Collaborative Communication and Computation for Hierarchical Learning at the Mobile Edge

移动边缘分层学习的协作通信和计算

• 批准号: RGPIN-2020-05886
• 负责人: Liang, Ben
• 金额: $ 4.66万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740894
• 关键词: Collaborative; Communication; Computation; Hierarchical; Learning

In this research program, we study engineering theories and designs in an emerging system where Internet-of-Things (IoT) and other mobile devices collaborate with nearby computing servers, called Mobile/Multiaccess Edge Computing (MEC) hosts, to support machine learning (ML) services and applications. The following is a motivating example for such a system when it is applied to the augmented reality application. We may have multiple cameras aiming to jointly recognize the objects in their collective field of vision and send contextual augmentation labels to nearby mobile users, which can be displayed overlaying their view of the actual scenery. The cameras by themselves do not have sufficient computational power to recognize the objects, while it is also inefficient to send their videos to a remote cloud server for processing, due to the large data size and the limited communication over a long distance. In contrast, MEC hosts have unique features of high-bandwidth communication and low-latency computation, which enable them to provide timely assistance to the cameras. They can combine the cameras' information, perform the necessary ML computing jobs, and then send the augmentation labels to the mobile users. We name such a system collaborative wireless hierarchical ML over MEC. A crucial component in this emerging multi-level ML hierarchy is the communication pathway linking the large number of computing engines in the IoT devices, MEC hosts, and cloud servers. Furthermore, there is a high level of correlation between the communication and computation requirements, since the amount of transmitted data can vary dramatically depending on the portion of an ML job that is offloaded to the MEC hosts or cloud servers. Effective collaboration among IoT devices and MEC hosts in both communication and computation is crucial to overall system performance. Therefore, in the proposed research, we promote a holistic design of previously separate ML, wireless communication, and distributed computation algorithms. We aim to seamlessly integrate collaborative IoT devices, MEC hosts, and cloud servers, in a joint communication-computation paradigm, to support hierarchical ML services and applications. We will focus on three shorter-term objectives: (1) dynamic ML job division and task placement strategies to achieve optimal trade-off between data efficiency and computation efficiency, (2) integrated communication-computation resource management methods for multi-agent cooperation in hierarchical ML, and (3) algorithms and analytical tools for online ML and scheduling in MEC. The outcomes of the proposed research are expected to contribute substantially to motivate novel system designs in a wide range of services and applications, such as automated manufacturing, remote e-health, environment monitoring, and intelligent transportation. They are expected to have sustained market impact in strategic sectors of the Canadian economy.

在这个研究项目中，我们研究新兴系统中的工程理论和设计，在该系统中，物联网 (IoT) 和其他移动设备与附近的计算服务器（称为移动/多路访问边缘计算 (MEC) 主机）协作，以支持机器学习（ ML）服务和应用程序。以下是将此类系统应用于增强现实应用程序的一个激励示例。我们可能有多个摄像头，旨在共同识别他们共同视野中的物体，并向附近的移动用户发送上下文增强标签，这些标签可以显示在他们的实际风景视图上。摄像机本身没有足够的计算能力来识别物体，同时由于数据量大且长距离通信有限，将视频发送到远程云服务器进行处理也效率低下。相比之下，MEC主机具有高带宽通信和低延迟计算的独特功能，这使得它们能够为摄像机提供及时的帮助。他们可以结合相机的信息，执行必要的机器学习计算工作，然后将增强标签发送给移动用户。我们将这样的系统命名为 MEC 上的协作无线分层 ML。这种新兴的多级机器学习层次结构中的一个关键组成部分是连接物联网设备、MEC 主机和云服务器中大量计算引擎的通信路径。此外，通信和计算要求之间存在高度相关性，因为传输的数据量可能会根据卸载到 MEC 主机或云服务器的 ML 作业部分的不同而发生巨大变化。 IoT 设备和 MEC 主机在通信和计算方面的有效协作对于整体系统性能至关重要。因此，在拟议的研究中，我们提倡对以前独立的机器学习、无线通信和分布式计算算法进行整体设计。我们的目标是在联合通信计算范式中无缝集成协作物联网设备、MEC 主机和云服务器，以支持分层机器学习服务和应用程序。我们将重点关注三个短期目标：（1）动态ML作业划分和任务放置策略，以实现数据效率和计算效率之间的最佳权衡，（2）用于多智能体合作的集成通信计算资源管理方法分层机器学习，(3) 用于 MEC 中在线机器学习和调度的算法和分析工具。拟议研究的成果预计将极大地促进各种服务和应用中的新颖系统设计，例如自动化制造、远程电子医疗、环境监测和智能交通。预计它们将对加拿大经济的战略部门产生持续的市场影响。