/*! \page publis Reference publications about SimGrid
-When citing SimGrid, the prefered reference paper is <i>Scheduling
-Distributed Applications: the SimGrid Simulation Framework</i>, even if it's
-a bit old now. We are actively working on improving this.
+When citing SimGrid, the prefered reference paper is <i>SimGrid: a
+Generic Framework for Large-Scale Distributed Experimentations</i>.
-\li <b>Scheduling Distributed Applications: the
- SimGrid Simulation Framework</b>\n
- by <em>Henri Casanova and Arnaud Legrand and Loris Marchal</em>\n
- Proceedings of the third IEEE International Symposium
- on Cluster Computing and the Grid (CCGrid'03)\n
- Since the advent of distributed computer systems an active field
- of research has been the investigation of scheduling strategies
- for parallel applications. The common approach is to employ
- scheduling heuristics that approximate an optimal
- schedule. Unfortunately, it is often impossible to obtain
- analytical results to compare the efficacy of these heuristics.
- One possibility is to conducts large numbers of back-to-back
- experiments on real platforms. While this is possible on
- tightly-coupled platforms, it is infeasible on modern distributed
- platforms (i.e. Grids) as it is labor-intensive and does not
- enable repeatable results. The solution is to resort to
- simulations. Simulations not only enables repeatable results but
- also make it possible to explore wide ranges of platform and
+\li <b>SimGrid: a Generic Framework for Large-Scale Distributed
+ Experimentations</b>\n
+ by <em>Henri Casanova, Arnaud Legrand and Martin Quinson</em>\n
+ Proceedings of the 10th IEEE International Conference on Computer
+ Modelling and Simulation (UKSIM/EUROSIM'08)\n
+ Distributed computing is a very broad and active research area
+ comprising fields such as cluster computing, computational
+ grids, desktop grids and peer-to-peer (P2P) systems.
+ Unfortunately, it is often impossible to obtain theoretical or
+ analytical results to compare the performance of algorithms
+ targeting such systems. One possibility is to conduct large
+ numbers of back-to-back experiments on real platforms. While
+ this is possible on tightly-coupled platforms, it is infeasible
+ on modern distributed platforms as experiments are labor-intensive
+ and results typically not reproducible. Consequently, one must
+ resort to simulations, which enable reproducible results and also
+ make it possible to explore wide ranges of platform and
application scenarios.\n
- In this paper we present the SimGrid framework which enables the
- simulation of distributed applications in distributed computing
- environments for the specific purpose of developing and evaluating
- scheduling algorithms. This paper focuses on SimGrid v2, which
- greatly improves on the first version of the software with more
- realistic network models and topologies. SimGrid v2 also enables
- the simulation of distributed scheduling agents, which has become
- critical for current scheduling research in large-scale platforms.
- After describing and validating these features, we present a case
- study by which we demonstrate the usefulness of SimGrid for
- conducting scheduling research.\n
- http://www-id.imag.fr/Laboratoire/Membres/Legrand_Arnaud/articles/simgrid2_CCgrid03.pdf
+ In this paper we describe the SimGrid framework, a
+ simulation-based framework for evaluating cluster, grid and P2P
+ algorithms and heuristics. This paper focuses on SimGrid v3, which
+ greatly improves on previous versions thanks to a novel and
+ validated modular simulation engine that achieves higher
+ simulation speed without hindering simulation accuracy. Also, two
+ new user interfaces were added to broaden the targeted research
+ community. After surveying existing tools and methodologies we
+ describe the key features and benefits of SimGrid.\n
+ www.loria.fr/~quinson/articles/SimGrid-uksim08.pdf
Previous publication do not cover the GRAS part of the framework. So, if you
want to cite GRAS, please use this publication instead:
-\li <b>Gras: A Research & Development Framework for Grid and P2P
- Infrastructures</b>\n
- by <em>Martin Quinson</em>\n
- <b>Best paper</b> of the 18th IASTED International Conference on
- Parallel and Distributed Computing and Systems (PDCS 2006)\n
- http://www.loria.fr/~quinson/articles/gras-iasted06.pdf
\section publis_others Other publications
<b>M.S. Thesis</b>, Dept. of Information and Computer Sciences, University of Hawai`i at Manoa, April 2007.\n
http://navet.ics.hawaii.edu/~casanova/homepage/theses/kayo_fujiwara_MS.pdf
+\li <b>Gras: A Research & Development Framework for Grid and P2P
+ Infrastructures</b>\n
+ by <em>Martin Quinson</em>\n
+ <b>Best paper</b> of the 18th IASTED International Conference on
+ Parallel and Distributed Computing and Systems (PDCS 2006)\n
+ http://www.loria.fr/~quinson/articles/gras-iasted06.pdf
+
\li <b>The SimGrid Project - Simulation and Deployment of Distributed Applications</b>\n
by <em>A. Legrand, M. Quinson, K. Fujiwara, H. Casanova</em>\n
<b>POSTER</b> in Proceedings of the IEEE International Symposium on High Performance Distributed Computing (HPDC-15), Paris, France, May 2006.\n
\endhtmlonly
http://navet.ics.hawaii.edu/~casanova/homepage/papers/simgrid_hpdc06.pdf
+\li <b>Scheduling Distributed Applications: the SimGrid Simulation Framework</b>\n
+ by <em>Henri Casanova and Arnaud Legrand and Loris Marchal</em>\n
+ Proceedings of the third IEEE International Symposium
+ on Cluster Computing and the Grid (CCGrid'03)\n
+ http://www-id.imag.fr/Laboratoire/Membres/Legrand_Arnaud/articles/simgrid2_CCgrid03.pdf
+
\li <b>A Network Model for Simulation of Grid Application</b>\n
by <em>Henri Casanova and Loris Marchal</em>\n
\anchor paper_tcp