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>The SimGrid Project - Simulation and Deployment of Distributed Applications</b>\n
54 by <em>A. Legrand, M. Quinson, K. Fujiwara, H. Casanova</em>\n
55 <b>POSTER</b> in Proceedings of the IEEE International Symposium on High Performance Distributed Computing (HPDC-15), Paris, France, May 2006.\n
57 <a href="http://navet.ics.hawaii.edu/~casanova/homepage/papers/simgrid_hpdc06.pdf"><img src="poster_thumbnail.png" /></a>
59 http://navet.ics.hawaii.edu/~casanova/homepage/papers/simgrid_hpdc06.pdf
61 \li <b>A Network Model for Simulation of Grid Application</b>\n
62 by <em>Henri Casanova and Loris Marchal</em>\n
64 In this work we investigate network models that can be
65 potentially employed in the simulation of scheduling algorithms for
66 distributed computing applications. We seek to develop a model of TCP
67 communication which is both high-level and realistic. Previous research
68 works show that accurate and global modeling of wide-area networks, such
69 as the Internet, faces a number of challenging issues. However, some
70 global models of fairness and bandwidth-sharing exist, and can be link
71 withthe behavior of TCP. Using both previous results and simulation (with
72 NS), we attempt to understand the macroscopic behavior of
73 TCP communications. We then propose a global model of the network for the
74 Grid platform. We perform partial validation of this model in
75 simulation. The model leads to an algorithm for computing
76 bandwidth-sharing. This algorithm can then be implemented as part of Grid
77 application simulations. We provide such an implementation for the
78 SimGrid simulation toolkit.\n
79 ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-40.ps.gz
82 \li <b>MetaSimGrid : Towards realistic scheduling simulation of
83 distributed applications</b>\n
84 by <em>Arnaud Legrand and Julien Lerouge</em>\n
85 Most scheduling problems are already hard on homogeneous
86 platforms, they become quite intractable in an heterogeneous
87 framework such as a metacomputing grid. In the best cases, a
88 guaranteed heuristic can be found, but most of the time, it is
89 not possible. Real experiments or simulations are often
90 involved to test or to compare heuristics. However, on a
91 distributed heterogeneous platform, such experiments are
92 technically difficult to drive, because of the genuine
93 instability of the platform. It is almost impossible to
94 guarantee that a platform which is not dedicated to the
95 experiment, will remain exactly the same between two tests,
96 thereby forbidding any meaningful comparison. Simulations are
97 then used to replace real experiments, so as to ensure the
98 reproducibility of measured data. A key issue is the
99 possibility to run the simulations against a realistic
100 environment. The main idea of trace-based simulation is to
101 record the platform parameters today, and to simulate the
102 algorithms tomorrow, against the recorded data: even though it
103 is not the current load of the platform, it is realistic,
104 because it represents a fair summary of what happened
105 previously. A good example of a trace-based simulation tool is
106 SimGrid, a toolkit providing a set of core abstractions and
107 functionalities that can be used to easily build simulators for
108 specific application domains and/or computing environment
109 topologies. Nevertheless, SimGrid lacks a number of convenient
110 features to craft simulations of a distributed application
111 where scheduling decisions are not taken by a single
112 process. Furthermore, modeling a complex platform by hand is
113 fastidious for a few hosts and is almost impossible for a real
114 grid. This report is a survey on simulation for scheduling
115 evaluation purposes and present MetaSimGrid, a simulator built
117 ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-28.ps.gz
119 \li <b>SimGrid: A Toolkit for the Simulation of Application
121 by <em>Henri Casanova</em>\n
122 Advances in hardware and software technologies have made it
123 possible to deploy parallel applications over increasingly large
124 sets of distributed resources. Consequently, the study of
125 scheduling algorithms for such applications has been an active area
126 of research. Given the nature of most scheduling problems one must
