State-of-the-art blockchain sharding solutions, such as Monoxide, can cause severely imbalanced distribution of transaction (TX) workloads across all blockchain shards due to the deployment policy of their accounts. Imbalanced TX distributions then producehot shards, in which the cross-shard TXs may experience an unlimited confirmation latency. Thus, how to address the hot-shard issue and how to reduce cross-shard TXs become significant challenges of blockchain sharding. Through reviewing the related studies, we find that a cross-shard TX protocol that can achieve workload balance among all shards and simultaneously reduce the quantity of cross-shard TXs is still absent from the literature. To this end, we propose BrokerChain, which is a cross-shard blockchain protocol dedicated to account-based state sharding. Essentially, BrokerChain exploits fine-grained state partition and account segmentation. We also elaborate on how BrokerChain handles cross-shard TXs through broker accounts. The security issues and other properties of BrokerChain are analyzed rigorously. Finally, we conduct comprehensive evaluations using an open-source blockchain sharding prototype namedBlockEmulator. The evaluation results show that BrokerChain outperforms other baselines in terms of transaction throughput, transaction confirmation latency, the queue size of the transaction pool, and workload balance.

In a sharded blockchain, transactions are processed by a number of parallel committees. Thus, the transaction throughput can be largely boosted. A problem is that some groups of blockchain nodes consume large latency to form committees at the beginning of each epoch. Moreover, the heterogeneous processing capabilities of different committees also result in imbalanced consensus latency. Such imbalanced two-phase latency brings a large cumulative age to the transactions pending in transaction pool. Consequently, the blockchain throughput can be significantly degraded. We believe that a good committee-scheduling strategy can reduce the cumulative age of transactions, and thus benefit the throughput. However, we have not yet found a committee-scheduling mechanism that works for accelerating block formation in the context of blockchain sharding. To this end, this paper studies a fine-balanced tradeoff between the transactions’ throughput and their cumulative age in a large-scale sharded blockchain. We formulate this tradeoff as a utility-maximization problem, which is proved NP-hard. To solve this problem, we propose an online distributed Stochastic-Exploration (SE) algorithm, which guarantees a near-optimal system utility. We then rigorously analyze three theoretical properties of the proposed algorithm, including the theoretical convergence time, the probability of committees’ failure due to Sybil attacks, as well as the performance perturbation brought by committees’ offline events. Finally, we evaluate the proposed algorithm using the dataset of real-world blockchain transactions. The simulation results demonstrate that the proposed SE algorithm outperforms other baselines in terms of system utility, the valuable degree of yielded solutions, latency, and throughput performance.

State-of-the-art blockchain sharding solutions, say Monoxide, can induce imbalanced transaction (TX) distributions among all blockchain shards due to their account deployment mechanisms. Imbalanced TX distributions then cause hot shards, in which the cross-shard TXs may experience an unlimited length of confirmation latency. Thus, how to address the hot-shard issue and how to reduce cross-shard TXs become significant challenges of blockchain state sharding. Through reviewing the related studies, we find that a cross-shard TX protocol that can achieve workload balance among all shards and simultaneously reduce the number of cross-shard TXs is still absent from the literature. To this end, we propose BrokerChain, which is a cross-shard blockchain protocol devised for the account/balance-based state sharding. Essentially, BrokerChain exploits fine-grained state partition and account segmentation. We also elaborate on how BrokerChain handles cross-shard TXs through broker accounts. The security issues and other properties of BrokerChain are analyzed substantially. Finally, we conduct comprehensive evaluations using both a cloud-based prototype and a transaction-driven simulator. The evaluation results show that BrokerChain outperforms other solutions in terms of system throughput, transaction confirmation latency, the queue size of transaction pool, and workload balance.

