Collaborative Research: CNS Core: Medium: Cross-Layer Design of Video Analytics for the Internet of Things

合作研究：CNS 核心：媒介：物联网视频分析的跨层设计

• 批准号: 1955422
• 负责人: Minlan Yu
• 金额: $ 37.5万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955422&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

The emergence of the Internet of Things (IoT) enables many new applications ranging from augmented reality and self-driving cars, to surveillance and cashier-less retail stores. These applications continuously collect video streams from IoT devices, such as sensors, cameras, and radars. They aim to understand the video content to make intelligent decisions, by running sophisticated video analytics tasks, such as counting people and recognizing license plates in the video streams. These video analytics tasks often run a collection of computing resources including IoT devices, edge clusters near the devices and the remote cloud, connected through networks with dynamic bandwidth and latency. This project will enable a high-performance video analytics framework that can support a variety of IoT applications in real-time, with high accuracy, and at scale. The key idea of this project is to enable video analytics for IoT devices by joint optimizations across application, computing, and networking. Today’s solutions often focus on separated optimization, which leads to inaccurate answers to analytical queries, inefficient use of computing resources, and performance degrades when network condition changes. This project's video analytics framework will (1) leverage both network layer information and physical information to tune the parameters in video analytics, in order to optimize task accuracy, instead of network bandwidth, latency or quality of experience, (2) allocate computing resources for analytics tasks to meet multi-dimensional task-level service-level objectives with distributed time tracking and runtime scheduling, and (3) redesign video analytics and encoding algorithms by considering the network and computing constraints. This project will build and test representative video analytics applications on top of the system to demonstrate its capability. The project will facilitate the interactions between the machine learning research community and the systems/networking research community, and result in novel algorithms and efficient networked systems for video analytics. The project will also engage underrepresented groups and undergraduates in research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

物联网（IoT）的出现使许多新的应用程序范围从增强现实和自动驾驶汽车到监视和无现金零售商店。这些应用程序继续从物联网设备（例如传感器，相机和雷达）收集视频流。他们的目的是通过运行复杂的视频分析任务，例如计算人员并在视频流中识别车牌来理解视频内容以做出智能决策。这些视频分析任务通常会运行一系列计算资源集合，包括物联网设备，设备附近的边缘群集以及远程云，通过具有动态带宽和延迟的网络连接。该项目将启用一个高性能的视频分析框架，该框架可以实时以高准确性和大规模支持各种物联网应用程序。该项目的关键思想是通过在应用，计算和网络之间进行联合优化来启用IoT设备的视频分析。当今的解决方案通常集中在分离的优化上，这会导致分析查询的答案不准确，当网络条件变化时，计算资源的使用效率低下以及性能降低。该项目的视频分析框架（1）将（1）利用网络层信息和物理信息来调整视频分析中的参数，以优化任务准确性，而不是网络带宽，延迟或经验质量，（2）计算资源分析资源，以使分析任务与多维任务级别的分析时间和运行时间范围，并满足分布式时间表的分布时间和运行时间（3）通过考虑网络和计算约束来编码算法。该项目将构建和测试代表系统之上的视频分析应用程序，以证明其功能。该项目将促进机器学习研究社区与系统/网络研究社区之间的相互作用，并为视频分析提供新颖的算法和有效的网络系统。该项目还将参与研究不足的团体和本科生的研究。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估而被视为珍贵的。

项目成果

Carbink: Fault-Tolerant Far Memory

• 发表时间: 2022
• 作者: Yang Zhou;Hassan Wassel;Sihang Liu;Jiaqi Gao;James Mickens;Minlan Yu;Chris Kennelly;Paul Turner;David E. Culler;Henry M. Levy;A. Vahdat

MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles

• DOI: 10.23919/date56975.2023.10137246
• 发表时间: 2021-05
• 期刊: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)
• 作者: Yu-Shun Hsiao;Zishen Wan;Tianyu Jia;Radhika Ghosal;A. Raychowdhury;D. Brooks;Gu-Yeon Wei;V. Reddi

A throughput-centric view of the performance of datacenter topologies

• DOI: 10.1145/3452296.3472913
• 发表时间: 2021-08
• 期刊: Proceedings of the 2021 ACM SIGCOMM 2021 Conference
• 作者: Pooria Namyar;Sucha Supittayapornpong;Mingyang Zhang;Minlan Yu;R. Govindan

Jaqen: A High-Performance Switch-Native Approach for Detecting and Mitigating Volumetric DDoS Attacks with Programmable Switches

• 发表时间: 2021
• 作者: Zaoxing Liu;Hun Namkung;G. Nikolaidis;Jeongkeun Lee;Changhoon Kim;Xin Jin;V. Braverman;Minlan Yu-Minlan

Xatu: boosting existing DDoS detection systems using auxiliary signals

• DOI: 10.1145/3555050.3569121
• 发表时间: 2022-11
• 期刊: Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies
• 作者: Zhiying Xu;Sivaramakrishnan Ramanathan;Alexander M. Rush;J. Mirkovic;Minlan Yu