In-network computing (INC) offers orders-of-magnitude performance gains for various applications. However, the existing pipeline-based switch architecture, though well-suited for stateless packet forwarding, imposes three constraints on stateful INC applications: (i) control-plane table management, (ii) scarce memory resources under per-stage layout, and (iii) a strict memory access model. To address these constraints, this paper provides a full-stack solution, covering a programmable chipset Solar-NP, a programming language NPC, and a complete toolchain XuanWu. First, Solar-NP adopts a run-to-completion-based (RTC-based) architecture with three new hardware features: data-plane table management, a hierarchical memory pool, and built-in data structures with atomic access guarantees. Second, to fully exploit the hardware features, our language NPC introduces a new abstraction, namely operation-action table (OAT), that allows table manipulations in the data plane. Finally, our XuanWu toolchain provides various utility software covering the complete workflow of developing, testing, debugging, and deployment. To show the power of our solution, we implement three types of INC cases, including in-network control, in-network telemetry, and in-network storage. Experimental results demonstrate that our solution allows more operations to be offloaded and achieves higher performance than today's pipeline-based solutions.

In network measurement, data plane switches measure traffic and report events (e.g., heavy hitters) to the control plane via control channels. The control plane makes decisions to process events. However, current network measurement suffers from two problems. First, when traffic bursts occur, massive events are reported in a short time so that the control channels may be overloaded due to limited bandwidth capacity. Second, only a few events are reported in normal cases, making control channels underloaded and wasting network resources. In this paper, we propose Escala to provide the elastic scaling of control channels at runtime. The key idea is to dynamically migrate event streams among control channels to regulate the loads of these channels. Escala offers two components, including an Escala monitor that detects scaling situations based on realtime network statistics, and an optimization framework that makes scaling decisions to eliminate overload and underload situations. We have implemented a prototype of Escala on Tofino-based switches. Extensive experiments show that Escala achieves timely elastic scaling while preserving high application-level accuracy.