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.

诸如网络搜索（例如，Google和Bing）、社交网络（例如，Facebook）和在线视频流（例如，Netflix和YouTube）现在是一项重要的人类活动。这些服务的基础是由各种实体控制的服务器、网络和软件的庞大且日益复杂的系统，所述各种实体诸如拥有或生成内容的内容提供商（CP）、将该内容分发给用户的内容分发网络（CDN）以及拥有并操作基础网络基础设施的互联网服务提供商（ISP）。这些实体形成了内容分发生态系统，每个实体都有自己的商业利益，每个实体都试图优化自己的成本和性能，通常是单独进行，但有时会与其他实体合作。该项目的重点是了解这些不同实体之间的复杂交互，以期提供更好的架构解决方案来促进这些交互。从该项目中获得的见解可以帮助指导未来互联网服务的发展，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供更高的系统效率。该项目提出了一个框架，从多个角度研究内容分发生态系统中的复杂交互-控制信息，决策点，时间尺度和本地与全局决策，并应用该框架来了解它们对用户QoE的作用和影响。该项目沿着四个主线组织：1）CDN和ISP之间的复杂交互：描述CDN操作（如内容适配和请求路由）对ISP操作（如流量工程和流量整形）的影响; 2）CP和CDN之间的复杂交互：描述CP操作（如选择合适的CDN来交付其内容）与CDN操作（如服务器选择）的特征; 3）（单路径和多路径）TCP与应用程序之间的复杂交互：表征应用程序（如视频流）中存在的控制环路与协议（如（MP）TCP）之间的交互; 4）测量、跟踪驱动评估以及受控和“野外”实验。所有四个线程都通过一个分析推力连接在一起，该推力专注于理论模型的开发，先进的算法，以及基于优化，控制和经济方法的性能界限。该项目背后的主要前提是，随着实体数量的增加和它们之间的各种交互，内容分发变得越来越复杂。这种复杂性需要新的分析和算法方法来建模，理解，控制和优化内容分发。该项目迈出了开发这些工具和方法的第一步，并将其应用于CP，CDN和ISP之间以及不同应用级和传输级数据传输之间的交互。 如果成功，该项目将为有效改善系统性能和提高整体用户QoE提供良好的理论基础和通用设计指南，并使CP，CDN和ISP受益。该项目的成功完成有望提高内容分发生态系统和整个互联网基础设施的经济可行性。 此外，它将导致更好地理解如何设计由多种不同的交互组件组成的大型系统。该项目的教育活动将加强本科生，研究生和专业教育，这些教育处于网络，建模，控制理论和复杂系统的交叉点。PI将通过以下方式实现这些目标：1）创建适合高年级和研究生的复杂系统，内容分发系统和交互系统数学的课程和课程材料; 2）开展旨在增加本科女性和少数民族学生参与研究以及与更广泛的网络社区接触的外联活动。

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

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

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