云制造系统中区块链排队时延分析与仿真

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Title:

Analysis and simulation of blockchain queueing delay in the cloud manufacturing system

作者:

徐杨杨, 王艳, 纪志成

江南大学 物联网工程学院, 江苏 无锡 214122

Author(s):

XU Yangyang, WANG Yan, JI Zhicheng

School of the Internet of Things Engineering, Jiangnan University, Wuxi 214122, China

关键词:

云制造; 区块链; 排队时延; 排队论; 服务率; 自适应难度; 动态规划; 挖矿激励

Keywords:

cloud manufacturing; blockchain; queuing delay; queuing theory; service rate; adaptive difficulty; dynamic planning; mining incentives

分类号:

TP391

DOI:

10.11992/tis.202112033

摘要:

针对云制造系统中区块链的排队时延问题，探索降低云制造系统中区块链排队时延的因素，提出一种新型的云制造系统区块链模型，在传统云制造系统架构的服务层中引入区块链服务。将制造服务请求在区块链服务的排队时延过程分解为缓冲阶段和共识阶段，使用M/M/1排队模型分析系统指标。提出一种自适应难度值机制，优化不同算力的节点参与共识的机会。并且研究节点收益与节点服务率的关系。仿真结果表明，基于M/M/1排队模型能够反映云制造系统的请求排队时延过程；引入自适应难度值后，算力小的区块链节点有更大的机会获取记账权，且节点的收益与其服务率呈正相关。

Abstract:

Aiming to solve the queuing delay problem of the blockchain in the cloud manufacturing system, and explore factors that reduce the queuing delay of the blockchain in the cloud manufacturing system, this paper proposes a new type of cloud manufacturing system blockchain model. The blockchain services are introduced into the service layer of the traditional cloud manufacturing system architecture. The queuing delay process of manufacturing service requests in the blockchain service is decomposed into the buffer phase and the consensus phase, and the M/M/1 queuing model is used to analyze system indicators. An adaptive difficulty value mechanism is proposed to optimize the chances of nodes with different computing powers to participate in consensus. And the relationship between node revenue and node service rate is studied. The simulation results show that the M/M/1 queue model can reflect the request queuing delay process of the cloud manufacturing system. After the introduction of the adaptive difficulty value, the blockchain node with small computing power has a greater chance to obtain the bookkeeping right, and the node’s revenue is positively correlated with its service rate.