127 resort to simulation to effectively evaluate and compare their
128 efficacy over a wide range of scenarios. It has thus become
129 necessary to simulate those algorithms for increasingly complex
130 distributed, dynamic, heterogeneous environments. In this paper we
131 present SimGrid, a simulation toolkit for the study of scheduling
132 algorithms for distributed application. This paper gives the main
133 concepts and models behind SimGrid, describes its API and
134 highlights current implementation issues. We also give some
135 experimental results and describe work that builds on SimGrid's
137 http://grail.sdsc.edu/papers/simgrid_ccgrid01.ps.gz
139 \section pub_ext Papers that use SimGrid-generated results (not counting our owns)
141 This list is a selection of articles. We list only papers written by people
142 external to the development group, but we also use our tool ourselves (see
146 - <b>Simbatch: an API for simulating and predicting the performance of parallel resources and batch systems.</b>\n
147 INRIA Research Report 6040, November 2006.\n
148 https://hal.inria.fr/inria-00115880
149 - <b>Simbatch : une API pour la simulation et la prédiction de performances de systèmes batch</b>\n
150 by <em>Jean-Sébastien Gay and Yves Caniou</em>.\n
151 In 17ème Rencontres Francophones du Parallélisme, des Architectures et des Systèmes, RenPar'17.\n
152 October 4-6, Perpignan, France
153 - <b>Metascheduling Multiple Resource Types using the MMKP</b>\n
154 by <em>D. Vanderster, N. Dimopoulos, R. Sobie</em>\n
155 7th IEEE/ACM International Conference on Grid Computing\n
156 Barcelona, September 28th-29th 2006
157 - <b>Master-Slave Tasking on Asymmetric Networks</b>\n
158 by <em>Cyril Banino-Rokkones, Olivier Beaumont and Lasse Natvig</em>.\n
159 In Proceedings of 12th International Euro-Par Conference, Euro-Par 2006.\n
160 August 29 - September 1, Pages 167--176, Dresden, Germany.
161 - <b>Critical Path and Area Based Scheduling of Parallel Task Graphs on Heterogeneous Platforms</b>\n
162 by <em>Tchimou N'Takpé and Frédéric Suter</em>\n
163 Proceedings of the Twelfth International Conference on Parallel and Distributed Systems (ICPADS)\n
164 Minneapolis, MN, July 12-15, 2006.
165 - <b>Sensitivity Analysis of Knapsack-based Task Scheduling on the Grid</b>\n
166 by <em>D.C. Vanderster and N.J. Dimopoulos</em>.\n
167 In Proceedings of The 20th ACM International Conference on Supercomputing\n
168 Cairns, Australia, June 28-July 1, 2006.\n
169 http://portal.acm.org/citation.cfm?id=1183401.1183446&coll=GUIDE&dl=%23url.coll
170 - <b>Hierarchical Scheduling of Independent Tasks with Shared Files</b>\n
171 by <em>H. Senger, F. Silva, W. Nascimento</em>.\n
172 Proceedings of the Sixth IEEE International Symposium on Cluster
173 Computing and the Grid Workshop (CCGRIDW'06)\n
174 Singapore, 16-19 May 2006.\n
175 http://www.unisantos.br/mestrado/informatica/hermes/File/senger-HierarchicalScheduling-Workshop-TB120.pdf
176 - <b>Evaluation of Knapsack-based Scheduling using the NPACI JOBLOG</b>\n
177 by <em>D. Vanderster, N. Dimopoulos, R. Parra-Hernandez and R. Sobie</em>.\n
178 20th International Symposium on High-Performance Computing in an
179 Advanced Collaborative Environment (HPCS'06)\n
180 St. John's, Newfoundland, Canada, 14-17 May 2006\n
181 http://doi.ieeecomputersociety.org/10.1109/HPCS.2006.23
184 - <b>On Dynamic Resource Management Mechanism using Control
185 Theoretic Approach for Wide-Area Grid Computing</b>\n
186 by <em>Hiroyuki Ohsaki, Soushi Watanabe, and Makoto Imase</em>\n
187 in Proceedings of IEEE Conference on Control Applications (CCA 2005), Aug. 2005.\n
188 http://www.ispl.jp/~oosaki/papers/Ohsaki05_CCA.pdf
189 - <b>Evaluation of Meta-scheduler Architectures and Task Assignment Policies for
190 high Throughput Computing</b>\n
191 by <em>Eddy Caron, Vincent Garonne and Andrei Tsaregorodtsev</em>\n
192 Proceedings of 4th Internationnal Symposium on Parallel and
193 Distributed Computing Job Scheduling Strategies for Parallel
194 Processing (ISPDC'05), July 2005.\n
195 http://www.ens-lyon.fr/LIP/Pub/Rapports/RR/RR2005/RR2005-27.pdf
197 - <b>Deadline Scheduling with Priority for Client-Server Systems on the Grid</b>\n
198 by <em>E Caron, PK Chouhan, F Desprez</em>\n
199 in IEEE International Conference On Grid Computing. Super Computing 2004, oct 2004.
