CAREER: A New Efficient and Cooperative Large-Scale Distributed Data Sharing System

CAREER：新型高效协作的大规模分布式数据共享系统

• 批准号: 1254006
• 负责人: Haiying Shen
• 金额: $ 40万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1254006&HistoricalAwards=false
• 关键词: CAREER; New; Efficient; Cooperative; Large

This project investigates how information from social networks may be used to create efficient and cooperative large-scale distributed data sharing systems. Two of the challenges facing such systems are locating data quickly and cost-efficiently, and enforcing cooperative node behaviors. Social network-based approaches seek to build social networks into data sharing systems in order to leverage the real-world friend properties of mutual trust and common interest. Although these approaches can significantly reduce cost and complexity compared to purely technical approaches, current methods only make use of a very superficial level of social network properties, and cannot support the broad goal of enabling all nodes to share data freely and efficiently. This project addresses these shortcomings through more fully leveraging social network properties, and coordinating social network-based approaches with technical approaches. Areas of investigation include infrastructures for data searching, cooperation enhancement mechanisms, and algorithms for data server selection. Today, a multitude of large-scale distributed systems use the Internet to deliver a variety of data -- such as software and audio/video content -- to end users. These systems support many social, commercial, and cultural activities. Millions of dollars are spent on commercial servers to deliver this data. Peer-to-peer technology, in which user computers cooperate to share content between themselves, has potential for doing this cheaper and faster. However, wider use of peer-to-peer technology is held back by several limitations. One is that nodes may misbehave, selfishly receiving content but not contributing in return, or distributing corrupted or malicious content into the system, to be further spread by unsuspecting users. Another limitation is that locating content is difficult, due to the wide distribution of nodes and the lack of a central index. This project seeks to address these limitations by making use of information obtained from social networks. Potential benefits of the work include savings in the costs of infrastructure and energy consumption for data distribution. The project also provides educational opportunities for graduate and undergraduate students, and collaborates with other educational programs for under-represented student recruitment, and outreach to K-12 students.

该项目研究如何使用来自社交网络的信息来创建高效且协作的大规模分布式数据共享系统。此类系统面临的两个挑战是快速且经济高效地定位数据，以及强制执行协作节点行为。基于社交网络的方法寻求将社交网络构建到数据共享系统中，以利用现实世界中相互信任和共同兴趣的朋友属性。尽管与纯技术方法相比，这些方法可以显着降低成本和复杂性，但目前的方法仅利用了社交网络属性的非常肤浅的水平，并且无法支持使所有节点自由有效地共享数据的广泛目标。该项目通过更充分地利用社交网络属性并将基于社交网络的方法与技术方法相协调来解决这些缺点。研究领域包括数据搜索基础设施、合作增强机制和数据服务器选择算法。如今，许多大型分布式系统使用互联网向最终用户提供各种数据，例如软件和音频/视频内容。这些系统支持许多社会、商业和文化活动。数百万美元花费在商业服务器上来提供这些数据。点对点技术使用户计算机相互协作共享内容，有可能以更便宜、更快的速度实现这一点。然而，点对点技术的更广泛使用受到一些限制的阻碍。一是节点可能行为不当，自私地接收内容但不做出回报，或者将损坏或恶意的内容分发到系统中，由毫无戒心的用户进一步传播。另一个限制是，由于节点分布广泛且缺乏中心索引，定位内容很困难。该项目旨在通过利用从社交网络获得的信息来解决这些限制。这项工作的潜在好处包括节省基础设施成本和数据分发的能源消耗。该项目还为研究生和本科生提供教育机会，并与其他教育项目合作招收代表性不足的学生，并向 K-12 学生进行推广。