Collaborative Communication and Computation for Hierarchical Learning at the Mobile Edge

在这个研究项目中，我们在一个新兴的系统中学习工程理论和设计，在这个系统中，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)合作，以支持机器学习(ML)服务和应用。下面是这样一个系统应用于增强现实应用时的一个鼓舞人心的例子。我们可能有多个摄像头，旨在共同识别其集体视野中的对象，并向附近的移动用户发送上下文增强标签，这些标签可以显示为覆盖他们的实际风景。摄像机本身没有足够的计算能力来识别对象，同时由于数据量大且远距离通信有限，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，使它们能够为摄像机提供及时的帮助。他们可以组合摄像头的信息，执行必要的ML计算任务，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上的协作式无线分层ML。 在这种新兴的多层次ML层次结构中，一个关键组件是链接物联网设备、MEC主机和云服务器中的大量计算引擎的通信路径。此外，通信和计算需求之间存在高度的相关性，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作业的部分而显著不同。物联网设备和MEC主机之间在通信和计算方面的有效协作对整体系统性能至关重要。因此，在提出的研究中，我们促进了先前分离的ML、无线通信和分布式计算算法的整体设计。我们的目标是在联合通信-计算模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ML服务和应用。 我们将关注三个短期目标：(1)动态的ML任务划分和任务分配策略，以实现数据效率和计算效率之间的最佳折衷；(2)分层ML中多智能体协作的集成通信-计算资源管理方法；(3)MEC中在线ML和调度的算法和分析工具。预计拟议研究的结果将极大地促进在广泛的服务和应用中进行创新的系统设计，如自动化制造、远程电子健康、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。

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.