Programmable data plane devices have enabled various in-network applications that rely on locally stored state for delivering low-latency and high-throughput services. However, these applications are susceptible to network failures, which can disrupt state access and network functionality. Timely and reliable failure detection is therefore a critical component of a stateful data plane. In this paper, we propose a data plane framework, FASTR, that enables microsecond-scale fast failure detection between directly connected switches. FASTR can achieve sub- $10 \mu$ s detection latency by implementing a heartbeat mechanism in the data plane. In addition, FASTR also incorporates traffic-awareness to reduce overhead and priority queuing to avoid false alarms. We validate FASTR with hardware experiments, demonstrating that it can consistently detect failures within $10 \mu$ s using a $4 \mu$ s interval while remaining robust to network congestion.

The emergence of softwarized network devices, like programmable switches and smart NICs, has brought about new and advanced network functionalities. Intelligent decision-making becomes possible at line rate by offloading network functionality from the network control-plane to the programmable data-plane. In this paper, we offload fine-grained Distributed Denial of Service (DDoS) attack detection to the data-plane. The state-of-the-art in this regard, mainly aims to embed Machine Learning (ML) models into the data-plane without compromising on inference accuracy. Besides accuracy, we must consider multiple other factors, like traffic feature availability and false positive rates. To that end, we propose O’MINE: ONE MODEL IS NOT ENOUGH, a novel collaborative detection mechanism comprising lightweight ML models. This maximises the detection accuracy while keeping the false positive rate (FPR) low. We use three state-of-the-art datasets to evaluate the O’MINE algorithm and its ML models. Our results show that O’MINE can detect DDoS attacks with high accuracy (≈98% and ≈96% with full and scarce training data, respectively) and low FPR (≈0.22% and ≈0.72% with full and scarce training data, respectively), outperforming the state-of-the-art. Lastly, O’MINE only consumes a few device resources (≈6% of LUT and ≈4% of FF) on the Xlinx Alevo U250 FPGA we have used for inference at line rate.

To cater to constantly changing network needs, enabling stateful reconfiguration of Network Functions (NFs) is crucial. Recently, there has been growing interest in offloading NFs to programmable network devices. Unfortunately, it is currently not possible to maintain the full state of NFs during a switch reconfiguration without consuming network resources from and to neighboring switches. In this paper, we present State4, a framework that maintains the state of P4 programs during the reconfiguration of a P4-programmab1e network device, by only using a small amount of local resources on the switch undergoing reconfiguration. State4 acts on both the in-switch control-plane and the data-plane. By utilizing the in-switch local controller, State4 requires no external network resources to achieve reconfiguration while preserving states. As such, State4 enables on-the-fly reconfiguration of stateful NFs, at minimal traffic disruption, where previously traffic had to be re-routed.