Joint optimization of dynamic resource allocation and packet scheduling for virtual switches in cognitive internet of vehicles

Abstract

Abstract The rapidly evolving machine learning technologies have reshaped the transportation system and played an essential role in the Cognitive Internet of Vehicles (CIoV). Most of the cognitive services are computation-intensive or storage-intensive, and thus they are usually deployed in edge or cloud data centers. In today’s data center networks, the virtual machines hosted in a server are connected to a virtual switch responsible for forwarding all packets for the cognitive services deployed on the virtual machines. Therefore, the virtual switches will become a performance bottleneck for cognitive services without an efficient resource allocation and data scheduling strategy. However, the highly dynamic characteristics of cognitive services make the resource allocation and packet scheduling problem for virtual switches surprisingly challenging. To guarantee the performance of cognitive services, we investigate the joint optimization problem of dynamic resource allocation and packet scheduling for virtual switches. We first model the joint optimization problem of dynamic resource allocation and packet scheduling for virtual switches as a mathematical optimization problem. Then, we analyze the problem with Lyapunov Optimization Framework and derive efficient optimization algorithms with performance tradeoff bounds. At last, we evaluate these algorithms on a testbed and a network-wide simulation platform. Experiment results show that our algorithms outperform other designs and meet the theoretical performance bound.

Keywords

• Cognitive Internet of Vehicle
• Machine Learning
• Resource Allocation
• Virtual Switch