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.

快速发展的机器学习技术重塑了交通系统，并在认知车联网 (CIoV) 中发挥了重要作用。大多数认知服务是计算密集型或存储密集型的，因此它们通常部署在边缘或云数据中心。在当今的数据中心网络中，托管在服务器中的虚拟机连接到虚拟交换机，该交换机负责为部署在虚拟机上的认知服务转发所有数据包。因此，如果没有有效的资源分配和数据调度策略，虚拟交换机将成为认知服务的性能瓶颈。然而，认知服务的高度动态特性使得虚拟交换机的资源分配和数据包调度问题出人意料地具有挑战性。为了保证认知服务的性能，我们研究了虚拟交换机的动态资源分配和数据包调度的联合优化问题。我们首先将虚拟交换机的动态资源分配和数据包调度的联合优化问题建模为数学优化问题。然后，我们使用 Lyapunov 优化框架分析问题，并推导出具有性能权衡界限的高效优化算法。最后，我们在测试平台和全网仿真平台上评估这些算法。实验结果表明，我们的算法优于其他设计并满足理论性能界限。我们首先将虚拟交换机的动态资源分配和数据包调度的联合优化问题建模为数学优化问题。然后，我们使用 Lyapunov 优化框架分析问题，并推导出具有性能权衡界限的高效优化算法。最后，我们在测试平台和全网仿真平台上评估这些算法。实验结果表明，我们的算法优于其他设计并满足理论性能界限。我们首先将虚拟交换机的动态资源分配和数据包调度的联合优化问题建模为数学优化问题。然后，我们使用 Lyapunov 优化框架分析问题，并推导出具有性能权衡界限的高效优化算法。最后，我们在测试平台和全网仿真平台上评估这些算法。实验结果表明，我们的算法优于其他设计并满足理论性能界限。我们在测试平台和全网仿真平台上评估这些算法。实验结果表明，我们的算法优于其他设计并满足理论性能界限。我们在测试平台和全网仿真平台上评估这些算法。实验结果表明，我们的算法优于其他设计并满足理论性能界限。