Sharding technique is viewed as the most promising solution to improving blockchain scalability. However, to implement a sharded blockchain, developers have to address two major challenges. The first challenge is that the ratio of cross-shard transactions (TXs) across blockchain shards is very high. This issue significantly degrades the throughput of a blockchain. The second challenge is that the workloads across blockchain shards are largely imbalanced. If workloads are imbalanced, some shards have to handle an overwhelming number of TXs and become congested very possibly. Facing these two challenges, a dilemma is that it is difficult to guarantee a low cross-shard TX ratio and maintain the workload balance across all shards, simultaneously. We believe that a fine-grained account-allocation strategy can address this dilemma. To this end, we first formulate the tradeoff between such two metrics as a network-partition problem. We then solve this problem using a community-aware account partition algorithm. Furthermore, we also propose a sharding protocol, named Transformers, to apply the proposed algorithm into the sharded blockchain system. Finally, trace-driven evaluation results demonstrate that the proposed protocol outperforms other baselines in terms of throughput, latency, cross-shard TX ratio, and the queue size of transaction pool.

This article studies the PBFT-based sharded permissioned blockchain, which executes in either a local datacenter or a rented cloud platform. In such permissioned blockchain, the transaction (TX) assignment strategy could be malicious such that the network shards may possibly receive imbalanced transactions or even bursty-TX injection attacks. An imbalanced transaction assignment brings serious threats to the stability of the sharded blockchain. A stable sharded blockchain can ensure that each shard processes the arrived transactions timely. Since the system stability is closely related to the blockchain throughput, how to maintain a stable sharded blockchain becomes a challenge. To depict the transaction processing in each network shard, we adopt the Lyapunov Optimization framework. Exploitingdrift-plus-penalty(DPP) technique, we then propose an adaptive resource-allocation algorithm, which can yield the near-optimal solution for each network shard while the shard queues can also be stably maintained. We also rigorously analyze the theoretical boundaries of both the system objective and the queue length of shards. The numerical results show that the proposed algorithm can achieve a better balance between resource consumption and queue stability than other baselines. We particularly evaluate two representative cases of bursty-TX injection attacks, i.e., the continued attacks against all network shards and the drastic attacks against a single network shard. The evaluation results show that the DPP-based algorithm can well alleviate the imbalanced TX assignment, and simultaneously maintain high throughput while consuming fewer resources than other baselines.

In a large-scale sharded blockchain, transactions are processed by a number of parallel committees collaboratively. Thus, the blockchain throughput can be strongly boosted. A problem is that some groups of blockchain nodes consume large latency to form committees at the beginning of each epoch. Furthermore, the heterogeneous processing capabilities of different committees also result in unbalanced consensus latency. Such unbalanced two-phase latency brings a large cumulative age to the transactions waited in the final committee. Consequently, the blockchain throughput can be significantly degraded because of the large transaction's cumulative age. We believe that a good committee-scheduling strategy can reduce the cumulative age, and thus benefit the blockchain throughput. However, we have not yet found a committee-scheduling scheme that works for accelerating block formation in the context of blockchain sharding. To this end, this paper studies a fine-balanced tradeoff between the transaction's throughput and their cumulative age in a large-scale sharded blockchain. We formulate this tradeoff as a utility-maximization problem, which is proved NP-hard. To solve this problem, we propose an online distributed Stochastic-Exploration (SE) algorithm, which guarantees a near-optimal system utility. The theoretical convergence time of the proposed algorithm as well as the performance perturbation brought by the committee's failure are also analyzed rigorously. We then evaluate the proposed algorithm using the dataset of blockchain-sharding transactions. The simulation results demonstrate that the proposed SE algorithm shows an overwhelming better performance comparing with other baselines in terms of both system utility and the contributing degree while processing shard transactions.

In recent years, cognitive services like face recognition and speech recognition has found wide applications in smart cities. The main approach of providing cognitive services depends on cloud computing, which however suffers high response latency and information security problems. In this paper, based on edge computing, prototypes on face recognition and speaker recognition are developed on a cloud radio access network platform. In the face recognition architecture, a traditional face recognition network is cascaded to a face detection network so as to mitigate the interference of irrelevant information. In the speaker recognition architecture, multi-task learning is exploited to improve the generalization of neural network, and a phoneme recognition task is employed as an auxiliary task to assist the speaker recognition. Experimental results corroborate the effectiveness of the proposed architectures in terms of recognition accuracy and response latency.