Actas de las XX Jornadas de Paralelismo A Coruña, 16,17 y 18 de septiembre de 2009 Organiza: Grupo de Arquitectura de Computadores Universidade da Coruña

EDITORES: Ramón Doallo Biempica Manuel Arenaz Silva Patricia González Gómez

ACTAS DE LAS XX JORNADAS DE PARALELISMO Reservados todos los derechos. Ni la totalidad ni parte de esta publicación puede reproducirse o transmitirse por ningún procedimiento electrónico o mecánico, incluyendo fotocopia, grabación magnética o cualquier almacenamiento de información y sistema de recuperación, sin el permiso previo y por escrito de las personas titulares del copyright. Derechos reservados !2009 respecto a la primera edición en español por los autores. Derechos reservados !2009 Servizo de Publicacións, Universidade da Coruña. ISBN: 84-9749-346-8 Depósito legal: C 2318-2009 Edita: Servizo de Publicacións, Universidade da Coruña http://www.udc.es/publicaciones c/e: publica@udc.es Imprime: Macrom. Tel. 981 135 577 http://www.macrom.es c/e: macrom@macrom.es

XX JORNADAS DE PARALELISMO Comité de coordinación

Ramón Beivide Palacio (UC)

Jesús Carretero Pérez (UCIII)

José Duato Marín (UPV)

Inmaculada García Fernández (UAL)

Antonio Garrido del Solo (UCLM)

Emilio López Zapata (UMA)

Emilio Luque Fadón (UAB)

Pedro de Miguel Anasagasti (UPM)

Alberto Prieto Espinosa (UGR)

Francisco José Quiles Flor (UCLM)

Ana Ripoll Aracil (UAB)

Francisco Tirado Fernández (UCM)

Mateo Valero Cortés (UPC)

Victor Viñals Yúfera (UZ)

Comité Organizador (UDC)

Ramón Doallo Biempica

Patricia González Gómez

Manuel Arenaz Silva

Margarita Amor López

Diego Andrade Canosa

Iván Díaz Álvarez

Basilio B. Fraguela Rodríguez

Gonzalo Iglesias Aguiño

Guillermo López Taboada

María J. Martín Santamaría

Emilio Padrón González

Xoán C. Pardo Martínez

Gabriel Rodríguez Álvarez

José R. Sanjurjo Amado

Juan Touriño Domínguez

Entidades Patrocinadoras

Vicerreitorado de Investigación. Universidade da Coruña

Ministerio de Ciencia e Innovación. Gobierno de España

Consellería de Educación e Ordenación Universitaria. Xunta de Galicia

Concello da Coruña

Red Gallega de Computación de Altas Prestaciones

Red Mathematica Consulting & Computing de Galicia

HP España

Bull España

Entidades Colaboradoras

Facultade de Informática. Universidade da Coruña

Oficina de Cursos e Congresos. Universidade da Coruña

Turismo Coruña

Turgalicia

Entidades Organizadoras

Grupo de Arquitectura de Computadores. Universidade da Coruña

Sociedad de Arquitectura y Tecnología de Computadores (SARTECO)

Presentacion

Quisieramos aprovechar estas lıneas para daros la bienvenida a las XX Jornadasde Paralelismo, que tienen lugar en A Coruna entre los dıas 16 y 18 de septiembre de2009. La celebracion de este evento supone una vez mas, y ya van veinte, el encuentrode numerosos investigadores para intercambiar experiencias y presentar ponencias quereflejan su trabajo en el ambito de la arquitectura de sistemas de computadores.

El numero de asistentes a las Jornadas de Paralelismo ha ido creciendo con cada nue-va edicion. En este ano se han recibido 119 artıculos, y el numero de participantes con elque contamos asciende a mas de 170. Finalmente el programa ha quedado estructuradoen mas de 20 horas de trabajo, repartidas en 24 sesiones que cubren los temas siguien-tes: tecnologıas grid y plataformas distribuidas; aplicaciones; redes y comunicaciones;arquitecturas, algoritmos y aplicaciones sobre aceleradores hardware; arquitecturas deprocesador, multiprocesadores y chips multinucleo; algoritmos y tecnicas de programa-cion paralelas; compilacion para sistemas de altas prestaciones; docencia en Arquitecturay Tecnologıa de Computadores (ATC); y evaluacion de prestaciones.

Contamos en esta edicion con dos conferencias plenarias, impartidas por los profe-sores Mark Baker, de la Universidad de Reading (Reino Unido), sobre Cloud Computingy sus servicios, y Enrique Quintana-Ortı, de la Universitat Jaume I, sobre tecnicas su-perescalares en la construccion de bibliotecas numericas para procesadores multinucleoy GPUs. Ademas tendremos la oportunidad de asistir a dos sesiones tecnicas imparti-das por HP Espana y Bull Espana, y a la asamblea de la Sociedad de Arquitectura yTecnologıa de Computadores (SARTECO).

El programa de las Jornadas incluye tambien dos mesas redondas sobre asuntos queactualmente estan provocando un intenso debate entre la comunidad universitaria. Laprimera de ellas trata sobre como trasladar las fichas del Grado y Master en Informati-ca a los nuevos planes de estudio. La segunda mesa redonda abordara el tema de latransferencia de conocimiento universidad-empresa.

No queremos acabar esta presentacion sin mostrar nuestro agradecimiento a todoslos organismos y entidades, publicos y privados, que han colaborado en el desarrollo deestas Jornadas, en concreto, a la Universidade da Coruna, al Concello de A Coruna, ala Xunta de Galicia, al Ministerio de Ciencia e Innovacion, a la Red Gallega de Compu-tacion de Altas Prestaciones, a la Red Mathematica Consulting & Computing de Galicia,a las companıas HP y Bull, y finalmente a todos los miembros del Grupo de Arquitecturade Computadores de la UDC que han brindado su apoyo para la organizacion de estecongreso.

Finalmente, queremos agradecer vuestro interes y participacion en estas XX Jorna-das de Paralelismo, que esperamos cumplan con vuestras expectativas.

Comite Organizador de lasXX Jornadas de ParalelismoA Coruna, 16-18 de Septiembre de 2009

Indice

Algoritmos y tecnicas de programacion paralelas

Una Biblioteca Numerica Paralela para UPCJ. Gonzalez-Domınguez, M.J. Martın, G.L. Taboada, J. Tourino, R. Doallo

y A. Gomez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Concurrencia-paralelismo Auto-gestionada por Procesos Multihebrados enSistemas MulticoreJ.F. Sanjuan-Estrada, L.G. Casado, J.A. Martınez, M. Soler e I. Garcıa . . . . . 7

Modelado y Autooptimizacion en Esquemas Paralelos de BacktrackingM. Quesada y D. Gimenez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Algunas Consideraciones sobre las Estructuras Arbol en OpenMPC. Leon, G.M. Valladares y J.M. Rodrıguez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Parallelization of the Quadratic Assignment Problem on the CellJ.A. Pascual, J.A. Lozano y J.M. Alonso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Precondicionadores Paralelos para Sistemas Suavemente no LinealesH. Migallon, V. Migallon y J. Penades . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Modelado Paralelo de Circuitos Integrados Desarrollados con SystemC parauna Red de Datos PLCV. Galiano, M. Martınez, H. Migallon y D. Perez-Caparros . . . . . . . . . . . . . . . . . . 39

