NGNI-Medium: Collaborative Research: MUNDO: Managing Uncertainty in Networks with Declarative Overlays

NGNI-Medium：协作研究：MUNDO：使用声明性覆盖管理网络中的不确定性

• 批准号: 1318441
• 负责人: Carlos Guestrin
• 金额: $ 16.36万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-31 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318441&HistoricalAwards=false
• 关键词: NGNI; Medium; Collaborative; Research; MUNDO

We are entering an Industrial Revolution in the production of information. While in the past data was "handmade" by typing on keyboards, today data are increasingly manufactured by machines: sensors, cameras, software logs, etc. When harnessed in a timely manner, these data can have significant positive impact in many contexts, including early warning and rapid response in natural disasters, air quality monitoring, and improved Internet security. To provide useful information in these contexts, computers in multiple locations must coordinate over networks, because the data are both widely distributed and massive, and cannot be "warehoused" at a single location in a timely manner. Worse, sensor data is typical "noisy" or erroneous in various ways, so statistical methods must be employed to convert the raw "evidence" data into probabilistically reliable information. In this project we develop new techniques to integrate statistical inference methods from AI with overlay network algorithms developed for peer-to-peer and wireless settings. We design new overlay network algorithms customized for distributed inference. We also develop network-aware inference algorithms that can trade off inference approximation quality for communication efficiency and robustness to network failure. Finally, we explore the use of a high-level declarative language for programming both the networking and inference logic. The high-level language enables us to investigate compilation techniques to co-optimize the inference and overlay network tasks for maximal utility. We prototype and evaluate our ideas via open-source implementations deployed on testbeds like Emulab and Planetlab. Software and research papers are disseminated at http://declarativity.net.

我们正在进入信息生产的工业革命。过去，数据是通过键盘打字“手工制作”的，而如今，数据越来越多地由机器制造：传感器、摄像头、软件日志等。如果及时利用，这些数据可以在许多情况下产生重大的积极影响，包括自然灾害预警和快速反应、空气质量监测、提高互联网安全。为了在这些环境中提供有用的信息，多个位置的计算机必须通过网络进行协调，因为数据分布广泛且数量庞大，并且无法及时“存储”在单个位置。更糟糕的是，传感器数据在各种方面都是典型的“嘈杂”或错误，因此必须采用统计方法将原始“证据”数据转换为概率上可靠的信息。在这个项目中，我们开发了新技术，将人工智能的统计推断方法与为点对点和无线设置开发的覆盖网络算法相集成。我们设计了专为分布式推理定制的新覆盖网络算法。我们还开发了网络感知推理算法，可以权衡推理近似质量以换取通信效率和对网络故障的鲁棒性。最后，我们探索使用高级声明性语言对网络和推理逻辑进行编程。高级语言使我们能够研究编译技术，以共同优化推理和覆盖网络任务，以获得最大效用。我们通过部署在 Emulab 和 Planetlab 等测试平台上的开源实现来原型化并评估我们的想法。软件和研究论文在 http://declarativity.net 上传播。