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

我们正在进入一场信息生产的工业革命。过去的数据是通过键盘输入“手工制作”的，而今天的数据越来越多地由机器制造：传感器、相机、软件日志等。如果及时加以利用，这些数据可以在许多情况下产生重大的积极影响，包括自然灾害的早期预警和快速反应、空气质量监测和改善互联网安全。为了在这些环境中提供有用的信息，位于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巨大，不能及时地在单个位置“存储”。更糟糕的是，传感器数据在各种方面都是典型的“噪声”或错误，因此必须采用统计方法将原始“证据”数据转换为概率可靠的信息。在这个项目中，我们开发了新的技术，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集成在一起。我们为分布式推理设计了新的覆盖网络算法。我们还开发了网络感知推理算法，可以在通信效率和网络故障鲁棒性方面权衡推理近似质量。最后，我们探讨了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，以获得最大的效用。我们通过在Emulab和Planetlab等测试平台上部署的开源实现来原型化和评估我们的想法。软件和研究论文可在http://declarativity.net上发布。

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.