Metodos Paralelos para Decodificacion de Senales en Sistemas InalambricosMIMOR.A. Trujillo, A.M. Vidal y V.M. Garcıa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Evaluacion de Propuestas Master-slave para la Reconstruccion 3D en To-mografıa ElectronicaM.L. da Silva, J. Roca-Piera y J.J. Fernandez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Hiperheurısticas Paralelas Multi-objetivo. Aplicacion a las Funciones RC. Leon, G. Miranda, F.J. Morales y C. Segura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Paralelizacion de Magpack: Propiedades Magneticas de Sistemas Anisotro-pos de Alta NuclearidadE. Ramos, J.E. Roman, S. Cardona, J.M. Clemente-Juan y E. Coronado . . . . 63

Aplicaciones

Resolucion del Problema Reporting Cells mediante Computacion Cluster yun Equipo Paralelo de Algoritmos EvolutivosD.L. Gonzalez-Alvarez, A. Rubio-Largo, M.A. Vega-Rodrıguez, S. Almeida-

Luz, J.A. Gomez-Pulido y J.M. Sanchez-Perez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

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Ultra-fast Tomographic Reconstruction with a Highly Optimized Back-projection AlgorithmJ.I. Agulleiro, E.M. Garzon, I. Garcıa y J.J. Fernandez . . . . . . . . . . . . . . . . . . . . . . 75

Alternativas de Optimizacion Multiobjetivo Paralela en la Prediccion deEstructuras de ProteınasJ.C. Calvo, J. Ortega y M. Anguita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Aplicacion de Estadısticos para el Analisis y el Estudio de la Estimacion yCompensacion de Movimiento en MPEG-2A.J. Dıaz-Honrubia, G. Fernandez-Escribano y P. Cuenca . . . . . . . . . . . . . . . . . . . 87

La Necesidad de Mejoras en Prediccion y Computacion para el ModeloMM5H. Ihshaish, A. Sairouni, A. Cortes y M.A. Senar . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Entorno de Desarrollo de Heurısticas Distribuidas Utilizando OSGiP. Garcıa-Sanchez, P.A. Castillo, J. Gonzalez, J.J. Merelo y A.A. Escamez . . . . 99

Enhanced Non-embedded Lower Tree Wavelet EncoderO. Lopez, M. Martınez-Rach, P. Pinol, M.P. Malumbres y J. Oliver . . . . . . . . . 105

Resolucion del Problema del Lıder-seguidor mediante Algoritmos ParalelosJ.L. Redondo, A.G. Arrondo, I. Garcıa y P.M. Ortigosa . . . . . . . . . . . . . . . . . . . . . 111

A Partitioning Method Based on Convex Hulls for Crowd SimulationsG. Vigueras, M. Lozano, J. M. Orduna y F. Grimaldo . . . . . . . . . . . . . . . . . . . . . . . 117

PySParNLCG: Interfaz-librerıa del Metodo del Gradiente Conjugado noLineal Paralelo para Sistemas DispersosH. Migallon, V. Migallon y J. Penades . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Using Evolutionary Intelligent Systems to Enhance Forest Fire Spread Pre-dictionK. Wendt, A. Cortes y T. Margalef . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

Resolucion del Problema de la Planificacion de Frecuencias en Redes GSMUtilizando GRASP y Computacion GridJ.M. Chaves-Gonzalez, R. Hernando-Carnicero, M.A. Vega-Rodrıguez, J.A.

Gomez-Pulido y J.M. Sanchez-Perez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

MapReduce Implementation of High Resolution Irradiation ModelS. Tabik, L.F. Romero, O. Plata y G. Bandera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Solving Half Billion Unknowns with High Scalability Multipole MethodJ.C. Mourino, A. Gomez, J.M. Taboada, L. Landesa, F. Obelleiro y J.L.

Rodrıguez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Arquitecturas de procesador, multiprocesadores y chips multinucleo

Soporte Multicast y Broadcast para una Red Detallada de Interconexion enGEMSJ. Camacho y J. Flich . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

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Mejorando el Rendimiento SMT: Algoritmo Genetico para la Configuracionde Caches ReconfigurablesJ. Dıaz, J.I. Hidalgo, F. Fernandez, O. Garnica y S. Lopez . . . . . . . . . . . . . . . . . . 159

A Cache Filtering Mechanism for Hardware Transactional Memory Sys-tems Decoupled from CachesL. Orosa, J.D. Bruguera y E. Antelo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

Implementacion de un Predictor de Ultimo Uso con DecaimientoJ. Alastruey, T. Monreal, V. Vinals y M. Valero . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

A Novel Transactional Book-keeping Scheme for Tiled CMPsR. Titos-Gil, M.E. Acacio y J.M. Garcıa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

On the Feasibility of Fat-tree Topologies for Network-on-chipF. Gilabert, D. Ludovici, S. Medardoni, C. Gomez, M.E. Gomez, P. Lopez,

G.N. Gaydadjiev y D. Bertozzi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

A Workload-Aware ROB Sharing Strategy for Multithreaded ProcessorsR. Ubal, J. Sahuquillo, S. Petit y P. Lopez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

Energy-Efficient Power Budget MatchingJ.M. Cebrian , J.L. Aragon, J.M. Garcıa, P. Petoumenos y S. Kaxiras . . . . . . . 195

Limiting the Storage Requirements for Preventing Starvation in TokenCoherenceB. Cuesta, A. Robles y J. Duato . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

An Efficient Memory Disambiguation Hardware in an x86 ArchitectureR. Apolloni, D. Chaver, F. Castro, L. Pinuel, M. Prieto y F. Tirado . . . . . . . . . 207

RUFT: Reduced Unidirectional Fat–treeC. Gomez, F. Gilabert, M.E. Gomez, P. Lopez, y J. Duato . . . . . . . . . . . . . . . . . . 213

Achieving Directory Scalability and Lessening Network Traffic in Many-core CMPsA. Ros, M.E. Acacio y J.M. Garcıa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

Phit Reduction Techniques to Deal with Process Variability in NoC LinksC. Hernandez, F. Silla, V. Santonja y J. Duato . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

Logic Tree-Based Broadcast Support for CMPsS. Rodrigo, J. Flich y J. Duato . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231

Needs of CQoS for Future, Many-core CMPs to Support Server Consolida-tion and Cloud ComputingJ. Merino, L.G. Menezo y V. Puente . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

Performance Analysis of Non-Uniform Cache Architecture Policies forChip-Multiprocessors Using the Parsec v2.0 Benchmark SuiteJ. Lira, C. Molina y A. Gonzalez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241

Diseno de Signaturas Explotando Localidad en Memoria TransaccionalR. Quislant, E. Gutierrez, O. Plata y E.L. Zapata . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

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Arquitecturas del subsistema de memoria y almacenamiento secundario

Cache con Reequilibrio de ConjuntosD. Rolan, B.B. Fraguela y R. Doallo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253

Ahorro Energetico en un Sistema de Almacenamiento Hıbrido Compuestopor un Disco Duro y Varias Memorias FlashL. Prada, J.D. Garcıa, J. Carretero y F. Garcia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

Memoria Dinamica en Caches de Datos de Primer Nivel sin Necesidad deRefrescoA. Valero, V. Lorente, J. Sahuquillo, S. Petit, P. Lopez y J. Duato. . . . . . . . . . . 265

Nanotubos de Carbono para Conexiones en Caches: Arquitecturas masalla del CMOSJ. Ortiz, D. Suarez, V. Vinals y M. Villarroya-Gaudo . . . . . . . . . . . . . . . . . . . . . . . 271

Arquitecturas, algoritmos y aplicaciones sobre aceleradores hardware

Implementation and Evaluation of Collective Primitives for Dual Cell-basedBladesE. Gaona, J.F. Peinador y M.E. Acacio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277

Highly Parallel Image Processing on a Massively Parallel Processor ArrayR.R. Osorio, C. Dıaz-Resco y J.D. Bruguera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283

Analysis of P Systems Simulation on CUDAG.D. Guerrero, J.M. Cecilia, J.M. Garcıa, M.A. Martınez-del-Amor, I.

