Visible to the public Building Dynamic Mapping with CUPS for Next Generation Automotive Edge Computing

TitleBuilding Dynamic Mapping with CUPS for Next Generation Automotive Edge Computing
Publication TypeConference Paper
Year of Publication2019
AuthorsSun, Z., Du, P., Nakao, A., Zhong, L., Onishi, R.
Conference Name2019 IEEE 8th International Conference on Cloud Networking (CloudNet)
Date PublishedNov. 2019
ISBN Number978-1-7281-4832-
Keywords5G, 5G mobile communication, 5G networks, central cloud-based approach, central server, cloud computing, Computer architecture, control and user plane separation, coupled congestion control, CUPS, data traffic, Dynamic mapping, dynamic mapping traffic, edge computing, edge computing architecture, edge servers, global dynamic mapping information, intelligent transportation systems, Internet of Things, IoT, local edge server, Logic gates, mobility management (mobile radio), next generation automotive edge computing, next generation networks, next-generation intelligent transportation system, Prototypes, pubcrawl, resilience, Resiliency, response latency, road traffic, Scalability, Servers, synchronisation, telecommunication control, telecommunication traffic, traffic accidents, traffic engineering computing, Vehicle dynamics

With the development of IoT and 5G networks, the demand for the next-generation intelligent transportation system has been growing at a rapid pace. Dynamic mapping has been considered one of the key technologies to reduce traffic accidents and congestion in the intelligent transportation system. However, as the number of vehicles keeps growing, a huge volume of mapping traffic may overload the central cloud, leading to serious performance degradation. In this paper, we propose and prototype a CUPS (control and user plane separation)-based edge computing architecture for the dynamic mapping and quantify its benefits by prototyping. There are a couple of merits of our proposal: (i) we can mitigate the overhead of the networks and central cloud because we only need to abstract and send global dynamic mapping information from the edge servers to the central cloud; (ii) we can reduce the response latency since the dynamic mapping traffic can be isolated from other data traffic by being generated and distributed from a local edge server that is deployed closer to the vehicles than the central server in cloud. The capabilities of our system have been quantified. The experimental results have shown our system achieves throughput improvement by more than four times, and response latency reduction by 67.8% compared to the conventional central cloud-based approach. Although these results are still obtained from the preliminary evaluations using our prototype system, we believe that our proposed architecture gives insight into how we utilize CUPS and edge computing to enable efficient dynamic mapping applications.

Citation Keysun_building_2019