NDLI: A data parallel approach to modelling and simulation of large crowd

Content Provider	Springer Nature Link
Author	Yu, Tao Dou, Minggang Zhu, Mao
Copyright Year	2015
Abstract	The modeling and simulation (M&S) of large crowd has become increasingly important in the domain of public security, such as facility planning, disaster response, and anti-terrorism operations. The behavior of a large crowd is highly complex, and the M&S of a large crowd at the individual level therefore demands the support of a scalable and efficient computing technology. In this study, a method was proposed to formulate crowd behavior with the cell automata and multi-agent models, which were successfully mapped onto the MapReduce programming model. A simulation framework was developed upon Hadoop to simulate large crowd scenarios over a cluster. The simulation process was then transformed to a series of parallel operations on data streams. The simulation studies on a large-scale evacuation scenario had indicated that the simulation framework ensured the simulation process’ logic correctness. Experimental results also showed that the Hadoop-based simulation framework could complete five times more tasks while consuming only 19 % CPU time in comparison with the conventional simulation technology.
Starting Page	1307
Ending Page	1316
Page Count	10
File Format	PDF
ISSN	13867857
Journal	Cluster Computing
Volume Number	18
Issue Number	3
e-ISSN	15737543
Language	English
Publisher	Springer US
Publisher Date	2015-04-19
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data parallel computing Modeling and simulation of complex systems Cloud computing MapReduce Hadoop Processor Architectures Operating Systems Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Software

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