Sharding is a promising solution to scale blockchain by separating the system into multiple shards to process transactions in parallel. However, due to state separation and shard isolation, it is still challenging to efficiently support smart contracts on a blockchain sharding system where smart contracts can interact with each other, involving states maintained by multiple shards. Specifically, existing sharding systems adopt a costly multi-step collaboration mechanism to execute smart contracts, resulting in long latency and low throughput. This article proposesSparrow, a blockchain sharding protocol achieving one-step execution for smart contracts. To break shard isolation, inspired by non-local hotspot data caching in traditional databases, we propose a new idea ofinter-shard caching, allowing a shard to prefetch and cache frequently accessed contract states of other shards. The miner can thus use the inter-shard cache to pre-execute a pending transaction, retrieve all its contract invocations, and commit it to multiple shards in one step. Particularly, we first propose a speculative dispersal cache synchronisation mechanism for efficient and secure cache synchronization across shards in Byzantine environments. Then, we propose a multi-branch exploration mechanism to solve the rollback problem during the optimistic one-step execution of contract invocations with dependencies. We also present a series of conflict resolution mechanisms to decrease the rollback caused by inherent transaction conflicts. We implement prototypes forSparrowand existing sharding systems, and the evaluation shows thatSparrowimproves the throughput by$2.44\times$and reduces the transaction latency by 30% compared with the existing sharding systems.

Recently, stateless blockchains have been proposed to alleviate the storage overhead for nodes. A stateless blockchain achieves storage-consensus parallelism, where storage workloads are offloaded from on-chain consensus, enabling more resource-constraint nodes to participate in the consensus. However, existing stateless blockchains still suffer from limited throughput. In this paper, we present Porygon, a novel stateless blockchain with three-dimensional (3D) parallelism. First, Porygon separates the storage and consensus of transactions as the stateless blockchain, achieving the storage-consensus parallelism. This first-dimensional parallelism divides the processing of transactions into several stages and scales the network by supporting more nodes in the system. Based on such a design, we then propose a pipeline mechanism to achieve second-dimensional inter-block parallelism, where relevant stages of processing transactions are pipelined efficiently, thereby reducing transaction latency. Finally, Porygon presents a sharding mechanism to achieve third-dimensional inner-block parallelism. By sharding the executions of transactions of a block and adopting a lightweight cross-shard coordination mechanism, Porygon can effectively execute both intra-shard and cross-shard transactions, consequently achieving outstanding transaction throughput. We evaluate the performance of Porygon by extensive experiments on an implemented prototype and large-scale simulations. Compared with existing blockchains, Porygon boosts throughput by up to 20x, reduces network usage by more than 50%, and simultaneously requires only 5MB of storage consumption per node.

Blockchain store states in Log-Structured Merge (LSM) tree-based database. Due to blockchain traceability, the growing ancient states are inevitably stored in the databases. Unfortunately, by default, this process mixescurrentandancientstates in the data layout, increasing unnecessary disk I/O access and slowing transaction execution. This paper proposes MoltDB, a scalable LSM-based database for efficient transaction execution through a novel idea ofancient state segregation, i.e., to segregate current and ancient states in the data layout. However, the frequently generated and uncertainly accessed characteristics of ancient states make the segregation challenging. Thus, we develop an “extract-compact” mechanism to batch extraction process for frequently generated ancient states and the LSM compaction process to relieve additional disk I/O overhead. Moreover, we design an adaptive LSM-based storage for the uncertainly accessed ancient states extracted for on-demand access. We implement MoltDB as a database engine compatible with many mainstream blockchains and integrate it into Ethereum for evaluation. Experimental results show that MoltDB achieves 1.3 × transaction throughput and 30% disk I/O latency savings over the state-of-the-art works.

Payment channel networks (PCNs) are considered as a prominent solution for scaling blockchain, where users can establish payment channels and complete transactions in an off-chain manner. However, it is non-trivial to schedule transactions in PCNs and most existing routing algorithms suffer from the following challenges: 1) one-shot optimization, 2) privacy-invasive channel probing, 3) vulnerability to DoS attacks. To address these challenges, we propose a privacy-aware transaction scheduling algorithm with defence against DoS attacks based on deep reinforcement learning (DRL), namely PTRD. Specifically, considering both the privacy preservation and long-term throughput into the optimization criteria, we formulate the transaction-scheduling problem as a Constrained Markov Decision Process. We then design PTRD, which extends off-the-shelf DRL algorithms to constrained optimization with an additional cost critic-network and an adaptive Lagrangian multiplier. Moreover, considering the distribution nature of PCNs, in which each user schedules transactions independently, we develop a distributed training framework to collect the knowledge learned by each agent so as to enhance learning effectiveness. With the customized network design and the distributed training framework, PTRD achieves a good balance between the optimization of the throughput and the minimization of privacy risks. Evaluations show that PTRD outperforms the state-of-the-art PCN routing algorithms by 2.7%–62.5% in terms of the long-term throughput while satisfying privacy constraints.

Sharding has been considered as a prominent approach to enhance the limited performance of blockchain. However, most sharding systems leverage a non-cooperative design, which lowers the fault tolerance resilience due to the decreased mining power as the consensus execution is limited to each separated shard. To this end, we present Benzene, a novel sharding system that enhances the performance by cooperation-based sharding while defending the per-shard security. First, we establish a double-chain architecture for function decoupling. This architecture separates transaction-recording functions from consensus-execution functions, thereby enabling the cross-shard cooperation during consensus execution while preserving the concurrency nature of sharding. Second, we design a cross-shard block verification mechanism leveraging Trusted Execution Environment (TEE), via which miners can verify blocks from other shards during the cooperation process with the minimized overheads. Finally, we design a voting-based consensus protocol for cross-shard cooperation. Transactions in each shard are confirmed by all shards that simultaneously cast votes, consequently achieving an enhanced fault tolerance and lowering the confirmation latency. We implement Benzene and conduct both prototype experiments and large-scale simulations to evaluate the performance of Benzene. Results show that Benzene achieves superior performance than existing sharding/non-sharding blockchain protocols. In particular, Benzene achieves a linearly-improved throughput with the increased number of shards (e.g., 32,370 transactions per second with 50 shards) and maintains a lower confirmation latency than Bitcoin (with more than 50 shards). Meanwhile, Benzene maintains a fixed fault tolerance at 1/3 even with the increased number of shards.

Blockchain-based cryptocurrencies have developed rapidly in recent years, however, scalability is one of the biggest challenge. Payment channel networks (PCNs) are one of the important solutions to blockchain scalability and routing is the most critical problem in PCN. Routing algorithms in PCNs have evolved fast and achieved high throughput. However, most of these routing algorithms are designed from the perspective of technical feasibility, and few algorithms focus on the incentives of each off-chain participant, especially the economic incentives for intermediate routing nodes. Besides, due to the highly dynamic nature of off-chain channel deposits, existing routing algorithms rely heavily on channel deposit probing in order to ensure high throughput. In this article, we design routing algorithms from an incentive perspective to improve the profit of intermediate nodes and use deep learning to reduce the dependency of off-chain routing on channel deposit probing. Our experiments show that under the same model, MDRL-IR can increase the profit of intermediate nodes by up to 1.87x and increase the throughput by up to 2.0x compared to the state-of-the-art routing algorithm, while ensuring that the user routing cost per unit throughput remains unchanged. Moreover, approximate performance can be achieved when deposit probing is greatly reduced.