NeTS: Small: Collaborative Research: Dynamic Forwarding and Caching for Data-Centric Networks: Theory and Algorithms

联网的两个基本趋势显而易见。首先，今天的大部分网络流量及其预计的巨大增长，主要是传播给多个用户的内容。其次，在具有动态和不可靠的信道条件的移动无线环境中，网络内容被越来越多地访问。最初为静态有线网络上的点对点通信而设计的传统网络协议从根本上不适合这样的场景。在这些趋势的推动下，该项目将开发动态和分布式算法，以充分利用网络资源(带宽和存储)，在不断变化的网络条件下实现高效和稳健的内容传播。该项目建立在最近以数据为中心的网络研究成果的基础上，该网络将信息内容而不是源-目的地对置于网络体系结构的中心。虽然在以数据为中心的网络研究方面已经取得了许多重要成果，但动态缓存和转发算法的联合设计和优化的核心问题还没有得到深入的研究。该项目将研究缓存和转发的基本限制，以及设计实用和健壮的算法，以优化以数据为中心的内容交付中带宽和存储的使用。与许多现有的集中式静态缓存算法不同，该项目将开发可扩展的、分布式的动态算法，以应对不断变化的内容、用户需求和网络条件下的大规模缓存和转发。第一种方法基于PI最近开发的分布式缓存和转发的随机模型。该方法极大地扩展了传统的基于背压的路由技术，将缓存合并到一个统一的框架中，从而产生了最大限度地提高网络满足的用户需求速率的新算法。第二种方法基于基于流的分布式凸优化框架，其中特定于内容的路由和缓存在逐个节点的分布式基础上进行，以最小化延迟等全局代价目标。该项目解决了实际和理论问题，包括以下主要内容：(1)设计联合最优的转发和缓存算法以最小化延迟；(2)设计可扩展的、健壮的、分层的动态缓存和转发算法，其操作与动态调整的名称解析一起运行；(3)开发将动态缓存和转发与拥塞控制相结合的算法，以实现公平性和增强性能；(4)探索存储和传输中的编码技术，以获得性能和可靠性方面的实用优势，并使研究高速缓存和转发中的基本性能极限成为可能；(5)开发低复杂度、动态转发和高速缓存算法，在移动无线环境中提供更低的用户延迟和更强的对多用户干扰和信道衰落的恢复能力；这项工作的更广泛的意义将包括：(1)对用于国家安全、商业企业、科学探索和研究、卫生服务和其他重要社会项目的大数据应用的网络体系结构产生直接和长期的影响；(2)通过计划中的课程部分(关于以数据为中心的网络的理论和算法)和涉及试验台调查和验证的积极动手项目，对本科生和研究生教育产生影响，特别强调让女性和少数族裔学生参与进来；(3)通过与其他大学部门、政府研究机构和工业界积极合作，加强研究和教育的基础设施；以及(4)通过参加多学科会议和研讨会，以及通过接触更广泛的媒体，广泛传播信息，以增进对科学和技术的了解。

Two fundamental trends in networking are clearly visible. First, the bulk of network traffic today, and of its projected enormous growth, consists mainly of content disseminated to multiple users. Second, network content is accessed increasingly in mobile wireless environments with dynamic and unreliable channel conditions. Traditional network protocols, designed originally for point-to-point communication over static wired networks, are fundamentally ill suited for such scenarios. Motivated by these trends, this project will develop dynamic and distributed algorithms which can fully exploit network resources (both bandwidth and storage) for efficient and robust content dissemination under changing network conditions.This project builds on recent active research efforts in data-centric networking, which places information content, rather than source-destination pairs, at the center of the network architecture. While there have been a number of significant results in data-centric networking research, the central problem of the joint design and optimization of dynamic caching and forwarding algorithms has yet to be thoroughly studied. This project will study the fundamental limits of caching and forwarding, as well as the design of practical and robust algorithms for optimizing the use of bandwidth and storage in data-centric content delivery. Unlike many existing works on centralized algorithms for static caching, this project will develop scalable, distributed, dynamic algorithms that can address large-scale caching and forwarding under changing content, user demands and network conditions.To achieve this goal, the project will take two complementary approaches. The first approach is based on a stochastic model for distributed caching and forwarding recently developed by the PIs. This approach significantly expands on classical backpressure-based routing techniques to incorporate caching within a unified framework, leading to new algorithms which maximize user demand rate satisfied by the network. The second approach is based on a flow-based distributed convex optimization framework, in which content-specific routing and caching are carried out on a distributed node-by-node basis to minimize a global cost objective such as delay.This project addresses both practical and theoretical issues, and consists of the following main thrusts: (1) the design of jointly optimal forwarding and caching algorithms for minimizing delay; (2) the design of scalable, robust, hierarchical dynamic caching and forwarding algorithms which operate with dynamically adjusted name resolutions; (3) the development of algorithms which combine dynamic caching and forwarding with congestion control for fairness and enhanced performance; (4) the exploration of coding techniques in storage and transmission for obtaining practical advantages in performance and reliability, as well as for enabling the study of fundamental performance limits in caching and forwarding; (5) the development of low-complexity, dynamic forwarding and caching algorithms delivering lower user delay and greater resilience to multi-user interference and channel fading in mobile wireless environments; and (6) development of algorithms for querying and caching which lead to optimal decision making in a sensing context.The broader significance of this work will include: (1) direct and long-term impact on network architectures for big data applications used in national security, commercial enterprise, scientific exploration and research, health services, and other important social projects; (2) impact on undergraduate and graduate education, with particular emphasis on involving female and minority students, through a planned course segment on 'theory and algorithms for data-centric networking' and active hands-on projects involving testbed investigation and validation; (3) enhancement of infrastructure for research and education through active partnering with other university departments, government research institutions and industry; and (4) broad dissemination to enhance scientific and technological understanding by participation in multi-disciplinary conferences and workshops, and exposure to broader media.