The Research Of Scheduling Based On Distributed Multi-Agent Stock Market Simulation

Multi-agent based simulation has been widely used in behavior finance, and several single-processed simulation platforms with Agent-Based Modeling (ABM)have been proposed. However, traditional simulations of stock markets on single processed computers are limited by the computing capability since financial researchers need larger and larger number of agents and more and more rounds to evolve agents'intelligence and get more efficient data. This paper introduces a distributed multi-agent simulation platform, named PSSPAM, for stock market simulation focusing on large scale of parallel agents, communication system and simulation scheduling. A logical architecture for distributed artificial stock market simulation is proposed, containing four loosely coupled modules: communication system, agent module, market module and user interface. Agents exchange messages with each other and with the market based on a customization network topology through a uniform communication system.With a large number of agent threads, the distributed round scheduling strategy is proposed during the simulation. To improve the performance of PS SPAM with evolving social networks in distributed computing environment, the computational load balancing and inter-nodes communication should be considered jointly. This paper proposes a scheduling algorithm called LBMIC to partition the agents onto different computing nodes while keeping the degree of load imbalance lower than a given threshold with minimized inter-nodes communication between agents. LBMIC models the scheduling into a graph partitioning problem and uses the multi-level graph partitioning algorithm to achieve an efficient scheduling. When the network evolves,LBMIC refines the partitioning by migrating parts of the agents. Experiments are conducted to verify the verification of PSSPAM, and the platform shows fair scalability and performance under different parallelism configurations. Also, experiments indicate that LBMIC can efficiently improve the performance of communication-intensive simulation by both initial partitioning and refining partitioning. At last, a rules-driven programming model for distributed multi-agent stock market simulation is introduced,which builds up the definition of the market, the agent and the interaction network between agents in the stock market model through rules, and a plug-in architecture is designed and implemented to support the rules customization.