Perez-Hurtado y M.J. Perez-Jimenez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289

Metodologıa para la Generacion y Evaluacion Automatica de Hardware Es-pecıficoC. Gonzalez, D. Jimenez-Gonzalez, X. Martorell, C. Alvarez y G.N. Gay-

dadjiev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295

Implementacion en FPGA de la Transformada Generalizada de HoughS.R. Geninatti, J.I. Benavides, M. Hernandez, N. Guil y J. Gomez . . . . . . . . . . 301

Analisis de la Capacidad Stream Management de CUDA para Procesamien-to de VıdeoJ. Gomez-Luna, J.M. Gonzalez-Linares, J.I. Benavides y N. Guil . . . . . . . . . . . . 307

Acelerando el Producto Matriz Dispersa Vector en GPUsF. Vazquez, E.M. Garzon, J.A. Martınez y J.J. Fernandez . . . . . . . . . . . . . . . . . . 313

Implementacion de una Memoria Cache Reconfigurable, sobre FPGA’s, enVHDL y HANDEL CA.D. Santana, L. D. Tapia, E. Herruzo, J.I. Benavides . . . . . . . . . . . . . . . . . . . . . . 319

Using Hybrid CPU-GPU Platforms to Accelerate the Computation of theMatrix Sign FunctionA. Remon y E.S. Quintana-Ortı . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

Simulacion de Aguas Poco Profundas en una GPU mediante Brook+J. Lobeiras, M. Amor, M. Arenaz, B.B. Fraguela, J.A. Garcıa y M.J. Castro . . . . 327

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Programacion de Alto Rendimiento en el Procesador Cell: Aplicacion aSimulacion de FluıdosC.H. Gonzalez, B.B. Fraguela, D. Andrade, J.A. Garcıa y M.J. Castro . . . . . . 333

Paralelizacion de la Estimacion de Movimiento para Tamano de BloqueVariable Utilizando Aritmetica OLAJ. Olivares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339

Evaluation of the Cell Broadband Engine Running Continuous Estimationof Distribution AlgorithmsC. Perez-Miguel, J. Miguel-Alonso y A. Mendiburu . . . . . . . . . . . . . . . . . . . . . . . . . 343

Proyeccion de Algoritmos de Resolucion de Sistemas Tridiagonales en laTarjeta GraficaJ. Lamas-Rodrıguez, M. Boo, D.B. Heras y F. Arguello . . . . . . . . . . . . . . . . . . . . . 349

Compilacion para sistemas de altas prestaciones

Paralelizacion Especulativa de la Triangulacion de DelaunayA. Garcıa-Yaguez, D.R. Llanos, D. Orden y B. Palop . . . . . . . . . . . . . . . . . . . . . . . 355

Generacion Automatica de Codigo Hıbrido (MPI+OpenMP) con llcR. Reyes, A.J. Dorta, F. Almeida y F. de Sande . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361

Computacion de altas prestaciones sobre arquitecturas paralelas heterogeneas

Analisis de prestaciones de clusters de FPGA frente a clusters de compu-tadores en aplicaciones de computacion evolutivaJ.A. Gomez-Pulido , M.A. Vega-Rodrıguez y J.M. Sanchez-Perez . . . . . . . . . . . . 367

BP-TCoffee: A Balanced Guide Tree Heuristic for improving Parallel T-Coffee PerformanceM. Orobitg, F. Guirado, A. Montanola y F. Cores . . . . . . . . . . . . . . . . . . . . . . . . . . 373

Planificacion en Multicore Asimetricos con Repertorio Comun de Instruc-cionesJ.C. Saez, H. Vegas, M. Prieto y A. Fedorova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379

ADITHE: An Approach to Optimise Iterative Computation on Heteroge-neous MultiprocessorsG. Ortega, J. A. Martınez, E.M. Garzon, A. Plaza e I. Garcıa . . . . . . . . . . . . . . . 385

Docencia en ATC

Entorno Virtual de Aprendizaje y Autoevaluacion de Logica Proposicionaly CombinacionalF. Guirado, C. Ansotegui, F. Cores, J.L. Lerida, C. Roig, F. Gine y F.

Solsona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391

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Montaje de un Laboratorio de Desarrollo de Software Empotrado Usandosolo Herramientas Open SourceJ. Gonzalez, J.M. Palomares y P. Garcıa-Sanchez . . . . . . . . . . . . . . . . . . . . . . . . . . . 397

Forge: A Multi-purpose Platform for Measuring Energy and Temperaturein Commodity PCsS. Gutierrez, O. Benedı, D. Suarez, J.M. Marın y V. Vinals . . . . . . . . . . . . . . . . . 403

Un modulo para la Gestion de Practicas de Tecnologıa de Computadoresen MoodleJ. Ramos, S. Romero, M.A. Trenas, F. Corbera y E. Gutierrez . . . . . . . . . . . . . . 409

Evaluacion de prestaciones

Thread Allocation Issues for Irregular Codes in the Finisterrae SystemJ.A. Lorenzo, F.F. Rivera, D.B. Heras, J.C. Cabaleiro, T.F. Pena, J.C.

Pichel y D.E. Singh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415

Simulating Server ConsolidationA. Garcıa-Guirado, R. Fernandez-Pascual y J.M. Garcıa . . . . . . . . . . . . . . . . . . . . 421

Administracion Eficiente de Recursos de Computo Paralelo en un EntornoHeterogeneoR. Muresano, D. Rexachs y E. Luque . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427

Ubuntu Server de 32 bits v.s. 64 bitsA. Tenorio y L. Tomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433

El Criterio de Informacion de Akaike en la Obtencion de Modelos Estadısti-cos de RendimientoD.R. Martınez, J.L. Albın, J.C. Cabaleiro, T.F. Pena, F.F. Rivera y V.