200 - <b>Efficient Scheduling Heuristics for GridRPC Systems</b>\n
201 by <em>Y. Caniou and E. Jeannot.</em>\n
202 in IEEE QoS and Dynamic System workshop (QDS) of International Conference
203 on Parallel and Distributed Systems (ICPADS), New-Port Beach California, USA,
204 pages 621-630, July 2004\n
205 http://graal.ens-lyon.fr/~ycaniou/QDS04.ps
206 - <b>Exploiting Replication and Data Reuse to Efficiently Schedule
207 Data-intensive Applications on Grids</b>\n
208 by <em> E. Santos-Neto, W. Cirne, F. Brasileiro, A. Lima.</em>\n
209 Proceedings of 10th Job Scheduling Strategies for Parallel Processing, June 2004.\n
210 http://www.lsd.ufcg.edu.br/~elizeu/articles/jsspp.v6.pdf
211 - <b>Resource Management and Knapsack Formulations on the Grid</b>\n
212 by <em>R. Parra-Hernandez, D. Vanderster and N. J. Dimopoulos</em>\n
213 Fifth IEEE/ACM International Workshop on Grid Computing (GRID'04)\n
214 http://doi.ieeecomputersociety.org/10.1109/GRID.2004.54
215 - <b>Scheduling BoT Applications in Grids using a Slave Oriented Adaptive
217 by <em>T. Ferreto, C. A. F. De Rose and C. Northfleet.</em>\n
218 Second International Symposium on Parallel and Distributed Processing
219 and Applications (ISPA), 2004, Hong Kong. Published in Lecture Notes in
220 Computer Science (LNCS), Volume 3358, by Springer-Verlag. p. 392-398.
222 - <b>Link-Contention-Aware Genetic Scheduling Using Task Duplication in Grid Environments</b>\n
223 by <em>Wensheng Yao, Xiao Xie and Jinyuan You</em>\n
224 in Grid and Cooperative Computing: Second International Workshop, GCC 2003, Shanghai, China, December 7-10, 2003 (LNCS)\n
225 http://www.chinagrid.edu.cn/chinagrid/download/GCC2003/pdf/266.pdf
226 - <b>New Dynamic Heuristics in the Client-Agent-Server Model</b>\n
227 by <em>Y. Caniou and E. Jeannot</em>\n
228 in IEEE 13th Heteregeneous Computing Workshop - HCW'03, Nice, France, April 2003.\n
229 http://graal.ens-lyon.fr/~ycaniou/HCW03.ps
230 - <b>A Hierarchical Resource Reservation Algorithm for Network Enabled Servers</b>\n
231 by <em>E. Caron, F. Desprez, F. Petit, V. Villain</em>\n
232 in the 17th International Parallel and Distributed Processing Symposium -- IPDPS'03, Nice - France, April 2003.
234 \section pub_self Our own papers that use SimGrid-generated results
236 This list is a selection of the articles we have written that used results
237 generated by SimGrid.
