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

NeTS：小型：协作研究：以数据为中心的网络的动态转发和缓存：理论和算法

• 批准号: 1423240
• 负责人: John Doyle
• 金额: $ 25万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423240&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Dynamic

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.

网络的两个基本趋势是清晰可见的。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。其次，在动态和不可靠的信道条件下，移动无线环境对网络内容的访问越来越多。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，从根本上来说不适合这种情况。在这些趋势的推动下，该项目将开发动态和分布式算法，这些算法可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高效、稳健地传播内容。该项目建立在最近在以数据为中心的网络方面的积极研究成果的基础上，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中已经取得了许多重要的成果，但动态缓存和转发算法的联合设计和优化这一核心问题尚未得到深入的研究。该项目将研究缓存和转发的基本限制，以及设计实用且稳健的算法，以优化以数据为中心的内容交付中带宽和存储的使用。与许多现有的针对静态缓存的集中式算法的工作不同，该项目将开发可扩展的、分布式的、动态的算法，可以在不断变化的内容、用户需求和网络条件下解决大规模的缓存和转发问题。为了实现这一目标，该项目将采取两种互补的方法。第一种方法是基于pi最近开发的分布式缓存和转发的随机模型。这种方法大大扩展了经典的基于背压的路由技术，在统一的框架内合并缓存，从而产生新的算法，最大限度地满足网络的用户需求率。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分布式节点的基础上进行，以最小化延迟等全局成本目标。本项目解决了实际和理论问题，主要包括以下几个重点：(1)设计联合优化的转发和缓存算法，以最小化延迟；(2)设计可扩展的、鲁棒的、分层的动态缓存和转发算法，并根据动态调整的名称解析进行操作；(3)开发将动态缓存和转发与拥塞控制相结合的算法，以提高公平性和性能；(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，并能够研究缓存和转发的基本性能限制；(5)开发低复杂度、动态转发和缓存算法，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弹性；(6)开发用于查询和缓存的算法，从而在感知环境中做出最优决策。这项工作的广泛意义将包括：(1)对国家安全、商业企业、科学探索和研究、卫生服务和其他重要社会项目中使用的大数据应用的网络架构产生直接和长期的影响；(2)通过“以数据为中心的网络的理论和算法”的计划课程部分以及涉及测试平台调查和验证的积极实践项目，对本科和研究生教育的影响，特别强调女性和少数民族学生的参与；(3)积极与其他大学院系、政府研究机构和业界合作，加强研究和教育的基础设施；(4)广泛传播，通过参加多学科会议和讲习班，以及接触更广泛的媒体，加强对科学和技术的了解。