Gpunfv

Abstract

This paper presents GPUNFV, a high-performance NFV system providing ow-level micro services for stateful service chains with Graphics Processing Unit (GPU) acceleration. GPUNFV exploits the massively-parallel processing power of GPU to maximize the throughput of the NFV system. Combined with the customized ow handler, GPUNFV achieves a much better throughput than the existing NFV systems. With a carefully designed GPU-based virtualized network function framework, GPUNFV is able to e ciently support both stateful and stateless network functions. We have implemented a number of GPU-based network functions and a preliminary GPUNFV system to demonstrate the exibility and potential of our design.

Network Function Virtualization and Service Function Chaining Frameworks: A Comprehensive Review of Requirements, Objectives, Implementations, and Open Research Challenges

“…There are diverse frameworks for implementing virtual network functions in service provider network environments, which can be used to implement network functions at the data plane of the network, using virtual switching technologies such as OpenvSwitch [95] or P4 [53]. Virtual network functions can also be implemented in the user space, using unikernels [96], virtual machines [97], GPUs [98], or containers [13].…”

PacketMill: toward per-Core 100-Gbps networking

“…In addition to the works discussed throughout the paper, the work on NFV performance acceleration can be classified into three categories: 1 relies on hardware accelerators to improve processing speed by offloading (part of) packet processing into an FPGA, GPU, or modern NIC [20,28,45,52,53,69,87,96,98,101,104,105]; 2 focuses on NFV execution models and tries to improve the performance of either the pipeline/parallelism model [43,55,61,86,103] or run-to-completion (RTC) model [37,76]; and 3 improves the performance of NFV by reducing/eliminating redundant operations and/or merging similar packet processing elements into (one) consolidated optimized equivalent [1,12,40,44,55,85]. The second category also includes efforts toward better scheduling & load balancing [4,5,7,41,50,94] or more efficient I/O [24,25].…”