US Ignite: Collaborative Research: Focus Area 2: Resilient Virtual Path Management for Scalable Data-intensive Computing at Network-Edges

US Ignite：协作研究：重点领域 2：网络边缘可扩展数据密集型计算的弹性虚拟路径管理

• 批准号: 1647084
• 负责人: Flavio Esposito
• 金额: $ 20.38万
• 依托单位: Saint Louis University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1647084&HistoricalAwards=false
• 关键词: US; Ignite; Collaborative; Research; Focus

This project addresses bootstrapping and managing virtual path networks hosting Visual Cloud Computing (VCC) applications for public safety during disaster incidents. This project meets the needs of incident-supporting VCC applications that are essential for first responders to gain rapid visual situational awareness through: (a) scalable processing of imagery/video data collected at incident scenes, and (b) use of thin-client technologies for collaboration. Targeted demonstrations will also be conducted in collaboration with first-responder agencies such as trauma care hospitals (with theater-scale use cases) and a county fire protection district (with regional-scale use cases). Specifically, the project objectives are to: (i) investigate virtual path management algorithms and large-scale simulations with the aim of providing resilient VCC applications with guaranteed performance even in the presence of host/link failures, host mobility and network partitioning; and (ii) demonstrate resilient resource provisioning for such path management algorithms within hybrid fog-cloud infrastructures on experimental testbeds such as GENI and CloudLab.The salient theoretical contribution of this project lays in the design of novel distributed and federated algorithms for the virtual path embedding and computation placement problems, for real-time VCC applications. In particular, the project seeks a solution improvement for constrained path finders schemes, e.g., virtual network embedding or traffic steering, in presence of severe host and link failures, host mobility and network partitioning. The work also advances knowledge of application-aware network design through a scheme that allows fog-cloud compute location selection tradeoffs. In particular, the approach is based on decoupling small/large instances of visual data processing in the context of latency-critical VCC applications at: (a) "theater-scale" for first-responder and incident commander personnel co-ordination at an incident scene, and (b) "regional-scale" for tracking objects of interest across different geographical scales in wide-area motion imagery from airborne platforms.

该项目致力于引导和管理托管视觉云计算 (VCC) 应用程序的虚拟路径网络，以确保灾难事件期间的公共安全。 该项目满足事件支持 VCC 应用程序的需求，这些应用程序对于急救人员通过以下方式获得快速视觉态势感知至关重要：(a) 对事件现场收集的图像/视频数据进行可扩展处理，以及 (b) 使用瘦客户端技术为了合作。还将与急救机构合作进行有针对性的演示，例如创伤护理医院（具有剧院规模的用例）和县消防区（具有区域规模的用例）。 具体来说，该项目的目标是：（i）研究虚拟路径管理算法和大规模模拟，旨在提供弹性 VCC 应用程序，即使在存在主机/链路故障、主机移动性和网络分区的情况下也能保证性能； (ii) 在 GENI 和 CloudLab 等实验测试平台上展示混合雾云基础设施中此类路径管理算法的弹性资源配置。该项目的显着理论贡献在于设计用于虚拟路径嵌入的新型分布式和联合算法和计算布局问题，适用于实时 VCC 应用。特别是，该项目寻求针对受限路径查找方案的解决方案改进，例如，在存在严重主机和链路故障、主机移动性和网络分区的情况下，虚拟网络嵌入或流量引导。这项工作还通过允许雾云计算位置选择权衡的方案，增进了应用程序感知网络设计的知识。特别是，该方法基于在延迟关键的 VCC 应用程序中解耦小型/大型视觉数据处理实例：(a) “战区规模”，用于事件中的急救人员和事件指挥官人员的协调场景，以及（b）“区域尺度”，用于跟踪来自机载平台的广域运动图像中不同地理尺度的感兴趣对象。

项目成果

Reunifying Families after a Disaster via Serverless Computing and Raspberry Pis

通过无服务器计算和 Raspberry Pi 帮助灾后家庭团聚

• DOI: 10.1109/lanman.2018.8475098
• 发表时间: 2018-06
• 期刊: 2018 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN
• 作者: Franz, Justin;Nagasuri, Tanmayi;Wartman, Andrew;Ventrella, Agnese V.;Esposito, Flavio
• 通讯作者: Esposito, Flavio

A Distributed Orchestration Algorithm for Edge Computing Resources with Guarantees

一种有保证的边缘计算资源分布式编排算法

• DOI: 10.1109/infocom.2019.8737532
• 发表时间: 2019-04-01
• 期刊: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
• 作者: Gabriele Castellano;Flavio Esposito;Fulvio Risso
• 通讯作者: Fulvio Risso

ADELE: An Architecture for Steering Traffic and Computations via Deep Learning in Challenged Edge Networks

ADELE：在面临挑战的边缘网络中通过深度学习引导流量和计算的架构

• DOI: 10.1109/cccs.2019.8888120
• 发表时间: 2019-10
• 期刊: Communications and Security (ICCCS
• 作者: Gaballo, Alessandro;Flocco, Matteo;Esposito, Flavio;Marchetto, Guido
• 通讯作者: Marchetto, Guido

Catena: A distributed architecture for robust service function chain instantiation with guarantees

Catena：一种具有保证的稳健服务功能链实例化的分布式架构

• DOI: 10.1109/netsoft.2017.8004103
• 发表时间: 2017-07
• 期刊: IEEE Conference on Network Softwarization (NetSoft
• 作者: Esposito; Flavio
• 通讯作者: Flavio

Sustainable Task Offloading in UAV Networks via Multi-Agent Reinforcement Learning

通过多智能体强化学习实现无人机网络中的可持续任务卸载

• DOI: 10.1109/tvt.2021.3074304
• 发表时间: 2021-05-01
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Alessio Sacco;Flavio Esposito;Guido Marchetto;P. Montuschi
• 通讯作者: P. Montuschi