Blanco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439

Redes y comunicaciones

Futuros Avances en la Transcodificacion de Vıdeo DVCA. Corrales-Garcia , J.L. Martınez y F.J. Quiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445

SEM: An Improved Congestion Management Mechanism for MINsJ.L. Ferrer, E. Baydal, A. Robles, P. Lopez y J. Duato . . . . . . . . . . . . . . . . . . . . . . 451

MRR: Multicast Rotary RouterJ. Abad, V. Puente y J.A. Gregorio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457

Automating the Modeling and Simulation Life Cycle of Mobile Ad hoc Net-worksP.P. Garrido, M.P. Malumbres, J. Llor, E. Torres y C.T. Calafate . . . . . . . . . . . 463

Planificacion de una Red Sensorial para la Monitorizacion de VinedosM.L. Santamarıa y S. Galmes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469

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Analisis de Opciones de Externalizacion de Interfaces de Red con ModelosHDLW.M. Haider, J. Ortega, A.F. Dıaz y A. Ortiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475

Sistema Integrado de Simulacion de NoCs.F. Trivino, F. J. Andujar, A. Ros, J.L. Sanchez y F.J. Alfaro . . . . . . . . . . . . . . . . 481

Multipath Fault-Tolerant Routing Policies for High-Speed InterconnectionNetworksG. Zarza, D. Lugones, D. Franco y E. Luque . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487

Interconexion de Sistemas Basados en HyperTransportJ.A. Villar, F.J. Alfaro, J.L. Sanchez, J. Duato . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

Reducing User-Perceived Latencies and Web Server Load by PrevalidatingWeb ObjectsJ. Domenech, J.A. Gil, J. Sahuquillo y A. Pont . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499

FBICM: Control de Congestion Eficiente y Escalable en Redes de Interco-nexion con Encaminamiento Distribuido DeterministaJ. Escudero-Sahuquillo, P.J. Garcıa, F.J. Quiles, J. Flich y J. Duato . . . . . . . . . 505

Biblioteca de Primitivas Colectivas en Paso de Mensajes para Java en Sis-temas Multi-coreS. Ramos, G.L. Taboada, J. Tourino y R. Doallo . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

Biblioteca de Operaciones Colectivas para el Lenguaje de Programacion Pa-ralela UPCJ. Andion, G.L. Taboada, J. Tourino y R. Doallo . . . . . . . . . . . . . . . . . . . . . . . . . . . 517

Evaluation of Traffic Exposure in Anonymous Routing Protocols for MA-NETsM. Nacher, C.T. Calafate, J.C. Cano y P. Manzoni . . . . . . . . . . . . . . . . . . . . . . . . . . 523

Cross-Layer Evaluation of a VANET-based Content Delivery FrameworkJ. Marquez, C.T. Calafate, J.C. Cano y P. Manzoni . . . . . . . . . . . . . . . . . . . . . . . . . 529

The Watchdog: a Black-hole Intrusion Tolerance Implementation for Ad-hoc NetworksJ. Hortelano, J.C. Ruiz y P. Manzoni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535

Evaluating Warning Dissemination in VANETsF.J. Martınez, J.C. Cano, C.T. Calafate y P. Manzoni . . . . . . . . . . . . . . . . . . . . . . 541

A Multi-objective Genetic Approach for Concurrent Mapping and Routingin Networks-on-ChipR. Tornero, V. Sterrantino, M. Palesi y J.M. Orduna . . . . . . . . . . . . . . . . . . . . . . . . 547

Hardware Study of the SCFQ-CA and DRR-CA Egress Link SchedulersJ.M. Claver, R. Martınez, F.J. Alfaro y J.L. Sanchez . . . . . . . . . . . . . . . . . . . . . . . . 553

Experimenting with the IEEE 802.11e Technology in a Real TestbedA. Torres, C.T. Calafate, J.C. Cano y P. Manzoni . . . . . . . . . . . . . . . . . . . . . . . . . . . 559

Analyzing the Behavior of Acoustic Link Models in Underwater WirelessSensor NetworksJ. Llor, E. Torres, P. Garrido y M.P. Malumbres . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565

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Thin trees: Cost-Effective Tree-like NetworksJ. Navaridas, J. Miguel-Alonso y W. Denzel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571

Evaluacion en Servidores Web de una Interfaz de Red DistribuidaA. Ortiz, J. Ortega, A.F. Dıaz y A. Prieto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577

Esquemas Dinamicos de Distribucion de Claves en Redes Peer-to-peer Mul-timediaJ.A.M. Naranjo, J.A. Lopez-Ramos y L.G Casado . . . . . . . . . . . . . . . . . . . . . . . . . . . 583

Upper Bound of Web Page Latency Saving in an Experimental PrefetchingSystemB. de la Ossa, J. Sahuquillo, A. Pont y J.Gil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587

Tecnologıas grid y plataformas distribuidas

Evaluacion de Prestaciones de Meta-Planificadores de Grid Conscientes dela RedA. Caminero, O. Rana, B. Caminero y C. Carrion . . . . . . . . . . . . . . . . . . . . . . . . . . . 593

GloBeM: Un Modelo Global del GridJ. Montes, A. Sanchez y M.S. Perez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599

Adaptive Task Scheduling for Multi-job MapReduce EnvironmentsJ. Polo, D. de Nadal, D. Carrera, Y. Becerra, V. Beltran, J. Torres y E.

Ayguade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605

Web Services Based Scheduling in OpenCFA. Santos, F. Almeida, V. Blanco y J.C. Castillo . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611

Un Sistema Eficiente para Recuperacion de Vıdeo en Entornos GridP. Toharia, A. Sanchez, O.D. Robles y J.L. Bosque . . . . . . . . . . . . . . . . . . . . . . . . . . 617

The Impact of Fault Tolerance Configuration on Different ApplicationsL. Fialho, G. Santos, A. Duarte, D. Rechaxs y E. Luque . . . . . . . . . . . . . . . . . . . . 623

Arquitectura de E/S Paralela de Alta Prestaciones para Sistemas Blue Ge-neJ.G. Blas, F. Isaila y J. Carretero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629

Accelerating Computing through Virtualized Remote GPUsJ. Duato, A.J. Pena, F. Silla, F.D. Igual, R. Mayo y E.S. Quintana-Ortı . . . . . 635

Addressing the Use of Cloud Computing for Web Hosting ProvidersJ. Oriol-Fito y J. Guitart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641

Adaptacion de un Simulador de Nanodispositivos para la InfraestructuraGrid del FORMIGAR. Valın, A. Garcıa-Loureiro, C. Fernandez, D. Cordero, C. Fernandez y J.

Lopez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647

Towards High-Level SLAs with Heterogeneous Workloads: Job Resource Re-quirements Prediction for Deadline SchedulersG. Reig, J. Alonso y J. Guitart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653

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Replicacion de Datos en PVFS2 para Conseguir Tolerancia a FallosE. Nieto-Tovar, R. Hernandez-Palacios, H.E. Camacho-Cruz, A.F. Dıaz-

Garcıa, M. Anguita-Lopez y J. Ortega-Lopera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659

Implementacion Grid de Algoritmo Scatter Search. Aplicacion a la Opti-mizacion de Dispositivos de Fusion NuclearA. Gomez-Iglesias, M.A. Vega-Rodrıguez, F. Castejon, J.M. Sanchez-Perez,

M. Cardenas-Montes y E. Morales-Ramos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665

Elastic Management of Tasks in Virtualized EnvironmentsI. Goiri, J. Guitart y J. Torres . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671

Planificacion Avanzada en GridWayL.T. Bolıvar, C. Carrion y M.B. Caminero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677

Expanding a Node Memory Beyond the Node LimitsH. Montaner, F. Silla y J. Duato . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683

Modelo de Asignacion de Tareas en Entornos Multicluster Heterogeneos yno DedicadosH. Blanco, J.L. Lerida, F. Guirado y A.I. Usie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689

A Non-Additive Negotiation Model for Utility Computing MarketsM. Macıas y J. Guitart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695

