1 /*! \page publis Publications
3 \section pub_reference Reference publication about SimGrid
5 When citing SimGrid, the prefered reference paper is <i>Scheduling
6 Distributed Applications: the SimGrid Simulation Framework</i>, even if it's
7 a bit old now. We are actively working on improving this.
9 \li <b>Scheduling Distributed Applications: the
10 SimGrid Simulation Framework</b>\n
11 by <em>Henri Casanova and Arnaud Legrand and Loris Marchal</em>\n
12 Proceedings of the third IEEE International Symposium
13 on Cluster Computing and the Grid (CCGrid'03)\n
14 Since the advent of distributed computer systems an active field
15 of research has been the investigation of scheduling strategies
16 for parallel applications. The common approach is to employ
17 scheduling heuristics that approximate an optimal
18 schedule. Unfortunately, it is often impossible to obtain
19 analytical results to compare the efficacy of these heuristics.
20 One possibility is to conducts large numbers of back-to-back
21 experiments on real platforms. While this is possible on
22 tightly-coupled platforms, it is infeasible on modern distributed
23 platforms (i.e. Grids) as it is labor-intensive and does not
24 enable repeatable results. The solution is to resort to
25 simulations. Simulations not only enables repeatable results but
26 also make it possible to explore wide ranges of platform and
27 application scenarios.\n
28 In this paper we present the SimGrid framework which enables the
29 simulation of distributed applications in distributed computing
30 environments for the specific purpose of developing and evaluating
31 scheduling algorithms. This paper focuses on SimGrid v2, which
32 greatly improves on the first version of the software with more
33 realistic network models and topologies. SimGrid v2 also enables
34 the simulation of distributed scheduling agents, which has become
35 critical for current scheduling research in large-scale platforms.
36 After describing and validating these features, we present a case
37 study by which we demonstrate the usefulness of SimGrid for
38 conducting scheduling research.\n
39 http://www-id.imag.fr/Laboratoire/Membres/Legrand_Arnaud/articles/simgrid2_CCgrid03.pdf
41 Previous publication do not cover the GRAS part of the framework. So, if you
42 want to cite GRAS, please use this publication instead:
44 \li <b>Gras: A Research & Development Framework for Grid and P2P
46 by <em>Martin Quinson</em>\n
47 <b>Best paper</b> of the 18th IASTED International Conference on
48 Parallel and Distributed Computing and Systems (PDCS 2006)\n
49 http://www.loria.fr/~quinson/articles/gras-iasted06.pdf
51 \section pub_simulation Other publications about the SimGrid framework
53 \li <b>Cost and Accuracy of Packet-Level vs. Analytical Network Simulations: An Empirical Study</b>\n
54 by <em>K. Fujiwara</em>\n
55 <b>M.S. Thesis</b>, Dept. of Information and Computer Sciences, University of Hawai`i at Manoa, April 2007.\n
56 http://navet.ics.hawaii.edu/~casanova/homepage/theses/kayo_fujiwara_MS.pdf
58 \li <b>The SimGrid Project - Simulation and Deployment of Distributed Applications</b>\n
59 by <em>A. Legrand, M. Quinson, K. Fujiwara, H. Casanova</em>\n
60 <b>POSTER</b> in Proceedings of the IEEE International Symposium on High Performance Distributed Computing (HPDC-15), Paris, France, May 2006.\n
62 <a href="http://navet.ics.hawaii.edu/~casanova/homepage/papers/simgrid_hpdc06.pdf"><img src="poster_thumbnail.png" /></a>
64 http://navet.ics.hawaii.edu/~casanova/homepage/papers/simgrid_hpdc06.pdf
66 \li <b>A Network Model for Simulation of Grid Application</b>\n
67 by <em>Henri Casanova and Loris Marchal</em>\n
69 In this work we investigate network models that can be
70 potentially employed in the simulation of scheduling algorithms for
71 distributed computing applications. We seek to develop a model of TCP
72 communication which is both high-level and realistic. Previous research
73 works show that accurate and global modeling of wide-area networks, such
74 as the Internet, faces a number of challenging issues. However, some
75 global models of fairness and bandwidth-sharing exist, and can be link
