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.

清晰可见网络中的两个基本趋势。 首先，当今的大部分网络流量及其预计的巨大增长，主要包括传播给多个用户的内容。 其次，在具有动态和不可靠的通道条件的移动无线环境中，越来越多地访问网络内容。最初是针对静态有线网络上点对点通信设计的传统网络协议根本适用于这种情况。由于这些趋势的启发，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），以在不断变化的网络条件下进行有效且可靠的内容传播。该项目基于数据中心网络中的最新积极研究工作，该项目将信息内容放置在网络架构中心，而不是源源代替网络架构中心。 尽管以数据为中心的网络研究取得了许多重要结果，但动态缓存和转发算法的联合设计和优化的核心问题尚未得到彻底研究。 该项目将研究缓存和转发的基本限制，以及实用和健壮算法的设计，以优化以数据为中心的内容传递中的带宽和存储。 与许多用于静态缓存的集中算法的现有作品不同，该项目将开发可扩展的，分布式的，动态的算法，这些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。为了实现此目标，该项目将采用两种互补的方法。 第一种方法是基于用于PIS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，以将缓存纳入统一框架中，从而导致新算法，从而最大程度地利用了网络满足的用户需求率。 第二种方法是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是按分布式节点逐节点进行的，以最大程度地减少诸如延迟之类的全球成本目标。该项目解决了实用和理论问题，并构成了以下主要力量： （2）可扩展，稳健，层次的动态缓存和转发算法的设计，这些算法以动态调整的名称分辨率运行； （3）将动态缓存和转发的算法的开发与公平性和增强性能的拥塞控制相结合； （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，以及能够研究缓存和转发中基本性能限制的研究； （5）开发低复杂性，动态转发和缓存算法，在移动无线环境中，对多用户干扰的用户延迟以及对多用户干扰的弹性更大； （6）开发用于查询和缓存的算法，从而导致在感应环境中实现最佳决策。这项工作的更广泛意义将包括：（1）对国家安全，商业企业，科学企业，科学勘探和研究，卫生服务，卫生服务，其他重要社会项目的大数据应用程序对网络架构的直接和长期影响； （2）对本科和研究生教育的影响，特别是通过计划的“以数据为中心网络的理论和算法”以及涉及测试床位调查和验证的主动动手项目的计划课程，并特别着重于女性和少数群体学生； （3）通过与其他大学部门，政府研究机构和行业积极合作，增强研究和教育的基础设施； （4）广泛的传播，以通过参与多学科会议和讲习班以及对更广泛的媒体的影响来增强科学和技术理解。