Gaviss : Boosting the Performance of GPU-Accelerated NFV Systems via Data Sharing

Abstract

GPUs have demonstrated the capability of significantly improving the performance of network functions (NF). In an Network Function Virtualization (NFV) system, multiple NFs form a service chain to provide services. However, NFs in state-of-the-art GPU-accelerated NFV systems still utilize a GPU independently where each NF needs to transfer data to the GPU memory for acceleration. As a result, a packet might be transferred into the GPU memory by each NF when it passes through the service chain. We find these expensive and repetitive transfers are the main factor that limits the overall performance of an NFV system. We propose Gaviss, a GPU-accelerated NFV system with effective data sharing. By sharing packets in the GPU memory among network functions, a packet needs to be transferred to the GPU only once, eliminating the performance overhead caused by repetitive transfers. Extensive experimental results show that Gaviss can improve the overall throughput by 2.6-13.2× and reduce the latency by up to 37.9%, when compared with state-of-the-art approaches. Moreover, Gaviss also demonstrates up to 2.5× higher price-performance ratio than CPU-based implementations, making GPUs competitive for building NFV systems.