76 withthe behavior of TCP. Using both previous results and simulation (with
77 NS), we attempt to understand the macroscopic behavior of
78 TCP communications. We then propose a global model of the network for the
79 Grid platform. We perform partial validation of this model in
80 simulation. The model leads to an algorithm for computing
81 bandwidth-sharing. This algorithm can then be implemented as part of Grid
82 application simulations. We provide such an implementation for the
83 SimGrid simulation toolkit.\n
84 ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-40.ps.gz
87 \li <b>MetaSimGrid : Towards realistic scheduling simulation of
88 distributed applications</b>\n
89 by <em>Arnaud Legrand and Julien Lerouge</em>\n
90 Most scheduling problems are already hard on homogeneous
91 platforms, they become quite intractable in an heterogeneous
92 framework such as a metacomputing grid. In the best cases, a
93 guaranteed heuristic can be found, but most of the time, it is
94 not possible. Real experiments or simulations are often
95 involved to test or to compare heuristics. However, on a
96 distributed heterogeneous platform, such experiments are
97 technically difficult to drive, because of the genuine
98 instability of the platform. It is almost impossible to
99 guarantee that a platform which is not dedicated to the
100 experiment, will remain exactly the same between two tests,
101 thereby forbidding any meaningful comparison. Simulations are
102 then used to replace real experiments, so as to ensure the
103 reproducibility of measured data. A key issue is the
104 possibility to run the simulations against a realistic
105 environment. The main idea of trace-based simulation is to
106 record the platform parameters today, and to simulate the
107 algorithms tomorrow, against the recorded data: even though it
108 is not the current load of the platform, it is realistic,
109 because it represents a fair summary of what happened
110 previously. A good example of a trace-based simulation tool is
111 SimGrid, a toolkit providing a set of core abstractions and
112 functionalities that can be used to easily build simulators for
113 specific application domains and/or computing environment
114 topologies. Nevertheless, SimGrid lacks a number of convenient
115 features to craft simulations of a distributed application
116 where scheduling decisions are not taken by a single
117 process. Furthermore, modeling a complex platform by hand is
118 fastidious for a few hosts and is almost impossible for a real
119 grid. This report is a survey on simulation for scheduling
120 evaluation purposes and present MetaSimGrid, a simulator built
122 ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-28.ps.gz
124 \li <b>SimGrid: A Toolkit for the Simulation of Application
126 by <em>Henri Casanova</em>\n
127 Advances in hardware and software technologies have made it
128 possible to deploy parallel applications over increasingly large
129 sets of distributed resources. Consequently, the study of
130 scheduling algorithms for such applications has been an active area
131 of research. Given the nature of most scheduling problems one must
132 resort to simulation to effectively evaluate and compare their
133 efficacy over a wide range of scenarios. It has thus become
134 necessary to simulate those algorithms for increasingly complex
135 distributed, dynamic, heterogeneous environments. In this paper we
136 present SimGrid, a simulation toolkit for the study of scheduling
137 algorithms for distributed application. This paper gives the main
138 concepts and models behind SimGrid, describes its API and
139 highlights current implementation issues. We also give some
140 experimental results and describe work that builds on SimGrid's
142 http://grail.sdsc.edu/papers/simgrid_ccgrid01.ps.gz
144 \section pub_ext Papers that use SimGrid-generated results (not counting our owns)
146 This list is a selection of articles. We list only papers written by people
147 external to the development group, but we also use our tool ourselves (see
151 - <b>Scheduling Δ-Critical Tasks in Mixed-Parallel Applications on a National Grid</b>\n
