Sketches have proven to be useful for measuring traffic. They store measurement results in the registers of data plane switches. However, they suffer from the short of switch register resources, limiting their measurement accuracy.

Sketch-based traffic measurement is a crucial building block for monitoring traffic statistics and ensuring the quality of services of end-host applications. However, existing approaches for building sketches in end-hosts exhibit poor packet processing performance or high CPU consumption. In this paper, we propose MPU, which automatically offloads sketch-based measurement to the emerging hardware, DPU. MPU consists of a sketch analyzer that profiles sketch resource consumption and an optimization framework that formulates the offloading problem and maximizes sketch performance on DPU. We implement MPU on the NVIDIA BlueField DPU. Our testbed results indicate that MPU achieves 85% lower per-packet processing latency and 47% higher traffic measurement accuracy when compared to existing approaches.

In modern data center networks (DCNs), network-stack processing denotes a large portion of the end-to-end latency of TCP flows. So profiling network-stack latency anomalies has been considered as a crucial part in DCN performance diagnosis and troubleshooting. In particular, such profiling requires full coverage (i.e., profiling every TCP packet) and low overhead (i.e., profiling should avoid high CPU consumption in end-hosts). However, existing solutions rely on system calls or tracepoints in end-hosts to implement network-stack latency profiling, leading to either low coverage or high overhead. We propose Torp, a framework that offers full-coverage and low-overhead profiling of network-stack latency. Our key idea is to offload as much of the profiling from costly system calls or tracepoints to the Torp agent built on eBPF modules, and further to include a Torp handler on the ToR switch to accelerate the remaining profiling operations. Torp efficiently coordinates the ToR switch and the Torp agent on end-hosts to jointly execute the entire latency profiling task. We have implemented Torp on$32\times 100$Gbps Tofino switches. Testbed experiments indicate that Torp achieves full coverage and orders of magnitude lower host-side overhead compared to other solutions.

The programmable switch offers a limited capacity of packet header vector (PHV) words that store packet header fields and metadata fields defined by network functions. However, existing switch compilers employ inefficient strategies of encoding fields on PHV words. Their encoding wastes scarce PHV words and may result in failures when deploying network functions. In this paper, we propose Melody, a new framework that reuses PHV words for as many fields as possible to achieve resource-efficient PHV encoding. Melody offers a field analyzer and an optimization framework. The analyzer identifies which fields can reuse PHV words while preserving the original packet processing logic. The framework integrates analysis results into its encoding to offer the resource-optimal decisions. Also, to achieve timeliness at runtime, it provides a Greedy-based heuristic, which quickly solves PHV encoding and returns near-optimal results. We evaluate Melody with production-scale network functions. Our results show that Melody reduces the consumption of PHV words by up to 85%.