NeTS Small: Analysis and Design of Best-Effort Content-Caching Networks

NeTS Small：尽力而为内容缓存网络的分析和设计

• 批准号: 1117764
• 负责人: Sridhar Mahadevan
• 金额: $ 30万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117764&HistoricalAwards=false
• 关键词: NeTS; Small; Analysis; Design; Best

Since the development of its earliest technical foundations more than 40 years ago, the Internet?s dominant communication paradigm has been packet-based, host-to-host communication. However, as the Internet has matured and increasingly more applications have been developed on top of this communication abstraction, there is a growing realization that many user applications are primarily concerned with accessing content rather than communicating with a specific host. In this content-centric view, emphasis is placed on what is obtained rather than from where it is obtained, and content search, dissemination (routing), and storage are consequently of increased importance. Indeed, several 'clean slate' approaches towards Internet architecture have emphasized in-network content naming, search, routing, and storage (including in-network caching ) as key architectural components of a next-generation Internet architecture.Intellectual Merit. This project undertakes fundamental research on developing the modeling and performance evaluation tools/methodologies, and on designing and evaluating approaches for a key architectural element of these content-centric network architectures - dynamic, demand-driven, in-network, content caching. This effort will develop bounding deterministic performance models of caching networks based on a -characterization of content request streams, probabilistic bounds performance using stochastic bounding techniques, and approximate performance models for networks of caches based on reduced-load approximation techniques. The research will also investigate several simple best-effort algorithms for content-caching and content-location; here, the focus will be on the underlying approaches themselves rather than their embodiment in any particular content-centric network architecture. Broader Impact. The modeling and analysis of in-network caching - a component of many content-centric next generation network architectures - will provide tools and techniques for analyzing such networks in much the same way that network calculi and reduced-load approximations have served as foundations for bounding and approximate analyses of complex queueing and blocking networks that have been used to model a wide range of packet-switched and circuit-switched networks and their protocols. The project's investigation of specific simple, 'best effort' content-caching and content-location algorithms is based on the belief that just as a best-effort Internet service model has proven to be 'good enough' compared with more sophisticated network architectures, best-effort caching may similarly prove 'good enough' when compared to more stateful and more complex request routing and cache-content management approaches. This would be a lesson with far-reaching impact. Graduate research assistants and undergraduate REU students will be mentored as part of this project, helping to educate the next generation of networking researchers. Involvement of minority graduate students will be coordinated through the Northeast Alliance for Graduate Education and the Professoriate (NEAGEP). Research results will be adopted into a widely-disseminated graduate networking course taught at the University of Massachusetts.

自从40多年前发展出最早的技术基础以来，互联网？的主要通信范例是基于分组的主机到主机通信。然而，随着互联网的成熟和越来越多的应用程序已经开发了这种通信抽象的顶部，有一个越来越多的认识到，许多用户应用程序主要关注的是访问内容，而不是与特定的主机通信。在这种以内容为中心的观点中，重点放在获得什么而不是从哪里获得，因此内容搜索，传播（路由）和存储变得越来越重要。事实上，几个“白板”的方法对互联网架构强调在网络内容命名，搜索，路由和存储（包括在网络缓存）作为下一代互联网架构的关键架构组件。该项目进行基础研究，开发建模和性能评估工具/方法，并设计和评估这些以内容为中心的网络架构的关键架构元素的方法-动态，需求驱动，在网络中，内容缓存。这项工作将开发边界确定性的性能模型的基础上的内容请求流，概率边界的性能使用随机绑定技术，和近似的性能模型的基础上减少负载近似技术的网络缓存的缓存网络的表征。 该研究还将调查几个简单的尽力而为的内容缓存和内容定位算法;在这里，重点将放在底层的方法本身，而不是在任何特定的内容为中心的网络架构中的实施例。更广泛的影响。 网内缓存是许多以内容为中心的下一代网络体系结构的组成部分，其建模和分析将提供用于分析此类网络的工具和技术，其方式与网络演算和减载近似作为复杂网络和阻塞网络的边界和近似分析的基础（已用于对各种分组交换和电路交换网络进行建模）大致相同。交换网络及其协议。 该项目的调查具体的简单，“尽力而为”的内容缓存和内容定位算法是基于这样的信念，即正如一个尽力而为的互联网服务模型已被证明是“足够好”相比，更复杂的网络架构，尽力而为的缓存可能同样证明“足够好”相比，更有状态和更复杂的请求路由和缓存内容管理方法。这将是一个影响深远的教训。研究生研究助理和本科REU学生将作为该项目的一部分进行指导，帮助教育下一代网络研究人员。少数民族研究生的参与将通过东北研究生教育和教授联盟（NEAGEP）进行协调。研究成果将被采纳到马萨诸塞州大学教授的一个广泛传播的研究生网络课程中。