Throughput Optimization for Blockchain System with Dynamic Sharding
Sharding technology, which divides a network into multiple disjoint groups so that transactions can be processed in parallel, is applied to blockchain systems as a promising solution to improve Transactions Per Second (TPS). This paper considers the Optimal Blockchain Sharding (OBCS) problem as a Ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/24/4915 |
| _version_ | 1797381313901101056 |
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| author | Chuyi Liu Jianxiong Wan Leixiao Li Bingbing Yao |
| author_facet | Chuyi Liu Jianxiong Wan Leixiao Li Bingbing Yao |
| author_sort | Chuyi Liu |
| collection | DOAJ |
| description | Sharding technology, which divides a network into multiple disjoint groups so that transactions can be processed in parallel, is applied to blockchain systems as a promising solution to improve Transactions Per Second (TPS). This paper considers the Optimal Blockchain Sharding (OBCS) problem as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL)-based methods, where the action space must be discretized to increase processability. However, the discretization degrades the quality of the solution since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and provide dynamic sharding strategies based on them. The Branching Dueling Q-Network Blockchain Sharding (BDQBS) algorithm is designed for discrete action spaces. Compared with traditional DRL algorithms, the BDQBS overcomes the drawbacks of high action space dimensions and difficulty in training neural networks. And it improves the performance of the blockchain system by 1.25 times. We also propose a sharding control algorithm based on the Parameterized Deep Q-Networks (P-DQN) algorithm, i.e., the Parameterized Deep Q-Networks Blockchain Sharding (P-DQNBS) algorithm, to efficiently handle the discrete–continuous hybrid action space without the scalability issues. Also, the method can effectively improve the TPS by up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>28</mn><mo>%</mo></mrow></semantics></math></inline-formula>. |
| first_indexed | 2024-03-08T20:49:37Z |
| format | Article |
| id | doaj.art-4fee042df12b4ab797554138c8f1d709 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T20:49:37Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-4fee042df12b4ab797554138c8f1d7092023-12-22T14:04:52ZengMDPI AGElectronics2079-92922023-12-011224491510.3390/electronics12244915Throughput Optimization for Blockchain System with Dynamic ShardingChuyi Liu0Jianxiong Wan1Leixiao Li2Bingbing Yao3College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, ChinaSharding technology, which divides a network into multiple disjoint groups so that transactions can be processed in parallel, is applied to blockchain systems as a promising solution to improve Transactions Per Second (TPS). This paper considers the Optimal Blockchain Sharding (OBCS) problem as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL)-based methods, where the action space must be discretized to increase processability. However, the discretization degrades the quality of the solution since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and provide dynamic sharding strategies based on them. The Branching Dueling Q-Network Blockchain Sharding (BDQBS) algorithm is designed for discrete action spaces. Compared with traditional DRL algorithms, the BDQBS overcomes the drawbacks of high action space dimensions and difficulty in training neural networks. And it improves the performance of the blockchain system by 1.25 times. We also propose a sharding control algorithm based on the Parameterized Deep Q-Networks (P-DQN) algorithm, i.e., the Parameterized Deep Q-Networks Blockchain Sharding (P-DQNBS) algorithm, to efficiently handle the discrete–continuous hybrid action space without the scalability issues. Also, the method can effectively improve the TPS by up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>28</mn><mo>%</mo></mrow></semantics></math></inline-formula>.https://www.mdpi.com/2079-9292/12/24/4915blockchainshardingdeep reinforcement learninghybrid action space |
| spellingShingle | Chuyi Liu Jianxiong Wan Leixiao Li Bingbing Yao Throughput Optimization for Blockchain System with Dynamic Sharding Electronics blockchain sharding deep reinforcement learning hybrid action space |
| title | Throughput Optimization for Blockchain System with Dynamic Sharding |
| title_full | Throughput Optimization for Blockchain System with Dynamic Sharding |
| title_fullStr | Throughput Optimization for Blockchain System with Dynamic Sharding |
| title_full_unstemmed | Throughput Optimization for Blockchain System with Dynamic Sharding |
| title_short | Throughput Optimization for Blockchain System with Dynamic Sharding |
| title_sort | throughput optimization for blockchain system with dynamic sharding |
| topic | blockchain sharding deep reinforcement learning hybrid action space |
| url | https://www.mdpi.com/2079-9292/12/24/4915 |
| work_keys_str_mv | AT chuyiliu throughputoptimizationforblockchainsystemwithdynamicsharding AT jianxiongwan throughputoptimizationforblockchainsystemwithdynamicsharding AT leixiaoli throughputoptimizationforblockchainsystemwithdynamicsharding AT bingbingyao throughputoptimizationforblockchainsystemwithdynamicsharding |