Broker2Earn: Towards Maximizing Broker Revenue and System Liquidity for Sharded Blockchains
Qinde Chen, Huawei Huang, Zhaokang Yin, Guang Ye, Qinglin Yang
Abstract
Cross-shard Transactions (CTXs) widely exist in sharded blockchains. CTXs have to endure large confirmation latency because they need to participate in consensus in both their source and destination shards. To diminish CTXs, plenty of state-of-the-art blockchain protocols have been proposed. For example, in BrokerChain [1], some intermediary broker accounts can help turn CTXs into intra-shard transactions through their voluntary liquidity services. Thereby, the original CTXs can be confirmed in blockchain shards quickly. However, we found that BrokerChain is impractical for a sharded blockchain because it does not consider how to recruit a sufficient number of broker accounts. Thus, blockchain clients do not have the motivation to provide token liquidity for others. To address this challenge, we design Broker2Earn, which is essentially a decentralized finance (DeFi) protocol that works as an incentive mechanism for blockchain users who choose to become brokers. Via participating in Broker2Earn, brokers can earn native revenues when they collateralize their tokens to the protocol. Furthermore, Broker2Earn can also benefit the sharded blockchain since it can efficiently spend each staked liquidity provided by brokers on diminishing CTXs. We formulate the core module of Broker2Earn into a revenue-maximization problem, which is proven NP-hard. To solve this problem, we design an online approximation algorithm using the relax-and-rounding technique. We also rigorously analyze the approximation ratio of our online algorithm. Finally, we conduct extensive experiments using real-world Ethereum transactions on both a transaction-driven simulator and an open-source blockchain testbed. The evaluation results show that the proposed Broker2Earn protocol demonstrates a near-optimal performance that outperforms other baselines, in terms of broker revenues and the usage of system liquidity.
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