Distributed computing and modeling &amp;amp; simulation: Speeding up simulations and creating large models

Taylor, Simon J. E.; Ghorbani,; Mustafee, Navonil; Turner, Mark D.; Farkas,; Kite,; Strassburger,

doi:10.1109/wsc.2011.6147748

Cited by 16 publications

(14 citation statements)

References 10 publications

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“…Simon [4] has presented that how distributed computing and grid computing approaches can be applied to Modeling and Simulation(M&S). This paper showed how Volunteer computing, a grid computing approach can be used to speed up simulation on bigger models and benefit M&S researchers and practitioners.…”

Section: Related Workmentioning

confidence: 99%

Distributed computing approach to optimize road traffic simulation

Sinha

Saini

Subramanian

2014

2014 International Conference on Parallel, Distributed and Grid Computing

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Distributed computing is the method of running CPU intensive computations on multiple computers collectively in order to achieve a common objective. Common problems that can be solved on the distributed systems include climate/weather modeling, earthquake simulation, evolutionary computing problems and so on. These type of problems may involve billions or even trillions of computations. A single computer is not capable to finish these computations in short span of time, which is typically in days. Distributed computation helps to solve these problems in hours, which could take weeks to solve on a single computer. Distributed computing generally uses the existing resources of the organization.Traffic simulation is the process of simulating transportation systems through software on a virtual road network. Traffic simulation helps in analyzing city traffic at different time intervals of a single day. Common use cases could be analyzing city wide traffic, estimating traffic demand at a particular traffic junction and so on. This paper discusses about the approach to use distributed computing paradigm for optimizing the traffic simulations. Optimizing simulations involves running a number of traffic simulations followed by estimating the nearness of that simulation to the real available traffic data. This real data could be obtained by either manual counting at traffic junctions, or using the probes such as loop inductors, CCTV cameras etc. This distributed computing based approach works to find the best traffic simulation corresponding to the real data in hand, using evolutionary computing technique.

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Section: Related Workmentioning

confidence: 99%

Distributed computing approach to optimize road traffic simulation

Sinha

Saini

Subramanian

2014

2014 International Conference on Parallel, Distributed and Grid Computing

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“…Experimentation of simulation as-a-service in a cloud are presented in [17] and [18]. References [19] and [20] deal with the porting of traditional parallel/distributed simulation schemes to a master/worker pattern that is more suitable for deployment in a public or private cloud. The Artis/GAIA+ project [21] aims at balancing adaptively the simulation by model decomposition, dynamic partitioning and bottleneck removal, to better suit the variable and dynamic performance levels observed in clouds.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Concurrent Simulation at Work: Fast Performance Prediction of Parallel Programs

Rak¹,

Cuomo

Villano

2012

2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises

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The performance prediction of parallel programs is a time-consuming process due to the need to execute a large number of simulations for different values of parameters linked to the software decomposition and to the target hardware environment. This paper shows how mJADES, a novel environment for running concurrent simulations in the cloud, can provide an effective support to a simulation-based performance prediction framework. The accuracy of predictions and the practical validity of the approach followed are evaluated through experimentation on a simple but realistic case study.

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“…The distributed computing can be still applied to each simulator in a distributed simulation model [60], but the complexity related to the synchronization of the different models can be such that the performance of the simulation (in terms of speed) can be worse than when a single simulation model is developed. This drawback related to the decrease in the efficiency in terms of speed of simulation leads to the following question: "Why is it useful to develop a distributed simulation model?".…”

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confidence: 99%

“…[12], [8], [11], [9], [10], [20], [28], [29], [30], [33], [74], [75] , [48], [50], [47], [49], [58], [53], [59], [56], [68], [70], [68], [73] and [71]). These papers can be considered as introductory to the topic of distributed simulation in civilian domain.…”

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confidence: 99%