面向分布式多主体股票市场仿真系统的调度方法研究
发布时间：2019-03-02 20:03
【摘要】：多主体仿真已经被广泛使用于行为金融学,一些基于ABM建模的仿真系统已经被提出。但是传统股票市场仿真系统运行在单机系统,仿真中主体(Agent)的数量受限于单机的计算能力,而金融研究人员为了获得更有效的数据需要的Agent数量越来越多,为了获得更智能的Agent需要仿真的轮次(round)越来越多。本文专注于大规模Agent、通讯系统和仿真调度提出了一个适用于股票市场的分布式的多主体仿真系统PSSPAM,包括通讯系统、Agent模块、市场中心模块和用户界面四个松耦合模块。Agent基于可以自定义的网络拓扑结构,通过通讯系统模块和其他Agent以及市场中心进行通信。本文提出了一个分布式round线程调度机制,对较大数量的Agent线程进行调度。为了提高分布式环境中带有可演化社交网络模型的PSSPAM仿真系统,节点间的负载均衡和节点间通讯总量都需要被考虑。本文提出一种处理调度方法LBMIC,在考虑节点间负载的不均衡性低于给定阈值并且节点间通信量尽量少的情况下将Agent划分并部署到不同的就按节点。LBMIC将上述问题转化为图划分问题并使用多层图划分算法来实现Agent的调度。当社交网络发生演化时,LBMIC通过迁移Agent来动态调整初始划分。本文进行了一些实验来验证PSSPAM仿真平台的有效性可扩展性。同时,实验还表明无论是LBMIC的初始划分还是网络演化时的动态调整,都能显著提高通讯密集型仿真的效率。最后,本文还提出一种面向分布式多主体股票市场仿真的规则驱动编程模型,以规则组合定义股票市场模型中的市场、主体和主体间交互网络,设计并实现了相应的插件式体系结构支持规则定制。
[Abstract]:Multi-agent simulation has been widely used in behavioral finance, and some simulation systems based on ABM modeling have been proposed. However, the traditional stock market simulation system runs in a single computer system, in which the number of principal (Agent) is limited by the computing ability of a single machine. However, more and more Agent are needed by financial researchers in order to obtain more effective data. In order to obtain a more intelligent Agent, more and more simulation rounds of (round) are needed. This paper focuses on large-scale Agent, communication system and simulation scheduling. A distributed multi-agent simulation system, PSSPAM, which is suitable for stock market, including communication system, Agent module, is proposed in this paper. Based on the customizable network topology, agent communicates with other Agent and market center modules through the communication system module, which is based on four loose coupling modules: the market center module and the user interface module, which is based on the customizable network topology. In this paper, a distributed round thread scheduling mechanism is proposed to schedule a large number of Agent threads. In order to improve the PSSPAM simulation system with evolutive social network model in distributed environment, the load balancing between nodes and the total amount of communication between nodes need to be considered. In this paper, a processing and scheduling method, LBMIC, is proposed. When considering that the load imbalance between nodes is lower than a given threshold and the traffic between nodes is minimal, the Agent is divided and deployed to different nodes. LBMIC converts the above problem into a graph partition problem and uses multi-layer graph. Partition algorithm to realize the scheduling of Agent. As social networks evolve, LBMIC dynamically adjusts the initial partition by migrating Agent. In this paper, some experiments are carried out to verify the validity and extensibility of the PSSPAM simulation platform. At the same time, the experiment also shows that both the initial partition of LBMIC and the dynamic adjustment of network evolution can significantly improve the efficiency of communication-intensive simulation. Finally, this paper proposes a rule-driven programming model for distributed multi-agent stock market simulation, and defines the market, the interaction network between agents and agents in the stock market model by rule combination. Design and implement the corresponding plug-in architecture support rule customization.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：F830.91;TP391.9
本文编号：2433417
[Abstract]:Multi-agent simulation has been widely used in behavioral finance, and some simulation systems based on ABM modeling have been proposed. However, the traditional stock market simulation system runs in a single computer system, in which the number of principal (Agent) is limited by the computing ability of a single machine. However, more and more Agent are needed by financial researchers in order to obtain more effective data. In order to obtain a more intelligent Agent, more and more simulation rounds of (round) are needed. This paper focuses on large-scale Agent, communication system and simulation scheduling. A distributed multi-agent simulation system, PSSPAM, which is suitable for stock market, including communication system, Agent module, is proposed in this paper. Based on the customizable network topology, agent communicates with other Agent and market center modules through the communication system module, which is based on four loose coupling modules: the market center module and the user interface module, which is based on the customizable network topology. In this paper, a distributed round thread scheduling mechanism is proposed to schedule a large number of Agent threads. In order to improve the PSSPAM simulation system with evolutive social network model in distributed environment, the load balancing between nodes and the total amount of communication between nodes need to be considered. In this paper, a processing and scheduling method, LBMIC, is proposed. When considering that the load imbalance between nodes is lower than a given threshold and the traffic between nodes is minimal, the Agent is divided and deployed to different nodes. LBMIC converts the above problem into a graph partition problem and uses multi-layer graph. Partition algorithm to realize the scheduling of Agent. As social networks evolve, LBMIC dynamically adjusts the initial partition by migrating Agent. In this paper, some experiments are carried out to verify the validity and extensibility of the PSSPAM simulation platform. At the same time, the experiment also shows that both the initial partition of LBMIC and the dynamic adjustment of network evolution can significantly improve the efficiency of communication-intensive simulation. Finally, this paper proposes a rule-driven programming model for distributed multi-agent stock market simulation, and defines the market, the interaction network between agents and agents in the stock market model by rule combination. Design and implement the corresponding plug-in architecture support rule customization.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：F830.91;TP391.9
【参考文献】
相关期刊论文 前1条
1 高宝俊;宣慧玉;李璐;;一个基于Agent的股票市场仿真模型的Swarm实现[J];系统仿真学报;2006年04期
，本文编号：2433417
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