Using Imperfect Transmission in MEC Offloading to Improve Service Reliability of Time-Critical Computer Vision Applications

Abstract

The emerging time-critical Internet-of-Things (IoT) use cases, e.g., augmented reality (AR), virtual reality (VR), autonomous vehicle etc., on the one hand, involve computation intensive computer vision (CV) components in the services, on the other hand, the computation task should be completed within the stringent latency constraint, otherwise, the service reliability will deteriorate. The state-of-the-art work has shown that it is promising to tackle this challenge by offloading the computation tasks to the edge servers (ESs) using mobile edge computing (MEC). However, offloading tasks from local IoT devices to remote ESs could cause communication errors, thereby resulting in transmission failure even service timeout. The existing work mainly requires perfect transmission during task offloading at physical layer or transport layer. In fact, CV algorithms for, e.g., image classification and recognition, are able to tolerate certain level distortion of the input image to maintain the required inference accuracy. In this paper, we focus on the service reliability at application layer and study how feasible it is to improve the service reliability of the time-critical CV services in MEC system by allowing imperfect transmission. The service reliability is modeled by the transmission failure probability, service timeout probability and inference accuracy. The optimization goal is to maximize the service reliability, subject to the latency constraint. Due to the non-convexity, we solve this problem by the semi-definite relaxation based algorithm for a multi-user scenario. We evaluate the algorithm considering the practical scenarios, i.e., object detection with SSD and YOLOv2. The proposed algorithm achieves the performance closed to the exhaustive method but at a much lower complexity.