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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 showcasesBFTGym, 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 withfull-stack adaptivityin 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. 

This paper presents AdaChain , a learning-based blockchain framework that adaptively chooses the best permissioned blockchain architecture to optimize effective throughput for dynamic transaction workloads. AdaChain addresses the challenge in Blockchain-as-a-Service (BaaS) environments, where a large variety of possible smart contracts are deployed with different workload characteristics. AdaChain supports automatically adapting to an underlying, dynamically changing workload through the use of reinforcement learning. When a promising architecture is identified, AdaChain switches from the current architecture to the promising one at runtime in a secure and correct manner. Experimentally, we show that AdaChain can converge quickly to optimal architectures under changing workloads and significantly outperform fixed architectures in terms of the number of successfully committed transactions, all while incurring low additional overhead. 

While permissioned blockchains enable a family of data center applications, existing systems suffer from imbalanced loads across compute and memory, exacerbating the underutilization of cloud resources. This paper presents FlexChain , a novel permissioned blockchain system that addresses this challenge by physically disaggregating CPUs, DRAM, and storage devices to process different blockchain workloads efficiently. Disaggregation allows blockchain service providers to upgrade and expand hardware resources independently to support a wide range of smart contracts with diverse CPU and memory demands. Moreover, it ensures efficient resource utilization and hence prevents resource fragmentation in a data center. We have explored the design of XOV blockchain systems in a disaggregated fashion and developed a tiered key-value store that can elastically scale its memory and storage. Our design significantly speeds up the execution stage. We have also leveraged several techniques to parallelize the validation stage in FlexChain to further improve the overall blockchain performance. Our evaluation results show that FlexChain can provide independent compute and memory scalability, while incurring at most 12.8% disaggregation overhead. FlexChain achieves almost identical throughput as the state-of-the-art distributed approaches with significantly lower memory and CPU consumption for compute-intensive and memory-intensive workloads respectively. 

Despite the emergence of probabilistic logic programming (PLP) languages for data driven applications, there are currently no debugging tools based on provenance for PLP programs. In this paper, we propose a novel provenance model and system, called P3 (Provenance for Probabilistic logic Programs) for analyzing PLP programs. P3 enables four types of provenance queries: traditional explanation queries, queries for finding the set of most important derivations within an approximate error, top-K most influential queries, and modification queries that enable us to modify tuple probabilities with fewest modifications to program or input data. We apply these queries into real-world scenarios and present theoretical analysis and practical algorithms for such queries. We have developed a prototype of P3, and our evaluation on real-world data demonstrates that the system can support a wide-range of provenance queries with explainable results. Moreover, the system maintains provenance and execute queries efficiently with low overhead.