Mejora del Servicio de Informacion de GT4 a traves del Common Infor-mation ModelG. Fernandez, I. Dıaz, P. Gonzalez, M. J. Martın y J. Tourino . . . . . . . . . . . . . . 701

xvii

Automating the Modeling and SimulationLife Cycle of Mobile Ad hoc Networks

P. Pablo Garrido, Manuel P. Malumbres, Jesus Llor, Esteban Torres1 ,

and Carlos T. Calafate2

Resumen— The use of simulation has grown in thenetworking field, specially for Mobile Ad hoc Net-works (MANETs). Simulation plays a very importantrole when evaluating routing protocols. However, thevalidity of published simulation results has been ques-tioned by several authors. One of the main reasonsexposed in the research literature is related to topol-ogy modeling, since the specific scenarios chosen havea great impact on simulation results. Most simula-tion studies usually pick a few randomly generatedsimulation scenarios leading to a lack of credibility ofsuch works. Several works have been carried out re-cently in which the authors define a set of metrics andpropose several recommendations for properly choos-ing the simulation scenarios. However, it is almostimpossible to follow those recommendations withoutthe support of specific tools, specially when networknodes are mobile. In this paper, a simulation frame-work is presented supporting both topology model-ing and the simulation automation based on the OP-NET Modeler simulator. The proposed set of toolsis helpful at constructing rigorous MANET scenarios,as well as at performing other necessary tasks suchas running simulations, extracting the selected datafrom the simulation results, and generating graphsand reports. These tools have been ported to themost common platforms, and can be executed eithersequentially or in parallel on a Condor cluster.

Palabras clave— Modeling, Simulation Life Cycle,Automation, OPNET Modeler, Condor

I. Introduction

MOBILE Ad hoc Networks (MANETs) are com-posed by a group of wireless stations that com-

municate with each other to form a network. Thiskind of networks does not require any sort of infras-tructure. Instead, a source node can communicatewith a destination node directly (if both are withinthe communication range) or through a set of inter-mediate nodes that forward any incoming packet tothe next neighbor in the established path (multihop).

Implementing a real MANET scenario is costlyand both resource and time consuming. On theother hand, these networks are mathematically in-tractable [1]. So, most published research worksabout MANETs use simulation tools [2], but the reli-ability of such simulation studies has been questioned[3], [4], [5].

Modeling appropriate scenarios and realistic mo-bility patterns is an important task when simulatingMANETs, since small differences in the scenario’stopology have a large impact on routing algorithms,as stated in [6] and [7]. However, the scenarios are

1Dpto. Fısica y Arquitectura Computadores, Univ.Miguel Hernandez, e-mail: {pgarrido, mels, jllor,esteban.torres}@umh.es.

2Dpto. Informatica de Sistemas, Univ. Politecnica Valencia,e-mail: calafate@disca.upv.es.

usually randomly generated in much of the researchworks without any kind of validation, leading to alack of accuracy. So, a way to characterize and vali-date simulation scenarios prior to evaluating routingprotocols is required. This problem has been widelyaddressed in the literature [2], [7], [8], [9], [10], [11].For example, two metrics are used in [8] for con-structing valid MANET scenarios in order to evalu-ate routing protocols: (1) average shortest path hopcount, and (2) average network partitioning. Somemetrics are defined in [10] in order to quantify thepartitioning degree too.

However, putting into practice the suggestionsgiven in such works is quite complicated without thehelp of adequate software tools. According to [2],some of the most commonly used simulator tools formodeling wireless networks are ns-2 [12], GloMoSim,and OPNET Modeler [13]. However, the OPNETModeler simulator is not as extended as ns-2, andsuch kind of tools is not available. Thus, in this pa-per we present a simulation framework that is ableto meet these research goals.

The remainder of the paper is organized as fol-lows. Firstly, an overview of the developed simula-tion framework is presented in section II. In Sec-tion III, the modeling of MANET scenarios and themethodology for running a set of simulations are de-scribed. Section IV describes how to process the re-sulting data from the simulations in order to generategraphs and reports that make easier the analysis ofsuch data. Finally, conclusions are drawn in SectionV, along with references to future work.

II. Framework overview

This paper presents a set of tools developed specif-ically for the OPNET Modeler simulator in order toautomate, not only the modeling, but also all thephases involved with the simulation cycle. The spe-cific purpose of each one is briefly described on thefollowing:

• simul topogen: this is a topology generator thatfollows some of the recommendations proposedin the literature in order to build appropriateMANET scenarios.

• simul simul : used for running one or several sim-ulation sets, specifying, for example, differentrandom number seeds or a list of values for someparameter.

• simul data: used for extracting data from allthe binary files obtained as result of running thesimulation sets. This program invokes another

463 A Coruña, 16-18 de septiembre de 2009

program called, ov2txt, which extracts data fromthe result file (.ov) for each individual simulationand saves it to a text file.

• simul graphs: used for generating graphs fromthe extracted data.

• simul reports: used for building a report for eachprogrammed simulation set.

All these tools are command-line programs andwere developed using the C programming language.This has allowed to migrate them easily to severalplatforms such as MS-Windows, UNIX-like systems(e.g. Linux), or MacOS X. However, running some ofthese programs will be only possible if the OPNETModeler has been ported to that specific platform.Currently, it is available for MS-Windows and Linux(since v11.5). For example, the simul topogen toolneeds the External Model Access (EMA) packagefrom OPNET in order to building MANET scenar-ios, and the simul simul tool invokes the op runsim

program to run each simulation from the commandline. The same applies to the simul data programbecause the program ov2txt is invoked, requiring theEMA package too.

On the other hand, the remainder tools do notneed the OPNET Modeler simulator, and so they canbe executed on a different platform if necessary. Inparticular, simul graphs can be executed on any plat-form in which either the R package [14] or gnuplot[15] are available. About the simul reports tool, thisonly needs a LATEX[16] distribution, which is avail-able in many platforms.

Finally, all the developed tools are able to workboth sequentially and in parallel when a cluster sys-tem is available (e.g. Condor [17]). This is achievedeasily by specifying a simple option from command-line, drastically reducing the time spent in each stepof the whole process.

III. Modeling and Simulation

A. Modeling MANET scenarios

Network simulators typically receive a scenario asinput, which, in the particular case of MANETs, de-scribes the mobility pattern of a group of nodes.Both the OPNET Modeler simulator and the pro-posed simul topogen utility allow the user to ran-domly set the initial node placement within a specificarea, as well as setting a mobility profile.

With regard to the mobility pattern of nodes, sev-eral mobility models have been proposed [18], butonly a few are available in OPNET Modeler. WithOPNET Modeler it is possible to use either a cen-tralized or a decentralized approach when modelingthe mobility pattern. In the centralized approach,a common mobility profile is used for all the nodes,and a ’Mobility Config’ object is needed. This ob-ject holds the random mobility parameters for theRandom Waypoint mobility model (RWM), namely,the area of the movement, the speed, and the pausetime. This is the most common mobility modelfor MANETs [8], [19]. The random path followed

by each mobile node is recorded in an ASCII textfile (with a .trj extension) by running a simulation.If more flexibility is required, the decentralized ap-proach is more adequate, allowing to define a distinctprofile for each node.

