GFlow: Towards GPU-based High-Performance Table Matching in OpenFlow Switches

This paper investigates the acceleration of Software-based OpenFlow switches, equipped with commodity off-the-shelf hardware, for high-performance table matching. Particularly, due to the high flexibility and compatibility, software-based and SDN-compatible switches, such as OpenvSwitch, has been widely applied in several viable fields, like cloud services, future Internet architectures, and the network function virtualization (NFV). In these switches, table matching is a critical function. Existing CPU-based solutions are suffering from a low performance. In our work, we leverage the power of GPUs to accelerate table matching in software-based OpenFlow switches. We propose GFlow, which can handle OpenFlow table matching in a parallel fashion. Based on our extensive evaluations, we can see the GFlow is 8 to 10 times faster than existing GPU-based matching algorithm.