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-/*! \page publis Publications
-
-When citing SimGrid, the prefered reference paper is Scheduling
-Distributed Applications: the SimGrid Simulation Framework, even if it's
-a bit old now. We are actively working on improving this.
-
-\subsection pub_simulation About simulation
-
-\li Scheduling Distributed Applications: the
- SimGrid Simulation Framework\n
- by Henri Casanova and Arnaud Legrand and Loris Marchal\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
+/*! \page publis Reference publications about SimGrid
+
+
+When citing SimGrid, the prefered reference paper is SimGrid: a
+Generic Framework for Large-Scale Distributed Experimentations.
+
+\li SimGrid: a Generic Framework for Large-Scale Distributed
+ Experimentations\n
+ by Henri Casanova, Arnaud Legrand and Martin Quinson\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.
-
-
-\li A Network Model for Simulation of Grid Application\n
- by Henri Casanova and Loris Marchal\n
- \anchor paper_tcp
- In this work we investigate network models that can be
- potentially employed in the simulation of scheduling algorithms for
- distributed computing applications. We seek to develop a model of TCP
- communication which is both high-level and realistic. Previous research
- works show that accurate and global modeling of wide-area networks, such
- as the Internet, faces a number of challenging issues. However, some
- global models of fairness and bandwidth-sharing exist, and can be link
- withthe behavior of TCP. Using both previous results and simulation (with
- NS), we attempt to understand the macroscopic behavior of
- TCP communications. We then propose a global model of the network for the
- Grid platform. We perform partial validation of this model in
- simulation. The model leads to an algorithm for computing
- bandwidth-sharing. This algorithm can then be implemented as part of Grid
- application simulations. We provide such an implementation for the
- SimGrid simulation toolkit.\n
- ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-40.ps.gz
-
-
-\li MetaSimGrid : Towards realistic scheduling simulation of
- distributed applications\n
- by Arnaud Legrand and Julien Lerouge\n
- Most scheduling problems are already hard on homogeneous
- platforms, they become quite intractable in an heterogeneous
- framework such as a metacomputing grid. In the best cases, a
- guaranteed heuristic can be found, but most of the time, it is
- not possible. Real experiments or simulations are often
- involved to test or to compare heuristics. However, on a
- distributed heterogeneous platform, such experiments are
- technically difficult to drive, because of the genuine
- instability of the platform. It is almost impossible to
- guarantee that a platform which is not dedicated to the
- experiment, will remain exactly the same between two tests,
- thereby forbidding any meaningful comparison. Simulations are
- then used to replace real experiments, so as to ensure the
- reproducibility of measured data. A key issue is the
- possibility to run the simulations against a realistic
- environment. The main idea of trace-based simulation is to
- record the platform parameters today, and to simulate the
- algorithms tomorrow, against the recorded data: even though it
- is not the current load of the platform, it is realistic,
- because it represents a fair summary of what happened
- previously. A good example of a trace-based simulation tool is
- SimGrid, a toolkit providing a set of core abstractions and
- functionalities that can be used to easily build simulators for
- specific application domains and/or computing environment
- topologies. Nevertheless, SimGrid lacks a number of convenient
- features to craft simulations of a distributed application
- where scheduling decisions are not taken by a single
- process. Furthermore, modeling a complex platform by hand is
- fastidious for a few hosts and is almost impossible for a real
- grid. This report is a survey on simulation for scheduling
- evaluation purposes and present MetaSimGrid, a simulator built
- on top of SimGrid.\n
- ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-28.ps.gz
-
-\li SimGrid: A Toolkit for the Simulation of Application
- Scheduling\n
- by Henri Casanova\n
- Advances in hardware and software technologies have made it