When executing one simulation, one random num-ber seed must be specified. Thus, to ensure the sta-tistical validity of the obtained results, it is necessaryto repeat the experiment with distinct random seeds.However, changing the seed affects the trajectory fol-lowed by the mobile nodes. In order to use the sametrajectory independently of the random number seedused, the generated trajectory file for each node us-ing the centralized approach should be assigned tothe corresponding mobile node using the ’trajectory’attribute. OPNET Modeler offers an option to dothis assignment.

Although, all the aforementioned options withinOPNET Modeler are useful, when it is necessary togenerate a great number of MANET scenarios, doingit manually can be a tedious task and prone to error.Our simul topogen tool does all those steps automat-ically for a specified list of seed values, generating adistinct scenario and trajectories for each one. An-other drawback of manually doing these tasks withinthe OPNET Modeler environment is that there is noway of knowing if the scenarios generated are validfor conducting a rigorous research, specially in thepresence of mobility. The position of nodes is a crit-ical factor because one or more of them could beisolated. This is specially true in the case of staticscenarios where the position of nodes provokes net-work partitioning. For example, if some nodes areisolated, they will not be able to communicate witheach other during all the simulation time nor receivebroadcast messages. In an dynamic scenario, thisproblem can also occur at any time due to the mo-bility of the nodes, but it is usually temporary.

Due to these issues, some researchers have pro-posed some recommendations when choosing the sce-narios for simulation. For example, as stated in [8],in order to rigorously test a MANET routing proto-col, the scenario should meet two standards: (1) theaverage shortest-path hop count needs to be large,and (2) only a small amount of network partitioningshould exist. Therefore, the proposed simul topogen

tool takes into account such recommendations andchecks the validity of any generated scenario.

The simul topogen tool computes the averageshortest-path hop count and several metrics definedin [8] and [10] in order to determine the network par-tition degree for each scenario generated. In short,the four metrics considered in simul topogen are de-fined as follows:

• Average shortest-path hop count (AHC): thesmallest number of hops along any path betweeneach pair of nodes, taking into account the trans-mission range. The minimum value is 1, indi-cating that every pair of nodes are direct neigh-bours. Although the average number of hops ismore commonly used when evaluating the per-

XX Jornadas de Paralelismo 464

formance of a protocol, this metric is more con-venient to check for the validity of the generatedscenarios because it is not protocol dependent.

• Average Network Partitioning (ANP): the pro-portion of node pairs with no communicationpath available. The optimum value for this met-ric is 0%, indicating that there is no partitioningdegree and so all the nodes are able to commu-nicate with each other.

• Average Number of Groups (ANG): the numberof partitions that exist in the network. If thisvalue is 1, all nodes are in the same group, thatis, there is no isolated node.

• Average Partition Size (APS): the average num-ber of nodes in each partition. The greater thesize of a partition, the higher connectivity andlower partitioning degree of the network. If thisvalue is exactly the number of nodes, all of themare in the same group. On the other hand, if thisvalue is 1, each node is isolated from the others.

All these metrics are computed not only in theinitial state, but also during the simulation time,being defined as the average value. Finally, thebest scenarios are chosen in order to meet thesetwo standards. The essential characteristics of thesimul topogen tool are shown by means of the fol-lowing example:

simul_topogen -p myproject -m mynetwork

-seeds 1 50 1

-nodes MOB node*

-area 0 0 1900 400 0 0

-mobility 5 0 0 0 1

-checkpartition 250

-duration 300

-select 10 ANP ASC n

-export rh ..\trajectories

In this case, the template scenario to be tested isread from the ’mynetwork’ model within the OPNETModeler project ’myproject’. The values followingthe -seed argument are the seeds to be used, thatis, the number of scenarios to be tested: from 1 to50 by increments of 1 (50 seeds). Then, the net-work nodes to be randomly placed are specified; theycan be either mobile nodes (MOB) or fixed ones (FIX),and those which name starts with ’node’. The spec-ified nodes will be placed in a rectangular flat areasized 1900x400 meters. A 3D volume can be set toospecifying the altitude range in the last two valuesof the -area parameter. Then, the RWM mobilitymodel parameters are specified with the -mobility

option in this order: speed, pause time, start time,stop time, and frequency. In order to check the net-work partitioning degree, the communication rangeis specified in the -checkpartition argument (250m.). The simulation time (-duration) is set to 300s. Then, with the -select option, the scenarios tobe choosen are specified; in this case, they will besorted by one of the evaluated metrics (ANP) in as-cending order, and only the top 10 scenarios out of50 generated will be selected, discarding those sce-

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Fig. 1. Snapshot during a simulation (top) and trajectoriesfollowed by nodes (bottom)

narios which present more than one partition at theinitial state.

Finally, the trajectories followed by each node canbe exported to a single ASCII file readable eitherby the R package or by MS-Excel. The destinationpath for this file can be specified too. In addition,the simul topogen utility is able to generate a set ofgraphical files showing the position of the nodes atany time and for each of the selected scenarios, thatis, all the snapshots along the simulation time. Fur-thermore, the trajectories followed by all the nodesor a group of them can be exported to graphical filestoo, by post-processing the trajectory file generatedpreviously. So, the user can choose any of the graphsgenerated for including them into a report or researchpaper outside of OPNET Modeler simulator (see Fig-ure 1). However, OPNET Modeler includes an ani-mation viewer that can be used to visualize the move-ment of the nodes during the simulation. All thesefacilities could be very useful in order to implementand verify a new mobility model, in a similar waythan the iNSpect [20] utility does.

Depending on the simulation study to be con-ducted, the researcher could need to choose the bestor worst scenarios. This can be done by simply spec-ifying any of the computed metrics and the order ofsorting (ASC or DESC) in the -select option.

B. Running simulations

Within the OPNET Modeler GUI, when theuser runs a simulation, the op runsim program islaunched in the background. This program can alsobe executed from the command-line too by the user,specifying the appropriate arguments. However, thisshould be done for every simulation within the wholesimulation set, that is, varying the random numberseed, the scenario, traffic load, etc.

465 A Coruña, 16-18 de septiembre de 2009

In order to automate this manual process, and toavoid human errors, the program simul simul hasbeen developed. The purpose of this tool is to runa set of simulations outside the OPNET Modelerenvironment in an unattended manner, either se-quentially or concurrently. The program receives anASCII text file as input, including the different pa-rameters for the simulations to run, and these arelaunched by invoking the op runsim program withthe appropriate arguments each time. The usage ofthis program is the following:

simul_simul -f simuls.txt

[-cluster 20] [-distributed]

The input file ’simuls.txt’, which is specifiedthrough the -f option, is a tab-delimited file, and soit can be edited within the MS-Excel application forconvenience. Each line corresponds to an indepen-dent simulation set, and each column to the argu-ments to the distinct simulations within the simula-tion set. Columns include mainly a list of randomnumber seeds, and several lists of comma-separatedvalues, which will be the varying arguments betweensimulations. Usually only one of these arguments isthe varying value, being the value shown at the ab-scissas axis in the graphs (e.g. traffic load).

Optionally, if a Condor cluster system [17] is avail-able, the simulation set could be run concurrentlyspecifying the option -cluster along with the maxi-mum number of simultaneous simulations, which hasto be less or equal than the number of the clusterprocessors available. However, since each simulationneeds the appropriate license, this value is limited bythe number of OPNET licenses available too. Theuser only needs to specify the -cluster option, andthe program automatically generates the necessarysubmit job files to launch all the simulations. Thatis, the user does not need any knowledge about theCondor system.