240 - <b>On the Harmfulness of Redundant Batch Requests</b>\n
241 by <em>H. Casanova</em>\n
242 Proceedings of the IEEE International Symposium on High Performance Distributed Computing (HPDC-15), Paris, France, May 2006.\n
243 http://navet.ics.hawaii.edu/~casanova/homepage/papers/hpdc_2006.pdf
244 - <b>An evaluation of Job Scheduling Strategies for Divisible Loads on Grid Platforms</b>\n
245 by <em>Y. Cardinale, H. Casanova</em>\n
246 in Proceedings of the High Performance Computing & Simulation Conference (HPC&S'06), Bonn, Germany, May 2006.\n
247 http://navet.ics.hawaii.edu/~casanova/homepage/papers/cardinale_2006.pdf
248 - <b>Interference-Aware Scheduling</b>\n
249 by <em>B. Kreaseck, L. Carter, H. Casanova, J. Ferrante, S. Nandy</em>\n
250 International Journal of High Performance Computing Applications (IJHPCA), to appear.\n
251 http://navet.ics.hawaii.edu/~casanova/homepage/papers/kreaseck_ijhpca_2005.pdf
253 - <b>From Heterogeneous Task Scheduling to Heterogeneous Mixed Data and Task Parallel Scheduling</b>\n
254 by <em>F. Suter, V. Boudet, F. Desprez, H. Casanova</em>\n
255 Proceedings of Europar, 230--237, (LCNS volume 3149), Pisa, Italy, August 2004.
256 - <b>On the Interference of Communication on Computation</b>\n
257 by <em>B. Kreaseck, L. Carter, H. Casanova, J. Ferrante</em>\n
258 Proceedings of the workshop on Performance Modeling, Evaluation, and Optimization of Parallel and Distributed Systems, Santa Fe, April 2004.\n
259 http://navet.ics.hawaii.edu/~casanova/homepage/papers/k_pmeo2004.pdf
261 - <b>RUMR: Robust Scheduling for Divisible Workloads</b>\n
262 by <em>Y. Yang, H. Casanova</em>\n
263 Proceedings of the 12th IEEE Symposium on High Performance and Distributed Computing (HPDC-12), Seattle, June 2003.\n
264 http://navet.ics.hawaii.edu/~casanova/homepage/papers/yang_hpdc2003.pdf
265 - <b>Resource Allocation Strategies for Guided Parameter Space Searches</b>\n
266 by <em>M. Faerman, A. Birnbaum, F. Berman, H. Casanova</em>\n
267 International Journal of High Performance Computing Applications (IJHPCA), 17(4), 383--402, 2003.\n
268 http://grail.sdsc.edu/papers/faerman_ijhpca04.pdf
270 - <b>Resource Allocation for Steerable Parallel Parameter Searches</b>\n
271 by <em>M. Faerman, A. Birnbaum, H. Casanova, F. Berman</em>\n
272 Proceedings of the Grid Computing Workshop, Baltimore, 157--169, November 2002.\n
273 http://grail.sdsc.edu/projects/vi_itr/grid02.pdf
275 - <b>Applying Scheduling and Tuning to On-line Parallel Tomography </b>\n
276 by <em>Shava Smallen, Henri Casanova, Francine Berman</em>\n
277 in Proceedings of Supercomputing 2001\n
278 http://grail.sdsc.edu/papers/tomo_journal.ps.gz
280 - <b>Heuristics for Scheduling Parameter Sweep applications in Grid environments</b>\n
281 by <em>Henri Casanova, Arnaud Legrand, Dmitrii Zagorodnov and Francine Berman</em>\n
282 in Proceedings of the 9th Heterogeneous Computing workshop (HCW'2000), pp349-363.\n
283 http://navet.ics.hawaii.edu/~casanova/homepage/papers/hcw00_pst.pdf
288 \li <b>Optimal algorithms for scheduling divisible workloads on
289 heterogeneous systems</b>\n
290 by <em>Olivier Beaumont and Arnaud Legrand and Yves Robert</em>\n
291 in Proceedings of the 17th International Parallel and Distributed Processing Symposium (IPDPS'03).\n
292 Preliminary version on ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-36.ps.gz
295 \li <b>On-line Parallel Tomography</b>\n
296 by <em>Shava Smallen</em>\n
297 Masters Thesis, UCSD, May 2001