A General Simulation Framework for Crowd Network Simulations

Abstract

Crowd network systems have been deemed as a promising mode of modern service industry and future economic society, taking crowd network as the research object and exploring its operation mechanism and laws is of great significance for realizing the effective governance of the government and the rapid development of economy, avoiding social chaos and mutation, and providing a scientific theoretical basis for constructing efficient networked economic and social era. However, because crowd network owes characteristics as large-scale, dynamic and diversified online deep interconnection, and its most results cannot be observed in real world, which cannot be carried out in accordance with traditional way, simulation is of great importance to put forward related researches. This paper adopts a data-driven architecture by deeply analyzing existing large-scale simulation architectures and proposes a novel reflective memory-based framework for crowd network simulations. In this paper, the framework is analyzed from three aspects: hierarchical architecture, functional architecture and implementation architecture. According to the characteristics of crowd network, hierarchical architecture and functional architecture adopt a general structure to decouple related work in a harmonious way. In the implementation architecture, several toolkits for system implementation are designed, which connected by Data Driven Files (DDF), and these XML files constitute a persistent storage layer. From the functional point of view, crowd network simulations obtain the support of reflective memory by connecting the reflective memory cards on different devices, and connect the interfaces of relevant simulation software to complete the corresponding function call. Meanwhile, in order to improve the credibility of simulations, VV&A (Verification, Validation and Accreditation) is introduced into the framework to verify the accuracy of simulation system executions.