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

网络化的两个基本趋势是显而易见的。 首先，今天的大部分网络流量及其预计的巨大增长主要由传播给多个用户的内容组成。 第二，在具有动态和不可靠信道条件的移动的无线环境中越来越多地访问网络内容。传统的网络协议最初是为静态有线网络上的点对点通信而设计的，从根本上说，它不适合这种情况。受这些趋势的推动，本项目将开发动态和分布式算法，可以充分利用网络资源（包括带宽和存储），在不断变化的网络条件下有效和强大的内容分发。本项目建立在最近的积极研究工作，以数据为中心的网络，它把信息内容，而不是源-目的地对，在网络体系结构的中心。 虽然在以数据为中心的网络研究中已经取得了一些重要成果，但动态缓存和转发算法的联合设计和优化的核心问题还有待深入研究。 该项目将研究缓存和转发的基本限制，以及在以数据为中心的内容交付中优化带宽和存储使用的实用和强大算法的设计。 与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动态算法，以解决不断变化的内容，用户需求和网络条件下的大规模缓存和转发问题。 第一种方法是基于随机模型的分布式缓存和转发最近开发的PI。 这种方法显着扩展了经典的基于反压的路由技术，将缓存纳入统一的框架中，从而产生了新的算法，可以最大限度地提高网络满足用户需求的比率。 第二种方法是基于流的分布式凸优化框架，在该框架中，内容特定的路由和缓存是在分布式逐节点的基础上进行的，以最小化全局成本目标，如延迟。（2）设计可扩展的、健壮的、分层的动态缓存和转发算法，这些算法可以动态调整名称解析度，（3）开发联合收割机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的性能;（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际优势，（5）开发低复杂度的动态转发和高速缓存算法，在移动的无线环境中提供更低的用户延迟和对多用户干扰和信道衰落的更大的恢复力;以及（6）开发用于查询和缓存的算法，其导致在感测上下文中的最佳决策。这项工作的更广泛的意义将包括：（1）对国家安全、商业企业、科学探索和研究、健康服务和其他重要社会项目中使用的大数据应用的网络架构产生直接和长期影响;（2）对本科生和研究生教育的影响，特别强调让女生和少数民族学生参与，通过计划中的“数据中心网络的理论和算法”课程部分和主动手-（3）积极与大学其他学系、政府研究机构及工业界合作，加强研究及教育的基础设施;以及（4）通过参加多学科会议和讲习班，广泛传播信息，以提高对科学和技术的认识，和更广泛的媒体曝光。

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.