This should not be confused with the Simulation

Partitioning concept [21], in which a single simula-tion is distributed onto a number of different ma-chines in order to achieve a greater simulation scalethan on a single machine. In fact, the OPNET Mod-eler has a preference that can be enabled or disabledto specify whether each simulation should be run se-quentially or in parallel on several processors. Bydefault, this preference is disabled; to enable the par-allel preference from the simul simul tool, the option-distributed should be specified in the commandline.

IV. Result processing

A. Extracting data

All the statistics resulting from the OPNET Mod-eler simulations are stored in a single output vectorfile (file type suffix .ov). This is a binary file thatmust be converted to a text file in order to be im-ported and processed by external software like the Rpackage, gnuplot, or MS-Excel, for example, in orderto graphically present the data (see Section IV-B).

OPNET Modeler allows the user to export the col-lected statistics in three different ways:

1. Executing the ’Export Data to Spreadsheet’ op-eration after showing a specific statistic withinthe Results Browser. By using a graph templateseveral statistics can be exported at once; how-ever this process takes a long time when dealingwith a great number of simulations.

2. Using the op cvov.exe and op cvos.exe util-ities available within OPNET Modeler sincev14.0. Unfortunately, these are not available forprevious versions.

3. By means of a self-designed C program whichuses the External Model Access (EMA) pack-age. This is the most convenient option becausethe process has been customized in order to betotally unattended, and it is available for anyOPNET Modeler version.

Two different tools were developed for this pur-pose: (1) ov2txt, which uses the EMA package andexports several specified statistics from the outputvector file (.ov) to a single ASCII text file, and (2)simul data, which invokes to ov2txt for each simula-tion in a simulation set. Although all this functional-ity could have been implemented in a single program,doing it in this way allows to parallelize the processusing a cluster, as it will be explained later.

The use of the simul data program is very sim-ple, since it receives exactly the same input file thansimul simul (see Section III-B). For example:

simul_data -f simuls.txt -x @stats.txt

[-cluster 96]

This program interprets the simulations set de-fined in the input file ’simuls.txt’ and then iteratesbuilding the appropriate arguments for ov2txt invo-cation for each simulation. As it can be seen fromthe example, in addition to the input file it is nec-essary to specify the statistics to be extracted withthe -x option. Although it is possible to give a list ofnumbers, the most convenient way is to specify a filename which contains the names of the selected statis-tics, as appear within the OPNET Modeler environ-ment. For example, the file ’stats.txt’ contains thefollowing:

time (sec.)

Wireless LAN.Throughput (bits/sec)

Wireless LAN.Delay (sec)

AODV.Number of Hops per Route

Finally, this program can do its task in parallel ifthe -cluster option is specified. In this case, eachinvocation to ov2txt will be a process, and the max-imum number of simultaneous processes will be lim-ited by the number of processors available and by thevalue specified in the command-line. Again, it is notnecessary for the user to know anything about howto launch jobs in Condor, as this program generatesall the necessary job files and submits them.

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1 2 3 4 5 6 7 8

1e−03

1e−02

1e−01

1e+00

1e+01

Number of hops from source to destination

Throughput per AC (Mbit/s)

AC_VO (Voice)AC_VI (Video)AC_BE (BestEffort)AC_BK (Background)

1 2 3 4 5 6 7 8

Number of hops from source to destination

Bandwidth share (%)

1e−02

1e−01

1e+00

1e+01

1e+02

0

0.9

43.1

56

0.3

1.1

16.7

82

0.5

2.4

19.2

77.8

1.2

5

25.1

68.7

2

8.3

31.5

58.3

6.9

13.3

37.7

42

14.7

17.7

46.6

21

26.8

18.8

30.2

24.2

AC_VO (Voice)AC_VI (Video)AC_BE (BestEffort)AC_BK (Background)

Fig. 2. Examples of graphs generated by simul graphs

B. Generating graphs

The simul graphs tool focuses on generatinggraphs from the data extracted previously. All theexported data for any individual simulation within asimulation set are taken into account. The usage ofsimul graphs is very similar to the previous ones:

simul_graphs -f simuls.txt graphs.txt

[-ci 90] [-gnuplot] [-cluster 96]

Again, ’simuls.txt’ is the simulation file ex-plained before, and ’graphs.txt’ is the graphs file,which is another text file describing each graph to begenerated. If the -ci option is specified, confidenceintervals will be computed and shown in the plotsusing the confidence level specified (90, 95, 99, . . . ).

The format of the graphs file is very similar tothe simulations one because it is an ASCII text tab-delimited file. So, both can be edited as a spread-sheet for convenience. The most relevant data in-cluded in the graphs file are: title and subtitle, the xand y axis titles, the column number for the abscissavalues within the data files, the column numbers forany of the data series to be plotted and the corre-sponding serial names to be included in the legend.Additionally, the limits of the graph, the position ofthe legend, and the kind of graph to be generated.Currently, only two graph types are supported: linesand stacked bars (see Figure 2).

The graphs are generated by means of some pre-defined and parameterized R scripts [14], thus theuser does not need to know the R language to ob-tain the graphs. However, the user can modify the Rscripts in order to custom the output if needed. Eachgraph is generated in two graphic formats, namely,Encapsulated PostScript (EPS) [22] and PortableNetwork Graphics (PNG) [23]. The EPS graph filesare vectorial format and it is the recommended for-mat to be included when writing a research paper,whereas the PNG graphs are recommended to be in-cluded in MS-Power Point presentations.

On the other hand, if -gnuplot is specified, gnu-plot [15] script files will be generated and executed.In this case, the same graphs will be generated again,but with a distinct appearance. The formats gener-ated are: EPS, PNG, or Scalable Vector Graphics(SVG) [24]. We are planning to include more dis-tinct outputs (e.g., Matlab or xgraph). In this waythe user can choose the desired graphics package, andmodify the automatically generated scripts without

editing and recompiling the software.

As the previous programs, simul graphs can be ex-ecuted in parallel in a Condor system by specifyingthe -cluster option, drastically reducing the timespent in this process. In this case, each invocationto a R or gnuplot script file will become a process inthe Condor system.

C. Generating reports

Finally, the last program presented allows to putall the graphs and the summarized data used for eachgraph together in a single document. That way, theuser can analyze the results in both a visual and ina numerical way more easily. The program in chargeof doing this task is simul reports, whose usage is thefollowing:

simul_reports -f simuls.txt graphs.txt

[-outputprofile {pdf|dvi|dvi-ps|dvi-ps-pdf}]

where ’simuls.txt’ is the same simulation fileused in all the previous steps, and ’graphs.txt’

the same graphs file used for simul graphs.

The result of this program is a single document persimulation set, generated using the LATEX documentpreparation system [16]. Firstly, the simul reports

builds a source file (with a .tex extension) on the fly.Then, depending on the output profile specified withthe -outputprofile option, the source file is com-piled into one of the following formats: Portable Doc-ument Format (PDF, by default), Device Indepen-dent format (DVI), Postscript (PS) or PDF throughPostscript. There exist viewers for all of these fileformats for the most common platforms. The graph-ical format included in the final document (e.g. PNGor EPS) depends on the output profile chosen, butall of them must have been generated previously bysimul graphs. Finally, if the user knows the LATEXlanguage, the source file generated can be modifiedor included in his/her own documents.

