Accelerating Network Features Deployment With Heterogeneous Platforms
Tingting Xu, Xiaoliang Wang, Chen Tian, Yun Xiong, Baoliu Ye, Sanglu Lu, Cam-Tu Nguyen · IEEE Transactions on Networking · 2024
Enhancing the networking system with appropriate functions is a longstanding goal. Unfortunately, in today’s large-scale high-speed data centers, the feature velocity of network functions is slow because it is hard to verify the function in realistic scenarios. Recent advances in programmable switching ASICs have enabled the network data plane to move beyond its traditional role of packet forwarding. However, the current compromise between performance and flexibility results in limitations such as restricted memory/computation resources and programmable models. These limitations make it challenging for programmable switches to offer more features and to be deployed in large-scale production environments. In response, we present CLIP, a framework that works in collaboration with programmable devices and commodity servers to enhance the validation and deployment velocity of features. CLIP defines a cross-platform function definition framework and provides a set of tools to reduce the complexity of manually writing cross-platform programs. We propose an automatic traffic placement and scaling mechanism to coordinate packet processing performance across heterogeneous devices. Compared with software-based Network Functions (NFs), CLIP achieves a throughput ranging from$1.36\times $to$16.06\times $under different realistic traffic loads. Through the development and deployment of three self-defined functions within a realistic testbed, we demonstrate the feasibility and efficiency of CLIP.