Today, blockchain ledgers utilize concurrent deterministic execution schemes to scale up. However, ordering fairness is not preserved in these schemes: although they ensure all replicas achieve the same serial order, this order does not always align with the fair, consensus-established order when executing smart contracts with runtime-determined accesses. To preserve ordering fairness, an intuitive method is to concurrently execute transactions and re-execute any order-violating ones. This in turn increases unforeseen conflicts, leading to scaling bottlenecks caused by numerous costly aborts under contention. To address these issues, we propose Spectrum, a novel deterministic execution scheme for smart contract execution on blockchain ledgers. Spectrum preserves the consensus-established serial order (so-called strict determinism) with high performance. Specifically, we leverage a speculative deterministic concurrency control to execute transactions in speculation and enforce an agreed-upon serial order by aborting and re-executing any mis-speculated ones. To overcome the scaling bottleneck, we present two key optimizations based on speculative processing: operation-level rollback and predictive scheduling, for reducing both the overhead and the number of mis-speculations. We evaluate Spectrum by executing EVM-based smart contracts on popular benchmarks, showing that it realizes fair smart contract execution by preserving ordering fairness and outperforms competitive schemes in contended workloads by 1.4x to 4.1x.

Permissioned blockchains are being widely applied to solve the trust problem in enterprise collaboration. However, most of these systems suffer from low throughput and flexibility lacking issues. In this paper, we present a blockchain system SChain with scalable concurrent execution based on a flexible architecture. SChain separates the functionality of a complete "node" into three sub-functions and assigns them to different peers within every organization. Then each organization can scale each sub-function flexibly with no need for negotiation between organizations. Based on this architecture, SChain explores scalable concurrent execution from two levels. First, SChain takes the advantage of multiple peers to execute transactions collectively, while promising they make the same results as one peer does serially. Second, SChain enables concurrent transaction execution across blocks to utilize the resources of peers fully, breaking up the block-by-block process manner, based on a pipelined workflow. The extensive evaluation results demonstrate that SChain significantly outperforms the serial execution and other competing systems-level approaches.

The emergence of digital asset applications, driven by Web 3.0 and powered by blockchain technology, has led to a growing demand for blockchain-specific graph analytics to unearth the insights. However, current blockchain data analytics systems are unable to perform efficient ad-hoc graph analytics over both live and past time windows due to their inefficient data synchronization and slow graph snapshots retrieval capability. To address these issues, we propose ChainDash, a blockchain data analytics system that dedicates a highly-parallelized data synchronization component and a retrieval-optimized temporal graph store. By leveraging these techniques, ChainDash supports efficient ad-hoc graph analytics of smart contract activities over arbitrary time windows. In the demonstration, we showcase the interactive visualization interfaces of ChainDash, where attendees will execute customized queries for ad-hoc graph analytics of blockchain data.

Permissioned blockchain is increasingly being used as a collaborative platform for sharing data. However, current blockchain-based data sharing is unable to balance privacy pro-tection and query functionality, limiting its application scenarios. Order-preserving encryption/encoding (OPE) allows encrypting data to prevent privacy leakage while still supporting efficient order-oriented queries on ciphertexts. But existing OPE schemes are constrained by limited use cases and inherent performance limitations that make them difficult to be adopted by permissioned blockchain where performance is a major concern. In this paper, we present BlockOPE, an efficient OPE scheme designed around the first study integrating OPE into blockchain systems. By supporting parallel processing with a conflict-reducing design, we argue that BlockOPE is feasible for permissioned blockchain, achieving orders-of-magnitude performance improvement while preserving the ideal OPE security. Additionally, we improve query processing by leveraging an adaptive lightweight client cache. Extensive experiment results and theoretical analysis illustrate the practicability of our approach.

The full-replication data storage mechanism, as commonly utilized in existing blockchains, suffers from poor scalability, since it requires every node to preserve a complete copy of the whole block data locally to tolerant potential Byzantine failures. In a hostile environment, the malicious node may discard or tamper data deliberately. Thus, existing distributed storage method, which partitions data into multiple parts and distributes them over all nodes, cannot suit for blockchains. This demonstration showcases BFT-Store, a novel distributed storage engine for blockchains to break full-replication by integrating erasure coding with Byzantine Fault Tolerance (BFT) consensus protocol. This demonstration will (\romannumeral1) allow audience members to see how BFT-Store partitions block data over all nodes to reduce the storage occupation of system, and (\romannumeral2) allow audience members to see how BFT-Store recovers blocks under distributed scenario even with Byzantine failure.

We demonstrate SChain, a consortium blockchain that scales transaction processing to support large-scale enterprise applications. The unique advantage of SChain stems from the exploitation of both intra- and inter-block concurrency. The intra-block concurrency not only takes advantage of the multi-core processor on a single peer but also leverages the capacity of multiple peers. The interblock concurrency enables simultaneous processing across multiple blocks to increase the utilization of various peers. In our demonstration, we use real-time dashboards containing visualization based on the output of SChain to give the attendees interactive explorations of how SChain achieves intra- and inter-block concurrency.