CSR: Small: Collaborative Research: Multi-tier Service Architecture in IoT-Edge-Cloud Paradigms

CSR：小型：协作研究：物联网-边缘-云范式中的多层服务架构

• 批准号: 1815115
• 负责人: Wenjun Hu
• 金额: $ 46.11万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815115&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; Multi

Edge computing is an emerging paradigm in which computing and storage are placed at multiple locations between the endpoint Internet of Things (IoT) devices and the cloud. This is seen as a driving force behind the next wave of advances in IoT solutions. Edge services are predicted to become the next multi-billion dollar technology market and disrupt the current cloud computing based industry. Active industry-wide development and standardization efforts for edge computing are under way to define related architectures. Against this backdrop, this project designs and implements a system architecture to optimize multi-tier service orchestration across IoT devices, edge servers, and the cloud. In contrast to the typical per-application optimization approach, this project takes a unique view of joint, concurrent optimization of multiple applications. This architecture encourages the development of new distributed algorithms for decomposing the processing semantics of IoT applications. Further, the project contributes techniques and reference models to industry standardization efforts.Harnessing edge capabilities to transform the IoT service landscape requires novel distributed algorithms and system architectures. This goes far beyond existing approaches for application code partitioning and computation offloading for individual applications. To that end, this project aims to build a system runtime that abstracts away the infrastructure complexity while automating the optimization of application processing. On the compute path, the project highlights new types of correlated computation, where the corresponding input and output are similar but not identical. Eliminating this holds the key to simultaneously optimizing for the computation latency and results quality. The techniques developed further provide primitives to restructure the processing pipelines of these applications. On the control path, the project departs from the existing server-centric control in a single client-server tier, and instead proposes client-centric control in a nested server model to leverage multiple tiers of computing capabilities. Finally, the above techniques are integrated into a system runtime, which further provides simple APIs (application programming interface) and generic system level support such as scheduling.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.

边缘计算是一种新兴范例，其中计算和存储放置在端点物联网 (IoT) 设备和云之间的多个位置。这被视为物联网解决方案下一波进步背后的驱动力。边缘服务预计将成为下一个数十亿美元的技术市场，并颠覆当前基于云计算的行业。全行业范围内正在积极开展边缘计算的开发和标准化工作，以定义相关架构。在此背景下，该项目设计并实现了一个系统架构，以优化跨物联网设备、边缘服务器和云的多层服务编排。与典型的按应用程序优化方法相比，该项目对多个应用程序的联合、并发优化采取了独特的观点。这种架构鼓励开发新的分布式算法来分解物联网应用程序的处理语义。此外，该项目还为行业标准化工作贡献了技术和参考模型。利用边缘功能来改变物联网服务格局需要新颖的分布式算法和系统架构。这远远超出了单个应用程序的应用程序代码分区和计算卸载的现有方法。为此，该项目旨在构建一个系统运行时，该系统运行时可以抽象化基础设施的复杂性，同时自动优化应用程序处理。在计算路径上，该项目突出了新型相关计算，其中相应的输入和输出相似但不相同。消除这个问题是同时优化计算延迟和结果质量的关键。进一步开发的技术提供了重构这些应用程序的处理管道的原语。在控制路径上，该项目脱离了现有的单一客户端-服务器层中以服务器为中心的控制，而是提出了在嵌套服务器模型中以客户端为中心的控制，以利用多层计算能力。最后，将上述技术集成到系统运行时中，进一步提供简单的 API（应用程序编程接口）和调度等通用系统级支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。

项目成果

Mistify: Automating DNN Model Porting for On-Device Inference at the Edge

Mistify：自动化 DNN 模型移植以实现边缘设备上推理

• DOI: 10.1109/ijcnn.2019.8852223
• 发表时间: 2024-09-14
• 期刊: 2019 International Joint Conference on Neural Networks (IJCNN)
• 作者: Peizhen Guo;Bo Hu;Wenjun Hu
• 通讯作者: Wenjun Hu

Sommelier: Curating DNN Models for the Masses

侍酒师：为大众打造 DNN 模型

• DOI: 10.1145/3514221.3526173
• 发表时间: 2022-06-10
• 期刊: Proceedings of the 2022 International Conference on Management of Data
• 作者: Peizhen Guo;Bo Hu;Wenjun Hu
• 通讯作者: Wenjun Hu

Libra and the Art of Task Sizing in Big-Data Analytic Systems

Libra 和大数据分析系统中任务规模调整的艺术

• DOI: 10.1145/3357223.3362720
• 发表时间: 2019-11-20
• 期刊: Proceedings of the ACM Symposium on Cloud Computing
• 作者: Ruikang Li;Peizhen Guo;Bo Hu;Wenjun Hu
• 通讯作者: Wenjun Hu

FoggyCache: Cross-Device Approximate Computation Reuse

FoggyCache：跨设备近似计算重用

• DOI: 10.1145/3241539.3241557
• 发表时间: 2018-10-15
• 期刊: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking
• 作者: Peizhen Guo;Bo Hu;Ruikang Li;Wenjun Hu
• 通讯作者: Wenjun Hu

Video-zilla: An Indexing Layer for Large-Scale Video Analytics

Video-zilla：用于大规模视频分析的索引层

• DOI: 10.1145/3514221.3517840
• 发表时间: 2022-06-10
• 期刊: Proceedings of the 2022 International Conference on Management of Data
• 作者: Bo Hu;Peizhen Guo;Wenjun Hu
• 通讯作者: Wenjun Hu