Abstract Keyword search, which identifies transactions associated with specified keywords across historical blocks, is a critical query type in blockchain analytics. However, existing approaches, such as on-chain indexing and off-chain synchronization, may lead to significant space overhead or challenges in maintaining data freshness. To address these challenges, we propose BlockSketch, a novel probabilistic data structure (PDS) that adopts a differentiated encoding strategy, aimed at resolving the trade-off between query performance and storage overhead in blockchain indexing. BlockSketch features a hierarchical filtering architecture that combines Bloom filters and Sketches within a binary tree framework, enabling dynamic structural maintenance. Keywords are categorized as “hot” or “cold” based on their on-chain frequency and encoded into the most suitable component to achieve resource-efficient storage and accurate querying. In addition, BlockSketch integrates two distinct query rules, namely “level-down” and “jump,” to balance query accuracy and efficiency when processing keywords with varying frequencies. Furthermore, we enhance the query efficiency of BlockSketch by merging inefficient lower-level nodes into more compact ones and pruning redundant node checks during query execution. Extensive experiments on a real-world dataset demonstrate that BlockSketch delivers up to 73% faster query processing, achieves 44.56% of the average false positive rate of baselines at low multiplicity and as low as 1.52% at high multiplicity, and saves 79% in storage compared to state-of-the-art methods.

Smart contracts have expanded blockchain applications, but permissioned blockchain systems face severe through-put challenges, especially with the increasing complexity of nested contract transactions. These transactions, involving cross-contract interactions and deep call chains, intensify execution conflicts and rollback overhead, ultimately limiting parallelism. We propose Loom, a deterministic execution framework that enhances the efficiency of nested contract transactions. Loom employs snapshot-based concurrent pre-execution to decompose transactions into fine-grained subtransactions. To reduce rollback overhead, it introduces a two-phase rollback algorithm to minimize computational redundancy and fine-grained rescheduling to improve subtransaction-level parallelism during re-execution. Additionally, a multi-phase parallelism mechanism optimizes resource utilization across transaction blocks. Experimental results show that Loom achieves 6.1 × to$10.2\times$higher throughput while reducing rollback overhead by 89.9% to 98.4%, significantly outperforming state-of-the-art solutions.

Sharding is a prevailing solution to enhance the scalability of current blockchain systems. However, the cross-shard commit protocols adopted in these systems to commit cross-shard transactions commonly incur multi-round shard-to-shard communication, leading to low performance. Furthermore, most solutions only focus on simple transfer transactions without supporting complex smart contracts, preventing sharding from widespread applications. In this paper, we propose LightCross, a novel sharding blockchain system that enables efficient execution of complex cross-shard smart contracts. First, LightCross offloads the execution of cross-shard transactions into off-chain executors equipped with the TEE hardware, which can accommodate execution for arbitrarily complex contracts. Second, we design a lightweight cross-shard commit protocol to commit cross-shard transactions without multi-round shard-to-shard communication between shards. Last, LightCross lowers the cross-shard transaction ratio by dynamically changing the distribution of contracts according to historical transactions. We implemented the LightCross prototype based on the FISCO-BCOS project and evaluated it in real-world blockchain environments, showing that LightCross can achieve 2.6× more throughput than state-of-the-art sharding systems.

As various optimizations being proposed recently, the performance of blockchains is no longer limited by the consensus protocols, successfully scaling to thousands of transactions per second. To further improve blockchains' throughput, exploiting the parallelism in smart contract executions becomes a clear solution to resolve the new performance bottleneck. The existing techniques perform concurrency control on smart contract transactions based on pre-determined read/write sets, which can hardly be calculated precisely. As a result, many parallelization opportunities are missed in order to maintain the correctness of transaction executions. In this paper, we propose a novel execution scheduling framework, DMVCC, to further increase the parallelism in smart contract executions, via more fine-grained control on state accesses. DMVCC improves over existing techniques with two key features: (1) write versioning, eliminating the write-write conflicts between transactions, and (2) early-write visibility, enabling other transactions to read the writes from a transaction earlier, before it being committed. We integrated DMVCC into the Ethereum Virtual Machine, to evaluate its performance in real-world blockchain environments. The experimental results show that DMVCC doubles the parallel speedup achievable to a 20 x overall speedup, compared with the serial execution baseline, approaching the theoretical optimum.

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.

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.

