+/*! \page publis Publications
+
+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.
+
+\subsection pub_simulation About simulation
+
+\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
+ 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 <b>A Network Model for Simulation of Grid Application</b>\n
+ by <em>Henri Casanova and Loris Marchal</em>\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 <b>MetaSimGrid : Towards realistic scheduling simulation of
+ distributed applications</b>\n
+ by <em>Arnaud Legrand and Julien Lerouge</em>\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 <b>SimGrid: A Toolkit for the Simulation of Application
+ Scheduling</b>\n
+ by <em>Henri Casanova</em>\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 <b> A study of meta-scheduling architectures for high throughput
+ computing: Pull vs. Push</b>\n
+ by <em> Vincent Garonne, Andrei Tsaregorodtsev, and Eddy Caron </em>\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 <b>Exploiting Replication and Data Reuse to Efficiently Schedule
+ Data-intensive Applications on Grids</b>\n
+ by <em> E. Santos-Neto, W. Cirne, F. Brasileiro, A. Lima.</em>\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 <b>Optimal algorithms for scheduling divisible workloads on
+ heterogeneous systems</b>\n
+ by <em>Olivier Beaumont and Arnaud Legrand and Yves Robert</em>\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 <b>On-line Parallel Tomography</b>\n
+ by <em>Shava Smallen</em>\n
+ Masters Thesis, UCSD, May 2001
+\li <b>Applying Scheduling and Tuning to On-line Parallel Tomography </b>\n
+ by <em>Shava Smallen, Henri Casanova, Francine Berman</em>\n
+ in Proceedings of Supercomputing 2001
+\li <b>Heuristics for Scheduling Parameter Sweep applications in
+ Grid environments</b>\n
+ by <em>Henri Casanova, Arnaud Legrand, Dmitrii Zagorodnov and
+ Francine Berman</em>\n
+ in Proceedings of the 9th Heterogeneous Computing workshop
+ (HCW'2000), pp349-363.
+*/
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