We first model the joint optimization problem of dynamic resource allocation and packet scheduling for virtual switches as a mathematical optimization problem. To guarantee the performance of cognitive services, we investigate the joint optimization problem of dynamic resource allocation and packet scheduling for virtual switches. However, the highly dynamic characteristics of cognitive services make the resource allocation and packet scheduling problem for virtual switches surprisingly challenging. Therefore, the virtual switches will become a performance bottleneck for cognitive services without an efficient resource allocation and data scheduling strategy. 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. Most of the cognitive services are computation-intensive or storage-intensive, and thus they are usually deployed in edge or cloud data centers. The rapidly evolving machine learning technologies have reshaped the transportation system and played an essential role in the Cognitive Internet of Vehicles (CIoV).

The contributions of this work build the foundation for future research in the areas of network softwarization and network function virtualization. We employ simulative approaches to investigate overload control mechanisms that allow efficient operation under adversary conditions. Finally, we investigate a full, complex system consisting of multiple software-based solutions and perform a detailed performance analysis. We present novel approaches towards network abstraction that allow the integration of heterogeneous data plane technologies into a singular network while maintaining total transparency between control and data plane.

In the scope of the thesis we propose mechanisms to monitor the performance of singular components and model key performance indicators of software based packet processing solutions. In this doctoral thesis we cover the performance evaluation of next generation data plane architectures, comprised of complex software as well as programmable hardware components that allow fine granular configuration.

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