Deep Reinforcement Learning Based Scheduling Strategy in Blockchain Payment Channel Networks

【Author】 Ren, Zhe; Wang, Zihao; Li, Xinghua; Miao, Yinbin; Li, Zhuowen; Liu, Ximeng; Han, Lei; Deng, Robert H.

【Source】IEEE-ACM TRANSACTIONS ON NETWORKING

【影响因子】3.796

【Abstract】With the popularity of blockchains, low transaction throughput has become a significant bottleneck in applications such as cryptocurrencies. Payment channel networks (PCNs) have received attention as a way to improve throughput. However, due to the difficulty of predicting future transactions for nodes, the transactions are prone to failure when the channel balances do not meet required conditions. It has been shown that increasing buffers (queues) in PCNs can increase the success rate of transactions and throughput. Nevertheless, there is no effective transaction scheduling strategy in buffers when transaction values are flexible and variable. To solve this problem, we first formulate the Scheduling Problem in PCNs (named PSP), and then prove it is NP-hard. We design a neural network solver based on the Sequence to Sequence (Seq2Seq) architecture and train the solver using the reinforcement learning method. With the solver, we first give two scheduling strategies to maximize transaction throughput, and then design a PCN simulator for performance evaluation. Extensive experiments are conducted to show the superiority and various performances of our proposal and illustrate that our proposal can get a significant advantage in terms of the transaction throughput compared to the existing works.

【Keywords】Blockchain; payment channel networks; off-chain payments; transaction scheduling; deep reinforcement learning; Blockchain; payment channel networks; off-chain payments; transaction scheduling; deep reinforcement learning

【发表时间】2024 2024 NOV 11

【收录时间】2024-11-27

【文献类型】

【主题类别】

--