- possible to deploy parallel applications over increasingly large
- sets of distributed resources. Consequently, the study of
- scheduling algorithms for such applications has been an active area
- of research. Given the nature of most scheduling problems one must
- resort to simulation to effectively evaluate and compare their
- efficacy over a wide range of scenarios. It has thus become
- necessary to simulate those algorithms for increasingly complex
- distributed, dynamic, heterogeneous environments. In this paper we
- present SimGrid, a simulation toolkit for the study of scheduling
- algorithms for distributed application. This paper gives the main
- concepts and models behind SimGrid, describes its API and
- highlights current implementation issues. We also give some
- experimental results and describe work that builds on SimGrid's
- functionalities.\n
- http://grail.sdsc.edu/papers/simgrid_ccgrid01.ps.gz
-
-\subsection pub_research Papers using SimGrid results
-
-\li A study of meta-scheduling architectures for high throughput
- computing: Pull vs. Push\n
- by Vincent Garonne, Andrei Tsaregorodtsev, and Eddy Caron \n
- Proceedings of 4th Internationnal Symposium on Parallel and
- Distributed Computing Job Scheduling Strategies for Parallel
- Processing (ISPDC'05), July 2005.\n
- Preliminary version in http://marwww.in2p3.fr/~garonne/garonne-meta.pdf
-
-\li Exploiting Replication and Data Reuse to Efficiently Schedule
- Data-intensive Applications on Grids\n
- by E. Santos-Neto, W. Cirne, F. Brasileiro, A. Lima.\n
- Proceedings of 10th Job Scheduling Strategies for Parallel Processing, June 2004.\n
- http://www.lsd.ufcg.edu.br/~elizeu/articles/jsspp.v6.pdf
-
-\li Optimal algorithms for scheduling divisible workloads on
- heterogeneous systems\n
- by Olivier Beaumont and Arnaud Legrand and Yves Robert\n
- in Proceedings of the 17th International Parallel and Distributed Processing Symposium (IPDPS'03).\n
- Preliminary version on ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-36.ps.gz
-
-\li On-line Parallel Tomography\n
- by Shava Smallen\n
- Masters Thesis, UCSD, May 2001
-\li Applying Scheduling and Tuning to On-line Parallel Tomography \n
- by Shava Smallen, Henri Casanova, Francine Berman\n
- in Proceedings of Supercomputing 2001
-\li Heuristics for Scheduling Parameter Sweep applications in
- Grid environments\n
- by Henri Casanova, Arnaud Legrand, Dmitrii Zagorodnov and
- Francine Berman\n
- in Proceedings of the 9th Heterogeneous Computing workshop
- (HCW'2000), pp349-363.
-*/
\ No newline at end of file
+ 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
+ http://www.loria.fr/~quinson/articles/SimGrid-uksim08.pdf
+
+\verbatim
+@InProceedings{simgrid,
+ author = {Casanova, Henri and Legrand, Arnaud and Quinson, Martin},
+ title = {{SimGrid: a Generic Framework for Large-Scale Distributed Experiments}},
+ booktitle = {10th IEEE International Conference on Computer Modeling and Simulation},
+ year = 2008,
+ month = mar
+}
+\endverbatim
+
+\section publis_others Other publications
+
+A lot of other papers where published about SimGrid. The list is
+splited in 3 pages (also accessible from the navbar on top of this page):
+ - \ref publis_core\n
+ This section contains papers describing some sub-parts of SimGrid,
+ or references superseeded by the one given above.
+ - \ref publis_extern\n
+ SimGrid is used by an ever growing scientific community. This
+ section lists all the papers resulting of works in which the core
+ SimGrid team were not involved.
+ - \ref publis_intra\n
+ This section lists the paper co-signed by at least one of the core
+ team member, and using SimGrid as a tool (and not studying SimGrid
+ itself).
+
+\section publis_count Amount of published papers using SimGrid results
+
+\htmlinclude publis_count.html
+
+\page publis_core Publications about the SimGrid framework
+
+\htmlinclude publis_core_bib.html
+
+\page publis_extern Papers that use SimGrid-generated results (not counting our owns)
+
+This list is a selection of articles. We list only papers written by people
+external to the development group, but we also use our tool ourselves (see
+next section).
+
+\htmlinclude publis_extern_bib.html
+
+\page publis_intra Our own papers that use SimGrid-generated results
+
+This list is a selection of the articles we have written that used results
+generated by SimGrid.
+
+\htmlinclude publis_intra_bib.html
+
+
+*/