semi-oblivious routing algorithms that can be implemented on top of current network hardware, accompanied by a probabilistic network programming language and compiler. The intellectual merit of this research lies in developing new routing algorithms that optimize for objectives such as minimizing congestion and balancing load, designing new programming languages based on probabilistic semantics that can be used to express and reason about these algorithms, and building prototype implementations that can be deployed on software-defined networking platforms. This project will have broad impact on society by developing algorithms and tools that could help make networks more reliable and efficient while simplifying network management. To reinforce this broad impact, the project incorporates organizing an interdisciplinary workshop at the intersection between algorithms and networking.

在许多计算机网络中，一个共同的目标是平衡多条路径上的流量，以提高吞吐量，减少拥塞，并实现更高的利用率。虽然有大量的理论文献介绍了如何通过容量受限的网络最好地路由流量，但今天的大多数网络都依赖于易于实现但在实践中并不总是表现良好的方案。这在一定程度上是由于研究高级路由方案的理论研究人员群体与设计、构建和运营网络的系统研究人员群体之间存在差距。这也部分是由于缺乏高级编程语言，使它很容易在网络中编程高级路由功能，并对这些程序的性能进行推理。本项目将通过开发不经意和半不经意的路由算法，可以在当前的网络硬件上实现，伴随着概率网络编程语言和编译器，推进路由的理论和实践。这项研究的智力价值在于开发新的路由算法，优化目标，如最大限度地减少拥塞和平衡负载，设计新的编程语言的基础上概率语义，可用于表达和推理这些算法，并建立原型实现，可以部署在软件定义的网络平台。该项目将通过开发算法和工具来帮助提高网络的可靠性和效率，同时简化网络管理，从而对社会产生广泛的影响。为了加强这种广泛的影响，该项目包括在算法和网络之间的交叉点组织跨学科研讨会。

项目成果

Universal Semantics for the Stochastic λ-Calculus

随机 δ 微积分的通用语义

• 发表时间: 2021
• 期刊: ACM/IEEE Symposium on Logic in Computer Science
• 作者: Azevedo de Amorim, Pedro;Kozen, Dexter;Mardare, Radu;Panangaden, Prakash;Roberts, Michael
• 通讯作者: Roberts, Michael

Online Flow Computation on Unit-Vertex-Capacitated Networks

单位顶点容量网络上的在线流计算

• DOI: 10.1137/1.9781611976021.9
• 发表时间: 2020
• 期刊: 1st Symposium on Algorithmic Principles of Computer Systems
• 作者: Arsenis, Makis;Kleinberg, Robert
• 通讯作者: Kleinberg, Robert

Scalable verification of probabilistic networks

概率网络的可扩展验证

• DOI: 10.1145/3314221.3314639
• 发表时间: 2019
• 期刊: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation
• 作者: Smolka, Steffen;Kumar, Praveen;Kahn, David M.;Foster, Nate;Hsu, Justin;Kozen, Dexter;Silva, Alexandra
• 通讯作者: Silva, Alexandra