152 by <em>Frédéric Suter</em>.\n
153 In 8th IEEE/ACM International Conference on Grid Computing (Grid 2007), Austin, TX, September 2007.
156 - <b>Simbatch: an API for simulating and predicting the performance of parallel resources and batch systems.</b>\n
157 INRIA Research Report 6040, November 2006.\n
158 https://hal.inria.fr/inria-00115880
159 - <b>Simbatch : une API pour la simulation et la prédiction de performances de systèmes batch</b>\n
160 by <em>Jean-Sébastien Gay and Yves Caniou</em>.\n
161 In 17ème Rencontres Francophones du Parallélisme, des Architectures et des Systèmes, RenPar'17.\n
162 October 4-6, Perpignan, France
163 - <b>Metascheduling Multiple Resource Types using the MMKP</b>\n
164 by <em>D. Vanderster, N. Dimopoulos, R. Sobie</em>\n
165 7th IEEE/ACM International Conference on Grid Computing\n
166 Barcelona, September 28th-29th 2006
167 - <b>Master-Slave Tasking on Asymmetric Networks</b>\n
168 by <em>Cyril Banino-Rokkones, Olivier Beaumont and Lasse Natvig</em>.\n
169 In Proceedings of 12th International Euro-Par Conference, Euro-Par 2006.\n
170 August 29 - September 1, Pages 167--176, Dresden, Germany.
171 - <b>Critical Path and Area Based Scheduling of Parallel Task Graphs on Heterogeneous Platforms</b>\n
172 by <em>Tchimou N'Takpé and Frédéric Suter</em>\n
173 Proceedings of the Twelfth International Conference on Parallel and Distributed Systems (ICPADS)\n
174 Minneapolis, MN, July 12-15, 2006.
175 - <b>Sensitivity Analysis of Knapsack-based Task Scheduling on the Grid</b>\n
176 by <em>D.C. Vanderster and N.J. Dimopoulos</em>.\n
177 In Proceedings of The 20th ACM International Conference on Supercomputing\n
178 Cairns, Australia, June 28-July 1, 2006.\n
179 http://portal.acm.org/citation.cfm?id=1183401.1183446&coll=GUIDE&dl=%23url.coll
180 - <b>Hierarchical Scheduling of Independent Tasks with Shared Files</b>\n
181 by <em>H. Senger, F. Silva, W. Nascimento</em>.\n
182 Proceedings of the Sixth IEEE International Symposium on Cluster
183 Computing and the Grid Workshop (CCGRIDW'06)\n
184 Singapore, 16-19 May 2006.\n
185 http://www.unisantos.br/mestrado/informatica/hermes/File/senger-HierarchicalScheduling-Workshop-TB120.pdf
186 - <b>Evaluation of Knapsack-based Scheduling using the NPACI JOBLOG</b>\n
187 by <em>D. Vanderster, N. Dimopoulos, R. Parra-Hernandez and R. Sobie</em>.\n
188 20th International Symposium on High-Performance Computing in an
189 Advanced Collaborative Environment (HPCS'06)\n
190 St. John's, Newfoundland, Canada, 14-17 May 2006\n
191 http://doi.ieeecomputersociety.org/10.1109/HPCS.2006.23
194 - <b>On Dynamic Resource Management Mechanism using Control
195 Theoretic Approach for Wide-Area Grid Computing</b>\n
196 by <em>Hiroyuki Ohsaki, Soushi Watanabe, and Makoto Imase</em>\n
197 in Proceedings of IEEE Conference on Control Applications (CCA 2005), Aug. 2005.\n
198 http://www.ispl.jp/~oosaki/papers/Ohsaki05_CCA.pdf
199 - <b>Evaluation of Meta-scheduler Architectures and Task Assignment Policies for
200 high Throughput Computing</b>\n
201 by <em>Eddy Caron, Vincent Garonne and Andrei Tsaregorodtsev</em>\n
202 Proceedings of 4th Internationnal Symposium on Parallel and
203 Distributed Computing Job Scheduling Strategies for Parallel
204 Processing (ISPDC'05), July 2005.\n
205 http://www.ens-lyon.fr/LIP/Pub/Rapports/RR/RR2005/RR2005-27.pdf
207 - <b>Deadline Scheduling with Priority for Client-Server Systems on the Grid</b>\n
208 by <em>E Caron, PK Chouhan, F Desprez</em>\n
209 in IEEE International Conference On Grid Computing. Super Computing 2004, oct 2004.
