AI Summary & Key Takeaways
AI InsightUsing this framework, the mobile users will make a decision on which situation is better suited to obtain the service based on the latencies as well as queuing capacities of the networks.
📌 3 Key Takeaways
Abstract
Today, many large usages of cloud-based vehicular networks and applications have rapidly increased. This rapid increase causes the requirement of systems to be reliable to share their resources without delay in order to ensure a better quality of service (QoS) for mobile users. Hence, network slicing is considered one of the key concepts to enhance QoS in 5G networks. At present, new architectures attempt to provide support for end-to-end server quality mechanisms. A key mechanism of network slicing supported by such modern architectures is able to either handover to better network or migrate services closer to the users as they move around. This can be done by advanced handover and server localization techniques. These sorts of advanced handover and server localization help to maintain the QoS for mobile application in heterogeneous environments. In order to obtain QoS measurements and get the network conditions in a specific area, a cloud-based vehicular network slicing management framework is proposed using an analytical modeling approach. The analytical model results obtained considering real scenarios from a Middlesex University VANET testbed. Using this framework, the mobile users will make a decision on which situation is better suited to obtain the service based on the latencies as well as queuing capacities of the networks.