Sharding technique, which divides the whole network into multiple disjoint groups or committees, has been recognized as a revolutionary solution to enhance the scalability of blockchains. For account-based model, state data are partitioned over all committees and organized as Merkle trees to ensure data consistency and immutability. However, existing techniques on Merkle tree-based state storage fail to scale out due to a large amount of network and compute overheads incurred by data migration and Merkle tree reconstruction, respectively. In this paper, we propose $\mathcal{S}$-Store, a scalable data storage technique towards permissioned blockchain sharding based on Aggregate Merkle B+ tree (AMB-tree). $\mathcal{S}$-Store utilizes consistent hashing to reduce data migration among committees and uses split and merge on AMB-tree to decrease Merkle tree reconstruction overheads. $\mathcal{S}$-Store also employs a novel committee addition protocol that guarantees the system service availability during data migration. Extensive experiments show that $\mathcal{S}$-Sotre outperforms existing techniques by one order of magnitude in terms of transaction execution, data transmission, and committee addition.

Although the emergence of the programmable smart contract makes blockchain systems easily embrace a wide range of industrial services, how to execute smart contracts efficiently becomes a big challenge nowadays. Due to the existence of Byzantine nodes, existing mature concurrency control protocols in database cannot be employed directly, since the mechanism of executing smart contracts varies a lot. Furthermore, even though smart contract execution follows a two-phase style, i.e., the primary node executes a batch of smart contracts in the first phase and the validators replay them in the second phase, existing parallel solutions merely focus on the optimization for the first phase, rather than the second phase. In this paper, we propose a novel two-phase concurrency control protocol to optimize both phases for the first time. First, the primary executes transactions in parallel and generates a transaction dependency graph with high parallelism for validators. Then, a graph partition algorithm is devised to divide the original graph into several sub-graphs to preserve parallelism and reduce communication cost remarkably. Finally, we propose a deterministic replay protocol to re-execute the primary’s parallel schedule concurrently. Moreover, this two-phase protocol is further optimized by integrating with PBFT. Theoretical analysis and extensive experimental results illustrate that the proposed scheme outperforms state-of-art solutions significantly.

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.

The full-replication data storage mechanism, as commonly utilized in existing blockchains, is the barrier to the system's scalability, since it retains a copy of entire blockchain at each node so that the overall storage consumption per block is O(n) with n participants. Yet another drawback is that this mechanism may limit the throughput in permissioned blockchain. Moreover, due to the existence of Byzantine nodes, existing partitioning methods, though widely adopted in distributed systems for decades, cannot suit for blockchain systems directly, so that it is critical to devise new storage mechanism for blockchain systems. This article proposes a novel storage engine, called BFT-Store, to enhance storage scalability by integrating erasure coding with Byzantine Fault Tolerance (BFT) consensus protocol. The first property of BFT-store is that the storage consumption per block can be reduced to O(1) for the first time, which enlarges overall storage capability when more nodes attend the blockchain. Second, we design an efficient online re-encoding protocol for storage scale-out and a hybrid replication scheme to enhance reading performance. Analysis in theory and extensive experimental results illustrate the scalability, availability and efficiency of BFT-Store via the implementation in an open-source permissioned blockchain Tendermint.

The full-replication data storage mechanism, as commonly utilized in existing blockchain systems, is lack of sufficient storage scalability, since it reserves a copy of the whole block data in each node so that the overall storage consumption per block is O(n) with n nodes. Moreover, due to the existence of Byzantine nodes, existing partitioning methods, though widely adopted in distributed systems for decades, cannot suit for blockchain systems directly, thereby it is critical to devise a new storage mechanism. This paper proposes a novel storage engine, called BFT-Store, to enhance storage scalability by integrating erasure coding with Byzantine Fault Tolerance (BFT) consensus protocol. First, the storage consumption per block can be reduced to O(1), which enlarges overall storage capability when more nodes join blockchain. Second, an efficient online re-encoding protocol is designed for storage scale-out and a hybrid replication scheme is employed to improve reading performance. Last, extensive experimental results illustrate the scalability, availability and efficiency of BFT-Store, which is implemented on an open-source permissioned blockchain Tendermint.

This paper proposes an effective feature selection method based on monarch butterfly optimization and Fisher criterion. Fisher criterion is applied to evaluate the feature subsets, based on which the optimal feature subsets are searched by using monarch butterfly optimization algorithm. To combine these two components, a method is developed to binarize continuous solution vectors for deciding the feature selection. We conduct experiments on widely used UCI (University of California, Irvine) classification datasets to study the design of our algorithm and compare it with other state-of-the-art counterparts. The experimental results show that the proposed method is reasonable and effective, which achieves the best result of feature selection among the compared methods and has satisfactory efficiency.