Implementation and Evaluation of Load Balancing Mechanism With Multiple Edge Server Cooperation for Dynamic Map System

Abstract

In recent years, research has been conducted on dynamic map systems, which are information and communication platforms that manage vehicle sensor information and run applications. However, there is a concern about scalability in the dynamic map system, which operates on a cloud on the Internet, when the number of vehicles that transmit and receive sensor information increases. Therefore, it is considered that the application load can be distributed by distributing multiple edge servers geographically and having the information managed in the cloud by the edge servers. However, the edge server that controls the vehicle information and the edge server that receives the data do not always match depending on the actual radio wave conditions. Therefore, some applications are difficult to manage, such as intersection collision danger warning and merging arbitration when edge servers are assigned in the same way as base stations that receive data from vehicles. Such an application should receive all vehicle data for the target road area. Therefore, we divide the area on the road where a vehicle travels as "lane section ID" and assign an edge server to each lane section ID on the basis of that area. In addition, we implemented a dynamic map system that connects vehicles and edge servers by linking multiple edge servers. This enables the edge server to aggregate vehicle data without being affected by radio traffic conditions. We evaluated the scalability of the dynamic map system and verified the effectiveness of the load balancing mechanism using multiple edge servers.

Deep Reinforcement Learning for Load-Balancing Aware Network Control in IoT Edge Systems

“…Load balancing is a widely used solution for optimizing the performance of IoT networks [32], [33] and edge servers [34], [35]. For the dynamic system environment as we study in this work, the D3QN model is used to simultaneously balance the size of data collected by each edge server as well as the communication load in each network cluster.…”

A Fundamental Study of Reliable Vehicle-to-Cloud Communication Using Multiple Paths with Redundancy Mitigation

“…High communication reliability is required in V2C communication, especially in cases where vehicles transmit important messages or sensing data, such as those associated with driving safety. A V2C communication path between a vehicle and a cloud server, such as a dynamic map server, consists of both wireless and wired links [8]. Wireless communication types for vehicles include Dedicated Short-Range Communication (DSRC), cellular C-V2X, 5G New Radio (NR), etc., [9][10][11][12].…”