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

自40多年前其最早的技术基础发展以来，S主导的互联网通信范式一直是基于分组的、主机到主机的通信。然而，随着Internet的成熟，在这种通信抽象之上开发了越来越多的应用程序，人们越来越认识到，许多用户应用程序主要关注访问内容，而不是与特定主机通信。在这种以内容为中心的观点中，重点放在获得什么上，而不是从哪里获得，因此内容搜索、传播(路由)和存储变得更加重要。事实上，互联网体系结构的几种“从头开始”的方法都强调网络内内容命名、搜索、路由和存储(包括网络内缓存)是下一代互联网体系结构的关键体系结构组件。该项目致力于开发建模和性能评估工具/方法，以及设计和评估这些以内容为中心的网络体系结构的关键体系结构元素--动态的、需求驱动的、网络内的内容缓存--的方法。这项工作将基于内容请求流的表征、使用随机绑定技术的概率边界性能以及基于减少负载近似技术的缓存网络的近似性能模型来开发缓存网络的边界确定性性能模型。该研究还将研究用于内容缓存和内容定位的几种简单的尽力而为算法；这里，重点将放在底层方法本身，而不是它们在任何特定的以内容为中心的网络体系结构中的实施。更广泛的影响。网内缓存的建模和分析--许多以内容为中心的下一代网络体系结构的组件--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少负载近似作为复杂排队和阻塞网络的边界和近似分析的基础非常相似，这些复杂排队和阻塞网络已被用来对广泛的分组交换和电路交换网络及其协议进行建模。该项目对特定的简单、“尽力而为”的内容缓存和内容定位算法的研究是基于这样一种信念，即就像尽力而为的互联网服务模型已被证明与更复杂的网络体系结构相比“足够好”一样，与更有状态和更复杂的请求路由和缓存内容管理方法相比，尽力而为的缓存可能同样被证明“足够好”。这将是一个影响深远的教训。研究生研究助理和REU本科生将作为本项目的一部分接受指导，帮助培养下一代网络研究人员。少数族裔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。研究成果将被纳入马萨诸塞大学教授的一门广为传播的研究生网络课程。

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.