210 - <b>Efficient Scheduling Heuristics for GridRPC Systems</b>\n
211 by <em>Y. Caniou and E. Jeannot.</em>\n
212 in IEEE QoS and Dynamic System workshop (QDS) of International Conference
213 on Parallel and Distributed Systems (ICPADS), New-Port Beach California, USA,
214 pages 621-630, July 2004\n
215 http://graal.ens-lyon.fr/~ycaniou/QDS04.ps
216 - <b>Exploiting Replication and Data Reuse to Efficiently Schedule
217 Data-intensive Applications on Grids</b>\n
218 by <em> E. Santos-Neto, W. Cirne, F. Brasileiro, A. Lima.</em>\n
219 Proceedings of 10th Job Scheduling Strategies for Parallel Processing, June 2004.\n
220 http://www.lsd.ufcg.edu.br/~elizeu/articles/jsspp.v6.pdf
221 - <b>Resource Management and Knapsack Formulations on the Grid</b>\n
222 by <em>R. Parra-Hernandez, D. Vanderster and N. J. Dimopoulos</em>\n
223 Fifth IEEE/ACM International Workshop on Grid Computing (GRID'04)\n
224 http://doi.ieeecomputersociety.org/10.1109/GRID.2004.54
225 - <b>Scheduling BoT Applications in Grids using a Slave Oriented Adaptive
227 by <em>T. Ferreto, C. A. F. De Rose and C. Northfleet.</em>\n
228 Second International Symposium on Parallel and Distributed Processing
229 and Applications (ISPA), 2004, Hong Kong. Published in Lecture Notes in
230 Computer Science (LNCS), Volume 3358, by Springer-Verlag. p. 392-398.
232 - <b>Link-Contention-Aware Genetic Scheduling Using Task Duplication in Grid Environments</b>\n
233 by <em>Wensheng Yao, Xiao Xie and Jinyuan You</em>\n
234 in Grid and Cooperative Computing: Second International Workshop, GCC 2003, Shanghai, China, December 7-10, 2003 (LNCS)\n
235 http://www.chinagrid.edu.cn/chinagrid/download/GCC2003/pdf/266.pdf
236 - <b>New Dynamic Heuristics in the Client-Agent-Server Model</b>\n
237 by <em>Y. Caniou and E. Jeannot</em>\n
238 in IEEE 13th Heteregeneous Computing Workshop - HCW'03, Nice, France, April 2003.\n
239 http://graal.ens-lyon.fr/~ycaniou/HCW03.ps
240 - <b>A Hierarchical Resource Reservation Algorithm for Network Enabled Servers</b>\n
241 by <em>E. Caron, F. Desprez, F. Petit, V. Villain</em>\n
242 in the 17th International Parallel and Distributed Processing Symposium -- IPDPS'03, Nice - France, April 2003.
244 \section pub_self Our own papers that use SimGrid-generated results
246 This list is a selection of the articles we have written that used results
247 generated by SimGrid.
250 - <b>A Comparison of Scheduling Approaches for Mixed-Parallel Applications on Heterogeneous Platforms</b>\n
