A Heuristic Offloading Method for Deep Learning Edge Services in 5G Networks

Abstract

With the continuous development of the Internet of Things (IoT) and communications technology, especially under the epoch of 5G, mobile tasks with big scales of data have a strong demand in deep learning such as virtual speech recognition and video classification. Considering the limited computing resource and battery consumption of mobile devices (MDs), these tasks are often offloaded to the remote infrastructure, like cloud platforms, which leads to the unavoidable offloading transmission delay. Edge computing (EC) is a novel computing paradigm, capable of offloading the computation tasks to the edge of networks, which reduces the transmission delay between the MDs and cloud. Therefore, combining deep learning and EC is expected to be a solution for these tasks. However, how to decide the offloading destination [cloud or deep learning-enabled edge computing nodes (ECNs)] for computation offloading is still a challenge. In this paper, a heuristic offloading method, named HOM, is proposed to minimize the total transmission delay. To be more specific, an offloading framework for deep learning edge services is built upon centralized unit (CU)-distributed unit (DU) architecture. Then, we acquire the appropriate offloading strategy by the origin-destination ECN distance estimation and heuristic searching of the destination virtual machines for accommodating the offloaded computation tasks. Finally, the effectiveness of the scheme is verified by detailed experimental evaluations.

Keywords

• Edge computing
• deep learning
• computation offloading
• 5G
• cloud