MONARC SIMULATION FRAMEWORK. This paper discusses the latest generation of the MONARC (MOdels of Networked Analysis at Regional Centers) simulation framework, as a design and modelling tool for large scale distributed systems applied to HEP experiments. A process-oriented approach for discrete event simulation is well-suited for describing concurrent running programs, as well as the stochastic arrival patterns that characterize how such systems are used. The simulation engine is based on Threaded Objects (or Active Objects), which offer great flexibility in simulating the complex behavior of distributed data processing programs. The engine provides an appropriate scheduling mechanism for the Active objects with support for interrupts. This approach offers a natural way of describing complex running programs that are data dependent and which concurrently compete for shared resources as well as large numbers of concurrent data transfers on shared resources. The framework provides a complete set of basic components (processing nodes, data servers, network components) together with dynamically loadable decision units (scheduling or data replication modules) for easily building complex Computing Model simulations. Examples of simulating complex data processing systems are presented, and the way the framework is used to compare different decision making algorithms or to optimize the overall Grid architecture and/or the policies that govern the Grid’s use

References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. Al-Khateeb, Asef; Rashid, Nur’Aini Abdul; Abdullah, Rosni: An enhanced meta-scheduling system for grid computing that considers the job type and priority (2012) ioport
  2. Olteanu, Alexandra; Pop, Florin; Dobre, Ciprian; Cristea, Valentin: A dynamic rescheduling algorithm for resource management in large scale dependable distributed systems (2012)
  3. Dobre, Ciprian; Stratan, Corina: MONARC simulation framework (2011) ioport
  4. Rahman, Rashedur M.; Alhajj, Reda; Barker, Ken: Replica selection strategies in data grid (2008)
  5. Venugopal, Srikumar; Buyya, Rajkumar: An SCP-based heuristic approach for scheduling distributed data-intensive applications on global grids (2008)
  6. Almehed, S.; Driouichi, Ch.; Eerola, P.; Mjörnmark, U.; Smirnova, O.; Zacharatou Jarlskog, Ch.; Åkesson, T.: Regional research exploitation of the LHC: a case-study of the required computing resources (2002)
  7. Legrand, Iosif: Multi-threaded, discrete event simulation of distributed computing systems (2001)
  8. Morita, Youhei; Aderholz, M.; Amako, K.; Auge, E.; Bagliesi, G.; Barone, L.; Battistoni, G.; Boschini, M.; Brunengo, A.; Bunn, J. J.; Butler, J.; Campanella, M.; Capiluppi, P.; D’Amato, M.; Dameri, M.; di Mattia, A.; Dorokhov, A.; Gasparini, U.; Gagliardi, F.; Gaines, I.; Galvez, P.; Ghiselli, A.; Gordon, J.; Grandi, C.; Harris, F.; Holtman, K.; Karimaki, V.; Karita, Y.; Klem, J.; Legrand, I.; Leltchouk, M.; Linglin, D.; Lubrano, P.; Luminari, L.; Michelotto, M.; McArthur, I.; Nazarenko, A.; Newman, H.; O’Dell, V.; O’Neale, S. W.; Osculati, B.; Pepe, M.; Perini, L.; Pinfold, J.; Pordes, R.; Prelz, F.; Putzer, A.; Resconi, S.; Robertson, L.; Rolli, S.; Sasaki, T.; Sato, H.; Servoli, L.; Schaffer, R. D.; Schalk, T.; Sgaravatto, M.; Shiers, J.; Silvestris, L.; Siroli, G. P.; Sliwa, K.; Smith, T.; Somigliana, R.; Stanescu, C.; Stockinger, H.; Ugolotti, D.; Valente, E.; Vistoli, C.; Willers, I.; Wilkinson, R.; Williams, D. O.: Validation of the MONARC simulation tools (2001)