MASSIVE SIMULATIONS USING MAPREDUCE MODEL
Artur Krupa
artur.krupa@ee.pw.edu.plPolitechnika Warszawska, Instytut Elektrotechniki Teoretycznej i Systemów Informacyjno-Pomiarowych (Poland)
Bartosz Sawicki
Politechnika Warszawska, Instytut Elektrotechniki Teoretycznej i Systemów Informacyjno-Pomiarowych (Poland)
Abstract
In the last few years cloud computing is growing as a dominant solution for large scale numerical problems. It is based on MapReduce programming model, which provides high scalability and flexibility, but also optimizes costs of computing infrastructure. This paper studies feasibility of MapReduce model for scientific problems consisting of many independent simulations. Experiment based on variability analysis for simple electromagnetic problem with over 10,000 scenarios proves that platform has nearly linear scalability with over 80% of theoretical maximum performance.
Keywords:
mapreduce, cloud computing, platform performance, hadoopReferences
Barker A., Varghese B., Ward J. S., Sommerville I.: Academic Cloud Computing Research: Five Pitfalls and Five Opportunities, in 6th USENIX Workshop on Hot Topics in Cloud Computing, 2014.
Google Scholar
Cunha A. Jr., Nasser R., Sampaio R., Lopes H., Breitman K., Uncertainty quantification through the Monte Carlo method in a cloud computing setting, vol. 185, 2014, 1355–1363.
Google Scholar
D’Angelo G., Marzolla M.: New trends in parallel and distributed simulation: From many-cores to Cloud Computing, Simul. Model. Pract. Theory, 2014, 126.
Google Scholar
Kim B. S., Lee S. J., Kim T. G.: MapReduce Based Experimental Frame for Parallel and Distributed Simulation Using Hadoop Platform, in Proceedings 28th European Conference on Modelling and Simulation, 2012.
Google Scholar
Kondo D., Javadi B., Malecot P., Cappello F., Anderson D. P., Berkeley U. C., Cost-Benefit Analysis of Cloud Computing versus Desktop Grids, 2009.
Google Scholar
Sakellari G., Loukas G., A survey of mathematical models, simulation approaches and testbeds used for research in cloud computing, Simul. Model. Pract. Theory, vol. 39, 2013, pp. 92103.
Google Scholar
Amazon EC2 Pricing, http://aws.amazon.com/ec2/pricing/, access date: [13.08.2015]
Google Scholar
Apache Hadoop project, http://hadoop.apache.org, access date: [21.01.2015]
Google Scholar
Google Cloud Platform Pricing, https://cloud.google.com/compute/pricing/, access date: [13.08.2015]
Google Scholar
Microsoft Azure Pricing, https://azure.microsoft.com/en-gb/pricing/calculator/, access date: [13.08.2015]
Google Scholar
Authors
Artur Krupaartur.krupa@ee.pw.edu.pl
Politechnika Warszawska, Instytut Elektrotechniki Teoretycznej i Systemów Informacyjno-Pomiarowych Poland
Authors
Bartosz SawickiPolitechnika Warszawska, Instytut Elektrotechniki Teoretycznej i Systemów Informacyjno-Pomiarowych Poland
Statistics
Abstract views: 172PDF downloads: 43
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.