CAREER: Orchestrating Edge Infrastructures and Mobile Devices under Uncertainty to Provision Edge AI as a Service

职业：在不确定性下协调边缘基础设施和移动设备以提供边缘人工智能即服务

• 批准号: 2047719
• 负责人: Lei Jiao
• 金额: $ 51.08万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047719&HistoricalAwards=false
• 关键词: CAREER; Orchestrating; Edge; Infrastructures; Mobile

Artificial Intelligence (AI) is often trained by data first, and then used to perform inference upon data which may have never been seen. Today's AI services are mostly trained in remote data centers and accessed via the Internet. However, increasingly, as large volumes of training data are generated by end users and inference tasks are also performed on premises, AI needs to be moved to the network edge in closer proximity to users. The objective of this project is to address this emerging paradigm shift by orchestrating the distributed computing and networking infrastructures and provisioning AI as a service from the network edge to serve large numbers of users at different locations.This project will define the optimization problems, design the control algorithms, and develop the deployable implementations for operating AI as a service across cloud-edge networks and mobile devices. Centered on system dynamics and uncertainty, the project consists of multiple research thrusts. First, the project will optimize the training and the inference of AI through making smart decisions on participant selection, model placement, aggregation control, and request dispatching, and will produce time-based algorithms running in an online manner with provable performance guarantees. Second, the project will investigate the interactions among different parties in the AI service ecosystem from an economics perspective, and will devise incentive mechanisms towards superior resource utilization and optimal social welfare with desired economic properties. Third, the project will conduct a combination of simulations, measurements, and implementations with real-world data traces and realistic experimental settings to comprehensively evaluate and validate the models, algorithms, and systems.This project will impact the industry by providing telecom carriers, network operators, and service providers with a critical set of techniques to enable them to provision and operate dedicated or value-added edge-AI services upon distributed infrastructures. Further, the project will devise novel theories and algorithms based on the mathematics of optimization, control, learning, and mechanism design, and could be of independent interest and have extended applications to problems in other related fields that also face dynamic and uncertain inputs. Finally, besides contributing to undergraduate and graduate course materials, the project will execute the education plan that focuses on facilitating K-12 education via training teachers and also equipping students with AI knowledge and experiences in an ethical manner.The deliverables of this project, which include but are not limited to papers, data, and codes, will be made publicly available at the following website: https://github.com/ai-at-edge. This website will be updated in time for the duration of this project and maintained online thereafter.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.

人工智能（AI）通常首先通过数据进行训练，然后用于对可能从未见过的数据进行推理。如今的人工智能服务大多在远程数据中心进行训练并通过互联网访问。然而，随着最终用户生成大量训练数据并且推理任务也在本地执行，人工智能需要逐渐转移到更靠近用户的网络边缘。该项目的目标是通过协调分布式计算和网络基础设施，并从网络边缘将人工智能作为服务提供，为不同位置的大量用户提供服务，从而解决这一新兴范式转变。该项目将定义优化问题，设计控制算法，并开发可部署的实现，以将人工智能作为跨云边缘网络和移动设备的服务进行操作。该项目以系统动力学和不确定性为中心，包含多个研究重点。首先，该项目将通过在参与者选择、模型放置、聚合控制和请求调度方面做出智能决策来优化人工智能的训练和推理，并将产生以在线方式运行且具有可证明性能保证的基于时间的算法。其次，该项目将从经济学角度研究人工智能服务生态系统中各方之间的相互作用，并设计激励机制，以实现具有理想经济属性的优质资源利用和最优社会福利。第三，该项目将结合真实世界的数据轨迹和真实的实验设置进行模拟、测量和实现，以全面评估和验证模型、算法和系统。该项目将通过为电信运营商、网络运营商和服务提供商拥有一套关键技术，使他们能够在分布式基础设施上提供和运营专用或增值边缘人工智能服务。此外，该项目将设计基于优化、控制、学习和机制设计数学的新颖理论和算法，并且可以具有独立的兴趣，并可扩展到其他也面临动态和不确定输入的相关领域的问题。最后，除了提供本科生和研究生课程材料外，该项目还将执行教育计划，重点是通过培训教师来促进 K-12 教育，并以道德的方式为学生提供人工智能知识和经验。该项目的可交付成果包括包括但不限于论文、数据和代码，将在以下网站公开：https://github.com/ai-at-edge。该网站将在该项目期间及时更新，并在此后在线维护。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

EAVS: Edge-assisted Adaptive Video Streaming with Fine-grained Serverless Pipelines

EAVS：具有细粒度无服务器管道的边缘辅助自适应视频流

• DOI: 10.1109/infocom53939.2023.10229102
• 发表时间: 2023-05-17
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Biao Hou;Song Yang;F. Kuipers;Lei Jiao;Xiao
• 通讯作者: Xiao

Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions

迈向可持续人工智能：通过拍卖实现云边缘系统中的联邦学习需求响应

• DOI: 10.1109/infocom53939.2023.10229014
• 发表时间: 2023-05-17
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Fei Wang;Lei Jiao;Konglin Zhu;Xiaojun Lin;Lei Li
• 通讯作者: Lei Li

AI in 5G: The Case of Online Distributed Transfer Learning over Edge Networks

5G 中的人工智能：边缘网络上的在线分布式迁移学习案例

• DOI: 10.1109/infocom48880.2022.9796779
• 发表时间: 2022-05-02
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
• 作者: Yulan Yuan;Lei Jiao;Konglin Zhu;Xiaojun Lin;Lin Zhang
• 通讯作者: Lin Zhang

Power-of-2-arms for bandit learning with switching costs

用于强盗学习的 2 臂力量（Power-of-2-arms），具有转换成本

• DOI: 10.1145/3492866.3549720
• 发表时间: 2022-10
• 期刊: and Protocol Design for Mobile Networks and Mobile Computing
• 作者: Shi, Ming;Lin, Xiaojun;Jiao, Lei
• 通讯作者: Jiao, Lei

Orchestrating Blockchain with Decentralized Federated Learning in Edge Networks

在边缘网络中通过去中心化联合学习来协调区块链

• DOI: 10.1109/secon58729.2023.10287416
• 发表时间: 2023-09-11
• 期刊: 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
• 作者: Yibo Jin;Lei Jiao;Zhuzhong Qian;Ruiting Zhou;Lingjun Pu
• 通讯作者: Lingjun Pu