The Border Gateway Protocol (BGP) is employed by autonomous systems (ASes), such as network operators or ISPs, to build routing tables. However, depending on the routing policies implemented by these ASes, BGP may fail to converge, potentially rendering the network inoperative. This paper introduces a workflow that leverages SMT solvers and rewriting tools to automate the verification of BGP convergence within a given AS network. We encode the convergence conditions defined by the Metarouting theoretical framework as an SMT problem. While SMT solvers can automatically determine whether BGP will converge, they do not generate counterexample traces in cases of divergence. To overcome this shortcoming, we propose a sound divergence criterion. We also construct an executable model for verifying BGP convergence, which can be automated using the Maude rewriting tool to produce witness traces in divergent scenarios. The effectiveness of our approach is demonstrated through a series of experiments.

Silicon quantum dot devices stand as promising candidates for large scale quantum computing due to their extended coherence times compact size, and recent experimental demonstrations of sizable qubit arrays. Despite the great potential, controlling these arrays remains a significant challenge. This paper introduces a new virtual gate extraction method to quickly establish orthogonal control on the potentials for individual quantum dots. Leveraging insights from the device physics, the proposed approach significantly re duces the experimental overhead by focusing on crucial regions around charge state transition. Furthermore, by employing an efficient voltage sweeping method, we can efficiently pinpoint these charge state transition lines and filter out erroneous points. Exper imental evaluation using real quantum dot chip datasets demon strates a substantial 5.84× to 19.34× speedup over conventional methods, thereby showcasing promising prospects for accelerating the scaling of silicon spin qubit devices.

Byzantine Fault Tolerant (BFT) protocols serve as a fundamental yet intricate component of distributed data management systems in untrustworthy environments. BFT protocols exhibit different design principles and performance characteristics under varying workloads and fault scenarios. The proliferation of BFT protocols and their growing complexity have made it increasingly challenging to analyze the performance and possible application scenarios of each protocol. This demonstration showcases BFTGym , an interactive platform that allows audience members to (1) evaluate, compare, and gather insights into the performance of various BFT protocols under a wide range of conditions, and (2) prototype new BFT protocols rapidly.

This paper articulates our vision for a learning-based untrustworthy distributed database. We focus on permissioned blockchain systems as an emerging instance of untrustworthy distributed databases and argue that as novel smart contracts, modern hardware, and new cloud platforms arise, future-proof permissioned blockchain systems need to be designed with full-stack adaptivity in mind. At the application level, a future-proof system must adaptively learn the best-performing transaction processing paradigm and quickly adapt to new hardware and unanticipated workload changes on the fly. Likewise, the Byzantine consensus layer must dynamically adjust itself to the workloads, faulty conditions, and network configuration while maintaining compatibility with the transaction processing paradigm. At the infrastructure level, cloud providers must enable cross-layer adaptation, which identifies performance bottlenecks and possible attacks, and determines at runtime the degree of resource disaggregation that best meets application requirements. Within this vision of the future, our paper outlines several research challenges together with some preliminary approaches.