Abstract
Networks of Evolutionary Processors (NEP) are a bio-inspired computational model able to solve NP complete problems in an efficient manner. Up to now, the only way to analyze and execute these devices is through hardware and software simulators able to encapsulate the inherent parallelism and the efficiency in their computations. Nowadays, simulators for these models only cover many software applications developed under sequential/parallel architectures over multicore desktop computers or clusters of computers. Most of them, are not able to handle the size of non trivial problems within a massively parallel environment. We consider that cloud computation offers an interesting and promising option to overcome the drawbacks of these solutions. In this paper, we propose a novel parallel distributed architecture to simulate NEPs using on-demand cloud elastic computation. A flexible and extensible simulator is developed in order to demonstrate the suitability and scalability of our architecture with several variants of NEP.
A.O. de la Puente — Results partially supported by Gain Dynamics, project iQuest which funding is applied to the Fondo Europeo de Desarrollo Regional para el Fomento de Sectores Tecnológicos de la Comunidad de Madrid, within the Estrategia Regional de Investigación e Innovación para una Especialización Inteligente (RIS3) in the Programa Operativo de la Comunidad de Madrid 2014-2020.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alarcón, P., Arroyo, F., Mitrana, V.: Networks of Polarized Evolutionary Processors. Information Sciences 265, 189–197 (2014)
Arroyo, F., Castellanos, J., Mitrana, V., Santos, E., Sempere, J.M.: Networks of Bio-inspired Processors, pp. 25–57. TRIANGLE, URV Publications (2012)
Arroyo, F., Gómez Canaval, S., Mitrana, V., Popescu, Ş.: Networks of polarized evolutionary processors are computationally complete. In: Dediu, A.-H., Martín-Vide, C., Sierra-Rodríguez, J.-L., Truthe, B. (eds.) LATA 2014. LNCS, vol. 8370, pp. 101–112. Springer, Heidelberg (2014)
Castellanos, J., Martín-Vide, C., Mitrana, V., Sempere, J.M.: Solving NP-complete problems with networks of evolutionary processors. In: Mira, J., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2084, p. 621. Springer, Heidelberg (2001)
Castellanos, J., Martín-Vide, C., Mitrana, V., Sempere, J.M.: Networks of Evolutionary Processors. Acta Informática. 39, 517–529 (2003)
Díaz, M., Mingo, L., Gómez, N., Castellanos, J.: Implementation of Massive Parallel Networks of Evolutionary Processors (MPNEP): 3-Colorability Problem. Studies in Computational Intelligence 129, 399–408 (2008)
Diez Dolinski, L., Núñez Hervás, R., Cruz Echeandía, M., Ortega, A.: Distributed simulation of P systems by means of map-reduce: first steps with hadoop and P-lingua. In: Cabestany, J., Rojas, I., Joya, G. (eds.) IWANN 2011, Part I. LNCS, vol. 6691, pp. 457–464. Springer, Heidelberg (2011)
García, M., Gutiérrez, R., Martínez, M., Orejuela, E., Pérez, I.: P-Lingua 2.0: A software framework for cell-like P systems. Int. Journal Computers, Communications and Control IV(3), 234–243 (2009)
Gómez, S., Batard, D., Gutiérrez, A.: A Web Implementation of A Generalized NEP. Int. Journal of Information Technologies and Knowledge 7(1), 116–125 (2013)
Jiménez, A.: Simlación de Sistemas P distribuido en WAN. Escuela Politécnica Superior, Universidad Autónoma de Madrid, Tesis Máster (2013)
Lezzi, D., Rafanell, R., Torres, E., Giovanni, R., Blanquer, I., Badia, R.: Programming ecological niche modeling workflows in the cloud. Proc. IEEE Advanced Information Networking and Applications, 1223–1228 (2013)
Margenstern, M., Mitrana, V., Jesús Pérez-Jímenez, M.: Accepting hybrid networks of evolutionary processors. In: Ferretti, C., Mauri, G., Zandron, C. (eds.) DNA 2004. LNCS, vol. 3384, pp. 235–246. Springer, Heidelberg (2005)
Martínez, M., Macías, L., Valencia, L., Riscos, A., Pérez, M.: Accelerated Simulation of P Systems on the GPU: A Survey. Bio-Inspired Computing 472, 308–312 (2014)
Manea, F., Margenstern, M., Mitrana, V., Pérez-Jiménez, M.J.: A New Characterization of NP, P, and PSPACE with Accepting Hybrid Networks of Evolutionary Processors. Theory Comput. Syst. 46, 174–192 (2010)
Manea, F., Martín-Vide, C., Mitrana, V.: On the Size Complexity of Universal Accepting Hybrid Networks of Evolutionary Processors. Mathematical Structures in Computer Science 17, 753–771 (2007)
Navarrete, C., Echeandia, M., Anguiano, E., Ortega, A., Rojas, J.: Parallel simulation of NEPs on clusters. Proc. of Int. Conf. of Web Intelligence and Intelligent Agent Technology, IEEE Computer. Society 3, 171–174 (2011)
Navarrete, C.: Platform for Automatic Parallelization of Sequential Codes using Dynamic Graphs Partitioning and Based on User Adaptable Load Balancing. PhD. Thesis, Escuela Politécnica Superior, Universidad Autónoma de Madrid (2011)
Rees, J.: Fizz Buzz: 101 Spoken Numeracy Games. LDA Date (2002)
Pâun, G.: Computing with membranes. Journal of Computer and System Sciences 61, 108–143 (1998)
Orgaz, P., Gómez S., Ortega, A.: Nepfix Project documentation (2015). https://github.com/pabloogc/Nepfix
Oasis.: Advanced Message Queuing Protocol. http://www.amqp.org/ (Accessed 12 February 2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Canaval, S.G., de la Puente, A.O., González, P.O. (2015). Distributed Simulation of NEPs Based On-Demand Cloud Elastic Computation. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-19258-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19257-4
Online ISBN: 978-3-319-19258-1
eBook Packages: Computer ScienceComputer Science (R0)