Large-Scale Many-Objective Deployment Optimization of Edge Servers

The development of the Internet of Vehicles (IoV) has made transportation systems into intelligent networks. However, with the increase in vehicles, an increasing number of data need to be analyzed and processed. Roadside units (RSUs) can offload the data collected from vehicles to remote cloud servers for processing, but they cause significant network latency and are unfriendly to applications that require real-time information. Edge computing (EC) brings low service latency to users. There are many studies on computing offloading strategies for vehicles or other mobile devices to edge servers (ESs), and the deployment of ESs cannot be ignored. In this paper, the placement problem of ESs in the IoV is studied, and the six-objective ES deployment optimization model is constructed by simultaneously considering transmission delay, workload balancing, energy consumption, deployment costs, network reliability, and ES quantity. In addition, the deployment problem of ESs is optimized by a many-objective evolutionary algorithm. By comparing with the state-of-the-art methods, the effectiveness of the algorithm and model is verified.