V. Conclusions and future works

Although simulation is the most common tech-nique used for testing MANET routing protocols,it is necessary to validate the scenarios generated.Some recommendations and metrics have been pro-posed in the literature in order to characterize simu-lation scenarios. However, putting such suggestionsinto practice is very difficult without the support ofadequate software tools. The framework presented inthis paper is very effective and useful when simulat-ing MANET experiments using the OPNET Modelersimulator.

The first of the simulation tools is a topology gen-erator which considers some of the metrics suggested.The best scenarios (or the worst ones) of a set of ran-domly generated scenarios can be chosen, dependingon one of the metrics evaluated. More metrics couldbe easily added if needed. In addition, it also plotsthe selected scenarios, including both the initial stateand the trajectories followed by each mobile node.

467 A Coruña, 16-18 de septiembre de 2009

The remainder tools aim at simplifying the simu-lation process and the subsequent analysis of results.They provide utilities for automating tasks such asrunning simulations, extracting relevant data, gen-erating graphs, and building reports including boththe data and graphs previously generated. All thesetasks can be done outside the graphical interface ofOPNET Modeler, in batch mode on the most com-mon platforms.

About the usability of these tools, although all ofthem are used from command-line and there is nographical interface, they are very easy to use, as theirsyntax is very similar. Moreover, the tools supportthe use of clusters of workstations for parallel pro-cessing, improving dramatically their running times.Finally, the user does not need to know anythingabout the R package, gnuplot, LATEX, nor the Condorsystem. Thus, this framework has a weak learningcurve.

As future work, it could be interesting to be ableto export the topology and trajectory files generated(.trj files) by simul topogen to other simulators likens-2. This would allow to compare the simulationresults against other simulators, further validatingthe obtained results.

The set of tools can be downloaded from the Gat-com web site (http://atc.umh.es/gatcom/), althoughthey are still under active development.

Acknowledgments

This work was partially supported by the Minis-terio de Educacion y Ciencia, Spain, under GrantDPI2007-66796-C03-03. The authors also would liketo acknowledge OPNET Technologies, Inc. for pro-viding an educational OPNET Modeler license.

Referencias

[1] L. Felipe Perrone, Yougu Yuan, and David M. Nicol,“Modeling and simulation best practices for wireless adhoc networks,” in Proceedings of the 35th Winter Simu-lation Conference (WSC), 2003, pp. 685–693.

[2] Stuart Kurkowski, Tracy Camp, and Michael Colagrosso,“Manet simulation studies: the incredibles,” SIGMO-BILE Mob. Comput. Commun. Rev., vol. 9, no. 4, pp.50–61, 2005.

[3] David Cavin, Yoav Sasson, and Andre Schiper, “On theaccuracy of MANET simulators,” in 2nd ACM int. work-shop on Principles of Mobile Computing (POMC), 2002,pp. 38–43.

[4] K. Pawlikowski, H. D. J. Jeong, and J. S. R. Lee, “Oncredibility of simulation studies of telecommunicationnetworks,” IEEE Communications Magazine, vol. 40,no. 1, pp. 132–139, 2002.

[5] Todd Andel and Alec Yasinsac, “On the credibility ofmanet simulations,” Computer, vol. 39, no. 7, pp. 48–54,July 2006.

[6] G. Amoussou, B.L. Agba, Z. Dziong, M. Kadoch, andF. Gagnon, “Performances analysis of mobile ad hocrouting protocols under realistic mobility and powermodels,” in OPNETWORK 2006, Washington D.C,USA, 2006.

[7] M.B. Doar, “A better model for generating test net-works,” Global Telecommunications Conference, 1996.GLOBECOM ’96. ’Communications: The Key to GlobalProsperity, pp. 86–93, Nov 1996.

[8] Stuart Kurkowski, Tracy Camp, and William Navidi,“Two standards for rigorous manet routing protocol eval-uation,” in Mobile Adhoc and Sensor Systems (MASS),2006 IEEE International Conference on, Oct. 2006, pp.256–266.

[9] S. Kurkowski, W. Navidi, and T. Camp, “Discoveringvariables that affect manet protocol performance,” inGlobal Telecommunications Conference, 2007. GLOBE-COM ’07. IEEE, 26-30 Nov. 2007, pp. 1237–1242.

[10] J. Hahner, D. Dudkowski, P.J. Marron, and K. Rother-mel, “Quantifying network partitioning in mobile ad hocnetworks,” in Mobile Data Management, 2007 Interna-tional Conference on, 1-1 May 2007, pp. 174–181.

[11] H. Haddadi, M. Rio, G. Iannaccone, A. Moore, andR. Mortier, “Network topologies: inference, modeling,and generation,” Communications Surveys & Tutorials,IEEE, vol. 10, no. 2, pp. 48–69, Quarter 2008.

[12] “The Network Simulator – ns-2,”http://www.isi.edu/nsnam/ns/.

[13] Opnet Technologies Inc., “OPNET Modeler,”http://www.opnet.com/.

[14] R Development Core Team, R: A Language and En-vironment for Statistical Computing, R Foundation forStatistical Computing, Vienna, Austria, 2006, ISBN 3-900051-07-0.

[15] Jeff Racine, “gnuplot 4.0: a portable interactive plottingutility,” Journal of Applied Econometrics, vol. 21, no. 1,pp. 133–141, 2006.

[16] Leslie Lamport, LaTeX User’s Guide and Document Ref-erence Manual, Addison-Wesley Publishing Company,Reading, Massachusetts, 1986.

[17] Condor, “Condor project,”http://www.cs.wisc.edu/condor/.

[18] T. Camp, J. Boleng, and V. Davies, “A survey of mobilitymodels for ad hoc network research,” Wireless Commu-nications & Mobile Computing (WCMC): Special issueon Mobile Ad Hoc Networking: Research, Trends andApplications, vol. 2, no. 5, pp. 483–502, 2002.

[19] J. Yoon, M. Liu, and B. Noble, “Random waypoint con-sidered harmful,” in IEEE INFOCOM Conference, 2003.

[20] S. Kurkowski, T. Camp, N. Mushell, and M. Colagrosso,“A visualization and analysis tool for ns-2 wireless sim-ulations: inspect,” in Modeling, Analysis, and Sim-ulation of Computer and Telecommunication Systems,2005. 13th IEEE International Symposium on, 27-29Sept. 2005, pp. 503–506.

[21] Donghua Xu and Mostafa Ammar, “Benchmap:Benchmark-based, hardware and model-aware partition-ing for parallel and distributed network simulation,” inMASCOTS ’04: Proceedings of the The IEEE Com-puter Society’s 12th Annual International Symposium onModeling, Analysis, and Simulation of Computer andTelecommunications Systems, Washington, DC, USA,2004, pp. 455–463, IEEE Computer Society.

[22] Peter Vollenweider, Encapsulated PostScript: applica-tion guide for the Macintosh and PCs, Prentice HallInternational (UK) Ltd., Hertfordshire, UK, UK, 1990.

[23] T. Boutell, “Png (portable network graphics) specifica-tion version 1.0,” Tech. Rep., United States, 1997.

[24] Andrew Watt, SVG Unleashed, Pearson Education, 2002.[25] K.I. Calvert, M.B. Doar, and E.W. Zegura, “Modeling

internet topology,” Communications Magazine, IEEE,vol. 35, no. 6, pp. 160–163, Jun 1997.

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