NeTS: Large: Collaborative Research: Complex Interactions in the Content Distribution Ecosystem

NeTS：大型：协作研究：内容分发生态系统中的复杂交互

• 批准号: 1413998
• 负责人: Donald Towsley
• 金额: $ 173.46万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1413998&HistoricalAwards=false
• 关键词: NeTS; Large; Collaborative; Research; Complex

Internet services such as web search (e.g, Google and Bing), social networks (e.g., Facebook), and online video streaming (e.g., Netflix and YouTube) are now an important human activity. Underlying such services is a vast, and increasingly complex system of servers, networks, and software controlled by a variety of entities such as content providers (CPs) who own or generate the content, content distribution networks (CDNs) who distribute that content to users, and Internet Service Providers (ISPs) who own and operate the underlying network infrastructure. These entities form the content distribution ecosystem, each with its own business interest, each attempting to optimize its own cost and performance, more often individually, but sometimes in cooperation with others. This project is focused on understanding the complex interactions among these different entities with the view of providing better architectural solutions to facilitate those interactions. Insights gained from the project can help guide the evolution of future Internet services, resulting in better quality-of-experience (QoE) for the users and greater system efficiencies for the entities in the ecosystem. This project advances a framework to study complex interactions in the content distribution ecosystem from multiple perspectives -- control information, decision points, time scales and local vs. global decision making, and applies this framework to understand their roles and effects on user QoE. The project is organized along four main threads: 1) Complex interactions between CDNs and ISPs: Characterize effects of CDN actions such as content adaptation and request routing on ISP actions such as traffic engineering and traffic shaping; 2) Complex interactions between CPs and CDNs: Characterize CP actions such as choosing the right CDN to deliver their content with CDN actions such as server selection; 3) Complex interactions between (uni- and multi-path) TCP and applications: Characterize interactions between control loops present in applications such as video streaming and protocols such as (MP)TCP; 4) Measurement, trace-driven evaluation, and controlled and "in the wild" experimentation. All four threads are connected by an analytical thrust focused on the development of theoretical models, advanced algorithms, and performance bounds based on optimization, control and economic methods.The main premise behind this project is that content distribution is becoming more complex with increasing numbers of entities and varied interactions between them. This complexity requires new analytical and algorithmic methods to model, understand, control, and optimize content distribution. The project takes a first step towards developing these tools and methods, and applying them to interactions that arise between CPs, CDNs, and ISPs, and between different application-level and transport level data transfers. If successful, the project will produce sound theoretical underpinnings and provide general design guidelines in effectively improving the system performance and enhancing overall user QoE and benefit CPs, CDNs and ISPs alike.The successful completion of this project promises to enhance the economic viability of the content distribution ecosystem and the Internet infrastructure at large. Moreover, it will lead to a better understanding of how to engineer large systems consisting of a multitude of diverse interacting components. Educational activities for this project will enhance undergraduate, graduate, and professional education lying at the intersection of networking, modeling, control theory, and complex systems. The PIs will accomplish these goals through 1) creation of courses and course material on complex systems, content distribution systems, and mathematics of interacting systems appropriate for seniors and graduate students; 2) development of outreach activities aimed at increasing participation of undergraduate women and minority students in research as well as engagement with the broader networking community.

网络搜索（例如，Google和Bing），社交网络（例如Facebook）和在线视频流（例如Netflix和YouTube）等互联网服务现在是一项重要的人类活动。基础服务是由拥有或生成内容的内容提供商（CPS）控制的服务器，网络和软件的广泛且日益复杂的系统，他们拥有或生成内容，内容分发网络（CDN），这些内容，将其内容分配给用户以及拥有和运营基础网络基础构建的Internet服务提供商（ISP）。这些实体形成了内容分布生态系统，每个实体都有自己的商业利益，每个人都试图优化其自身的成本和绩效，更经常地单独，但有时与他人合作。该项目的重点是理解这些不同实体之间的复杂互动，以提供更好的建筑解决方案来促进这些相互作用。从项目中获得的见解可以帮助指导未来的互联网服务的发展，从而为用户提供更好的体验质量（QOE），并为生态系统中的实体提高系统效率。该项目从多个角度（控制信息，决策点，时间范围和本地决策与全球决策制定）研究了一个框架，以研究内容分布生态系统中的复杂交互作用，并应用此框架来了解其对用户QoE的角色和影响。该项目沿着四个主要线程进行组织：1）CDN和ISP之间的复杂交互作用：表征CDN动作的影响，例如内容适应和请求路由ISP操作，例如流量工程和流量塑造； 2）CPS和CDN之间的复杂交互：表征CP操作，例如选择合适的CDN来使用CDN操作（例如服务器选择）提供其内容； 3）（UNI-和多路径）TCP与应用程序之间的复杂相互作用：表征应用程序中存在的控制循环之间的相互作用，例如视频流和（MP）TCP等协议； 4）测量，痕量驱动的评估以及在野外进行控制和“在野外”实验。所有四个线程均通过重点是基于优化，控制和经济方法的理论模型，高级算法和性能界限的分析推力连接。该项目的主要前提是，随着实体数量的增加，内容分布变得越来越复杂，并且之间的相互作用变化了。这种复杂性需要新的分析和算法方法来建模，理解，控制和优化内容分布。该项目迈出了开发这些工具和方法的第一步，并将它们应用于CP，CDN和ISP之间以及不同应用程序级别和传输级别数据传输之间的交互。 如果成功的话，该项目将产生合理的理论基础，并提供一般设计指南，以有效地改善系统性能并增强整体用户QOE和受益CPS，CDN和ISPS，以及该项目的成功完成，有望增强内容分配生态系统和互联网基础设施的经济可行性。 此外，它将更好地理解如何设计由多种相互作用组件组成的大型系统。该项目的教育活动将增强在网络，建模，控制理论和复杂系统的交汇处的本科，研究生和专业教育。 PI将通过1）创建适合老年人和研究生的相互作用系统的复杂系统，内容分配系统和数学的课程和课程材料来实现这些目标； 2）开发旨在增加本科妇女和少数族裔学生研究以及与更广泛的网络社区的参与的宣传活动。

项目成果

Combining Renewable Solar and Open Air Cooling for Greening Internet-Scale Distributed Networks

结合可再生太阳能和露天冷却，实现互联网规模分布式网络的绿色化

• DOI: 10.1145/3307772.3328307
• 发表时间: 2019
• 期刊: Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy'19
• 作者: Gupta, Vani;Shenoy, Prashant;Sitaraman, Ramesh K.
• 通讯作者: Sitaraman, Ramesh K.

From Theory to Practice: Improving Bitrate Adaptation in the DASH Reference Player

• DOI: 10.1145/3336497
• 发表时间: 2019-08-01
• 期刊: ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
• 影响因子: 5.1
• 作者: Spiteri, Kevin;Sitaraman, Ramesh;Sparacio, Daniel
• 通讯作者: Sparacio, Daniel

Learning from Optimal: Energy Procurement Strategies for Data Centers

• DOI: 10.1145/3307772.3328308
• 发表时间: 2019-06
• 期刊: Proceedings of the Tenth ACM International Conference on Future Energy Systems
• 作者: Sohaib Ahmad;Arielle Rosenthal;M. Hajiesmaili;R. Sitaraman

BOLA: Near-Optimal Bitrate Adaptation for Online Videos

• DOI: 10.1109/tnet.2020.2996964
• 发表时间: 2016-01
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Kevin Spiteri;Rahul Urgaonkar;R. Sitaraman