Enhancing In-Network Computing Deployment via Collaboration Across Planes

The new paradigm of In-network computing (INC) permits service computation to be executed within network paths, rather than solely on dedicated servers. Although the programmable data plane has showcased notable performance advantages for INC application deployments, its effectiveness is constrained by resource limitations, potentially impeding the expressiveness and scalability of these deployments. Conversely, delegating computational tasks to the control plane, supported by general-purpose servers with abundant resources, offers increased flexibility. Nonetheless, this strategy compromises efficiency to a considerable extent, particularly when the system operates under heavy load. To simultaneously exploit the efficiency of data plane and the flexibility of control plane, we propose Carlo, a cross-plane collaborative optimization framework to support the network-wide deployment of multiple INC applications across both the control and data plane. Carlo first analyzes resource requirements of various INC applications across different planes. It then establishes mathematical models for resource allocation in cross-plane and automatically generates solutions using proposed algorithms. We have implemented the prototype of Carlo on Intel Tofino ASIC switches and DPDK. Experimental results demonstrate that Carlo can effectively trade off between computation time and deployment performance while avoiding performance degradation.