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 μ 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 μ s using a 4 μ s interval while remaining robust to network congestion.

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.

With the increasing demand and limited production, China has to import a large amount of soybeans. However, soybean has been chosen as one target of the recent trade war between the US and China. It is therefore critical to assess the sustainability of soybean supply in China. Under such a circumstance, this study aims to fill such a research gap by using an emergy accounting approach from both spatial and temporal perspectives and at provincial-level. The impact of trade war on soybean imports and production is simulated by one GTAP (Global Trade Analysis Project) model. The results of Emergy Sustainability Indices (ESI) show that it is urgent to improve the sustainability of soybean planting in Heilongjiang, while Yunnan is the most appropriate place for planting soybean. For the international supply, the EER (Emergy Exchange Ratio) of China has decreased by 72% and the decrease of EERs at provincial level ranged from 59% to 86% during 2000–2015. The simulation results indicate the necessity of adjusting spatial structure of soybean planting and applying reasonable economic instruments to encourage sustainable soybean production.