251 by <em>T. N'takpé, F. Suter, and Henri Casanova</em>\n
252 In 6th International Symposium on Parallel and Distributed Computing, Hagenberg, Austria, July 2007.
254 - <b>On the Harmfulness of Redundant Batch Requests</b>\n
255 by <em>H. Casanova</em>\n
256 Proceedings of the IEEE International Symposium on High Performance Distributed Computing (HPDC-15), Paris, France, May 2006.\n
257 http://navet.ics.hawaii.edu/~casanova/homepage/papers/hpdc_2006.pdf
258 - <b>An evaluation of Job Scheduling Strategies for Divisible Loads on Grid Platforms</b>\n
259 by <em>Y. Cardinale, H. Casanova</em>\n
260 in Proceedings of the High Performance Computing & Simulation Conference (HPC&S'06), Bonn, Germany, May 2006.\n
261 http://navet.ics.hawaii.edu/~casanova/homepage/papers/cardinale_2006.pdf
262 - <b>Interference-Aware Scheduling</b>\n
263 by <em>B. Kreaseck, L. Carter, H. Casanova, J. Ferrante, S. Nandy</em>\n
264 International Journal of High Performance Computing Applications (IJHPCA), to appear.\n
265 http://navet.ics.hawaii.edu/~casanova/homepage/papers/kreaseck_ijhpca_2005.pdf
267 - <b>From Heterogeneous Task Scheduling to Heterogeneous Mixed Data and Task Parallel Scheduling</b>\n
268 by <em>F. Suter, V. Boudet, F. Desprez, H. Casanova</em>\n
269 Proceedings of Europar, 230--237, (LCNS volume 3149), Pisa, Italy, August 2004.
270 - <b>On the Interference of Communication on Computation</b>\n
271 by <em>B. Kreaseck, L. Carter, H. Casanova, J. Ferrante</em>\n
272 Proceedings of the workshop on Performance Modeling, Evaluation, and Optimization of Parallel and Distributed Systems, Santa Fe, April 2004.\n
273 http://navet.ics.hawaii.edu/~casanova/homepage/papers/k_pmeo2004.pdf
275 - <b>RUMR: Robust Scheduling for Divisible Workloads</b>\n
276 by <em>Y. Yang, H. Casanova</em>\n
277 Proceedings of the 12th IEEE Symposium on High Performance and Distributed Computing (HPDC-12), Seattle, June 2003.\n
278 http://navet.ics.hawaii.edu/~casanova/homepage/papers/yang_hpdc2003.pdf
279 - <b>Resource Allocation Strategies for Guided Parameter Space Searches</b>\n
280 by <em>M. Faerman, A. Birnbaum, F. Berman, H. Casanova</em>\n
281 International Journal of High Performance Computing Applications (IJHPCA), 17(4), 383--402, 2003.\n
282 http://grail.sdsc.edu/papers/faerman_ijhpca04.pdf
284 - <b>Resource Allocation for Steerable Parallel Parameter Searches</b>\n
285 by <em>M. Faerman, A. Birnbaum, H. Casanova, F. Berman</em>\n
286 Proceedings of the Grid Computing Workshop, Baltimore, 157--169, November 2002.\n
287 http://grail.sdsc.edu/projects/vi_itr/grid02.pdf
289 - <b>Applying Scheduling and Tuning to On-line Parallel Tomography </b>\n
290 by <em>Shava Smallen, Henri Casanova, Francine Berman</em>\n
291 in Proceedings of Supercomputing 2001\n
292 http://grail.sdsc.edu/papers/tomo_journal.ps.gz
294 - <b>Heuristics for Scheduling Parameter Sweep applications in Grid environments</b>\n
295 by <em>Henri Casanova, Arnaud Legrand, Dmitrii Zagorodnov and Francine Berman</em>\n
296 in Proceedings of the 9th Heterogeneous Computing workshop (HCW'2000), pp349-363.\n
297 http://navet.ics.hawaii.edu/~casanova/homepage/papers/hcw00_pst.pdf
302 \li <b>Optimal algorithms for scheduling divisible workloads on
303 heterogeneous systems</b>\n
304 by <em>Olivier Beaumont and Arnaud Legrand and Yves Robert</em>\n
305 in Proceedings of the 17th International Parallel and Distributed Processing Symposium (IPDPS'03).\n
306 Preliminary version on ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-36.ps.gz
309 \li <b>On-line Parallel Tomography</b>\n
310 by <em>Shava Smallen</em>\n
311 Masters Thesis, UCSD, May 2001