CNS Core: Small: Collaborative: Coalescent Computing - New Theory, Mechanism and Platform for Adaptive Edge Computing

CNS 核心：小型：协作：合并计算 - 自适应边缘计算的新理论、机制和平台

• 批准号: 1909077
• 负责人: Shigang Chen
• 金额: $ 28.83万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909077&HistoricalAwards=false
• 关键词: CNS; Core; Small; Collaborative; Coalescent

By moving computation to the edge of the Internet, the emerging edge computing model promises to reduce application response time, improve user experience, save bandwidth cost, and enhance data privacy. This project will establish a new edge-computing theory called coalescent computing, where each application program runs on an integrated virtual computing system, consisting of resources from both edge devices and the cloud, which jointly support application execution according to run-time conditions. The proposed research will establish a novel framework of edge-cloud resource sharing, explore various implementation mechanisms, deploy a hardware platform for experimental studies, develop software tools to transform existing applications to their coalescent-computing versions, and carry out two case studies. Its objective is to enable user applications to seamlessly overcome the resource limitation of edge devices, while keeping the benefits of low response time, communication reduction, and data privacy that edge computing promises.This work addresses the pressing need for a generic theory of adaptive edge computing, with concrete implementation mechanisms that allow existing applications to take advantage of edge computing without complete re-design. The proposed coalescent-computing framework and its software tools offer a convenient way of transforming existing application code into distributed software with enhanced performance, response time, robustness and scalability, which all contribute to better user experience. The integrated theoretical and experimental research will advance our understanding of modern edge-computing systems. All software tools developed from this project will be made open-source to help stimulate other related research. The project also includes an education plan that introduces edge computing into undergraduate/graduate courses, recruits from under-represented student groups, and helps in club activities at the local high school.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.

通过将计算转移到互联网的边缘，新兴的边缘计算模式有望减少应用程序响应时间，改善用户体验，节省带宽成本，并增强数据隐私。该项目将建立一种名为联合计算的新的边缘计算理论，其中每个应用程序运行在一个集成的虚拟计算系统上，该系统由来自边缘设备和云的资源组成，根据运行时条件共同支持应用程序的执行。拟议的研究将建立一个新的边云资源共享框架，探索各种实现机制，部署用于实验研究的硬件平台，开发软件工具将现有应用程序转换为其合并计算版本，并开展两个案例研究。它的目标是使用户应用程序能够无缝地克服边缘设备的资源限制，同时保持边缘计算承诺的低响应时间、减少通信和数据隐私的好处。这项工作解决了对自适应边缘计算通用理论的迫切需求，并提供了具体的实现机制，允许现有应用程序在不需要完全重新设计的情况下利用边缘计算。拟议的合并计算框架及其软件工具提供了一种将现有应用程序代码转换为分布式软件的便捷方法，具有增强的性能、响应时间、健壮性和可伸缩性，所有这些都有助于更好的用户体验。理论和实验相结合的研究将促进我们对现代边缘计算系统的理解。从该项目开发的所有软件工具都将开源，以帮助促进其他相关研究。该项目还包括一项教育计划，将边缘计算引入本科/研究生课程，从代表性不足的学生群体中招募人才，并帮助当地高中的俱乐部活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Survey on Mainstream Dimensions of Edge Computing

• DOI: 10.1145/3471287.3471295
• 发表时间: 2021-05
• 期刊: Proceedings of the 2021 5th International Conference on Information System and Data Mining
• 作者: Yuanda Wang;Haibo Wang;Shigang Chen;Ye Xia-

Supporting Real-Time T-Queries on Network Traffic with A Cloud-based Offloading Model, (CLOUD), September 2021. (*co-first authors, Invited Paper and Refereed, CSR 1909077, FCC)

使用基于云的卸载模型支持网络流量的实时 T 查询，(CLOUD)，2021 年 9 月。（*共同第一作者，特邀论文和审稿人，CSR 1909077，FCC）

• 发表时间: 2021
• 期刊: Proceedings of IEEE International Conference on Cloud Computing
• 作者: Wang, Yuanda;Wang, Haibo;Ma, Chaoyi;Chen, Shigang;Xia, Ye
• 通讯作者: Xia, Ye

Dynamic Edge-Twin Computing for Vehicle Tracking, in Proc. of 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), short paper, 6 pages, September 2021.

用于车辆跟踪的动态边缘孪生计算，过程中。

• 发表时间: 2021
• 期刊: Proceedings of IEEE 14th International Conference on Cloud Computing
• 作者: Wang, Yuanda;Chen, Shigang;Xia, Ye;Melissourgos, Dimitrios;Wang, Haibo
• 通讯作者: Wang, Haibo

Hierarchical Virtual Bitmaps for Spread Estimation in Traffic Measurement

• DOI: 10.5121/csit.2021.110718
• 发表时间: 2021-05
• 期刊: Computer Science & Information Technology (CS & IT)
• 作者: Olufemi O. Odegbile;Chaoyi Ma;Shigang Chen;Dimitrios Melissourgos;Haibo Wang

Randomized Error Removal for Online Spread Estimation in Data Streaming

• DOI: 10.14778/3447689.3447707
• 发表时间: 2021-02
• 期刊: Proc. VLDB Endow.
• 作者: Haibo Wang;Chaoyi Ma;Olufemi O. Odegbile;Shigang Chen;J. Peir