Effective Distribution of Large Scale Situated Agent-based Simulations

doi:10.5220/0004756903120319

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…We define two patterns to distribute traffic simulations. The patterns are the same than those identified by Rihawi et al [36] for general-purpose situated multiagent simulations, and we believe that they present two representative distribution patterns for this [6]. It consists in the duplication of the transport environment on all processing units, and the equal dispatching of the agents on each one.…”

Section: The Distribution Methodssupporting

confidence: 74%

“…Two approaches exist for distributing simulations [36,40]. The first ignores communication costs and focuses on workload, distributing the agents the most equitably possible between units.…”

Section: The Problemmentioning

confidence: 99%

“…The best way to manage the communications depends on the application and using such general platforms for a traffic simulator would not produce optimal results. Rihawi et al [36] discuss the issues related to multiagent simulation in a distributed virtual environment. The authors describe methods to split the virtual environment in several zones to parallelize the simulation execution.…”

Section: Distributing Traffic Simulationmentioning

confidence: 99%

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Two distribution methods for multiagent traffic simulations

Mastio

Zargayouna

Scemama

et al. 2018

Simulation Modelling Practice and Theory

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Modeling and simulation play an important role in transportation networks analysis. With the widespread use of personalized real-time information sources, the behavior of the simulation depends heavily on individual travelers reactions to the received information. As a consequence, it is relevant for the simulation model to be individual-centered, and multiagent simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we define two multiagent simulation models representing the existing sequential multiagent traffic simulations. The first model is fundamental diagram-based model, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is car-following based, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. Then we define patterns to distribute these simulations in a high-performance environment. The first is agent-based and distributes agents equally between available computation units. The second pattern is environment-based and partitions the environment over the different units. The results show that agent-based distribution is more efficient with fundamental diagram-based model simulations while environment-based distribution is more efficient with car following-based simulations.

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Section: The Distribution Methodssupporting

confidence: 74%

Section: The Problemmentioning

confidence: 99%

Section: Distributing Traffic Simulationmentioning

confidence: 99%

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Two distribution methods for multiagent traffic simulations

Mastio

Zargayouna

Scemama

et al. 2018

Simulation Modelling Practice and Theory

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Load-Balancing for Large Scale Situated Agent-based Simulations

Rihawi

Secq

Mathieu

2015

Procedia Computer Science

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International audienceIn large scale agent-based simulations, memory and computational power requirements can increase dramatically because of high numbers of agents and interactions. To be able to simulate millions of agents, distributing the simulator on a computer network is promising, but raises some issues like: agents allocation and load-balancing between machines. In this paper, we study the best ways to automatically balance the loads between machines in large scale situations. We study the performance of two different applications with two different distribution approaches, and we show in our experimental results that some applications can automatically adapt the loads between machines and get alone a high performance in large scale simulations with one distribution approach than the other

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Patterns to distribute mobility simulations

Mastio¹,

Zargayouna²,

Scemama³

et al. 2016

2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)

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Abstract-Travelers mobility simulation is a powerful tool to test strategies in a virtual environment, without impacting the quality of the real traffic network. However, existing mobility multiagent and microsimulations can only consider a sample of the real volumes of travelers, especially for big regions. With distributed simulations, it would be easier to analyze and predict the status of nowadays networks. This kind of simulations requires big computational power and methods to split the simulation between several machines. This work describes how to achieve such a distribution in a microscopic simulation context, and compare our results with a previous work on macroscopic simulation.

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Effective Distribution of Large Scale Situated Agent-based Simulations

Cited by 4 publications

References 10 publications

Two distribution methods for multiagent traffic simulations

Two distribution methods for multiagent traffic simulations

Load-Balancing for Large Scale Situated Agent-based Simulations

Patterns to distribute mobility simulations

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