Efficient Multi-User Resource Allocation for Urban Vehicular Edge Computing: A Hybrid Architecture Matching Approach

Abstract

Advanced in the proliferation of the Internet of Things (IoT), a plethora of functions have been integrated in vehicular networks and thereby transfered it into a smart network. However, the contradiction between the limited on-vehicle computing resource and the massive data collected by these IoT devices hinders the broader adoption of vehicular network as a vast variety of on-vehicle applications are latency-sensitive. To address this issue, vehicular edge computing has become a promising technology as it can offload a large number of tasks from its proximal vehicles. However, the offloading methods recently utilized are inefficient while dealing with multi-user vehicular networks under dynamic scenarios. To design a superior offloading method that can effectively and efficiently offload tasks from vehicles to servers, multiple objectives and constraints with various topologies should be considered. In this paper, instead of constructing a typical multi-user and multi-server vehicular edge computing scenario, a complex scenario with more uncertainties, i.e. urban scenario, is modeled. We propose a Hybrid Architecture Matching Algorithm (HAMA) to minimize the average time latency subject to the constraint on energy consumption and evaluate the proposed algorithm in the above two scenarios. Moreover, HAMA is constructed based on hybrid centralized-distributed architecture, which can process the centralized collected information on a distributed manner. Experimental results demonstrate that the matching algorithm can significantly reduce average time latency, achieving up to a 68% improvement compared to local execution.