Threshold signatures have improved the scalability of BFT protocols by replacing all-to-all broadcast with star-based communication. On the flip side, this approach renders network re-organization and performance optimization more costly, because information can only be exchanged between the leader and all other nodes.We present StarReact,an extension for BFT protocols that relies on star-based communication to collect and disseminate quorum certificates. This extension allows all nodes of the system to measure and evaluate the network state by monitoring the messages disseminated by the leader. Each node decides independently if performance degradation justifies a leader change. By deploying a median filter, the measured commit latency of the system is evaluated by each node to determine if a network change has a lasting effect that warrants a change in leadership or should be ignored. We showcase how StarReact is able to identify important network changes for different deployment scenarios and explain how view change executions should be adapted to different environments to improve commit latency and potentially throughput for such systems.

Measuring instruments are increasingly defined by complex software while using simple hardware sensors.For such systems, software conformity between certified prototypes and devices in the field is usually demonstrated using version numbers and hashes over executable code.Legal requirements for regulated instruments could equally be satisfied if prototype and device in the field display identical functional behavior even if hashes differ.Such functional identification can give instrument manufacturers room for software patches and bugfixes without the need for recertification.Based on the L * algorithm, which is used to learn the language which deterministic finite automata accept, a risk-based method is proposed that realizes automatic functional identification of software to a certain extent, thereby enabling quality control of regularly updated measuring instruments without the need for frequent manual inspections.Risk assessment may be used to identify critical state transitions in monitored devices, which can be used to trigger recertifications if needed.

Classic BFT protocols are often deployed in the LAN setting, with low delays and reliable links. Recent publications of BFT protocols have decreased drastically in their communication complexity and the application to previously unsuited areas such as mobile or sensor networks is getting more traction. To facilitate that development and showcase suitability under harsher network conditions, we take one of the most recent BFT protocols, HotStuff, and analyze the impact of increased and varying network delays on its performance. We apply the delay prediction scheme AWARE and make some simple modifications to the prediction algorithm in order to increase its performance even further.

The publication of the pivotal state machine replication protocol PBFT laid the foundation for a body of BFT protocols.We introduce a probabilistic model for evaluating BFT protocols in the presence of dynamic link and crash failures.The model is derived from the communication pattern, facilitating an adaptation to other protocols.The state of replicas is captured and used to derive the success probability of the protocol execution.To this end, we examine the influence of link and crash failure rates as well as the number of replicas.A comparison in protocol behavior of PBFT, Zyzzyva and SBFT is performed.