Toward Mobility-Aware Edge Inference Via Model Partition and Service Migration
Zhicheng Liu, Zebo Zhao, Xiaofei Wang, Mianxiong Dong, Chao Qiu, Cheng Zhang · 2023
Deep neural networks are deemed to be the cornerstone of a series of mobile intelligent systems, and their inference processes bring about a mass of computation-intensive tasks. To migrate the burden of inference computation from resource-constrained mobile devices, device-edge cooperative inference in mobile edge computing provides a fine-grained processing method. However, the geographical dispersion of resources and the mobility pattern of devices pose technical issues in the scheduling of co-inference systems, which have not been fully considered. In this paper, we propose a learning-based scheduling framework for such device-edge systems to improve the pipeline time of model inference. First, we consider a resource provisioning strategy based on the number of devices and a pre-fetching service migration setting in the environment of multiple mobile devices and edge nodes. Next, we propose an algorithm based on proximal policy optimization for each device to make the decision independently. Further, we adopt long short-term memory in the algorithm to capture the temporal characteristics of the system state. Experiments using a real-world network and computing trace demonstrate that the proposed algorithm can efficiently sense the mobile system to make decisions at various system scales and two mobility scenes. The average pipeline time of the proposed algorithm is only 67.63% of that of local processing, which is 97.50% of that of the omniscient algorithm.