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.
40 \section pub_simulation Other publications about simulation
42 \li <b>A Network Model for Simulation of Grid Application</b>\n
43 by <em>Henri Casanova and Loris Marchal</em>\n
45 In this work we investigate network models that can be
46 potentially employed in the simulation of scheduling algorithms for
47 distributed computing applications. We seek to develop a model of TCP
48 communication which is both high-level and realistic. Previous research
49 works show that accurate and global modeling of wide-area networks, such
50 as the Internet, faces a number of challenging issues. However, some
51 global models of fairness and bandwidth-sharing exist, and can be link
52 withthe behavior of TCP. Using both previous results and simulation (with
53 NS), we attempt to understand the macroscopic behavior of
54 TCP communications. We then propose a global model of the network for the
55 Grid platform. We perform partial validation of this model in
56 simulation. The model leads to an algorithm for computing
57 bandwidth-sharing. This algorithm can then be implemented as part of Grid
58 application simulations. We provide such an implementation for the
59 SimGrid simulation toolkit.\n
60 ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-40.ps.gz
63 \li <b>MetaSimGrid : Towards realistic scheduling simulation of
64 distributed applications</b>\n
65 by <em>Arnaud Legrand and Julien Lerouge</em>\n
66 Most scheduling problems are already hard on homogeneous
67 platforms, they become quite intractable in an heterogeneous
68 framework such as a metacomputing grid. In the best cases, a
69 guaranteed heuristic can be found, but most of the time, it is
70 not possible. Real experiments or simulations are often
71 involved to test or to compare heuristics. However, on a
72 distributed heterogeneous platform, such experiments are
73 technically difficult to drive, because of the genuine
74 instability of the platform. It is almost impossible to
75 guarantee that a platform which is not dedicated to the
76 experiment, will remain exactly the same between two tests,
77 thereby forbidding any meaningful comparison. Simulations are
78 then used to replace real experiments, so as to ensure the
79 reproducibility of measured data. A key issue is the
80 possibility to run the simulations against a realistic
81 environment. The main idea of trace-based simulation is to
82 record the platform parameters today, and to simulate the
83 algorithms tomorrow, against the recorded data: even though it
84 is not the current load of the platform, it is realistic,
85 because it represents a fair summary of what happened
86 previously. A good example of a trace-based simulation tool is
87 SimGrid, a toolkit providing a set of core abstractions and
88 functionalities that can be used to easily build simulators for
89 specific application domains and/or computing environment
90 topologies. Nevertheless, SimGrid lacks a number of convenient
91 features to craft simulations of a distributed application
92 where scheduling decisions are not taken by a single
93 process. Furthermore, modeling a complex platform by hand is
94 fastidious for a few hosts and is almost impossible for a real
95 grid. This report is a survey on simulation for scheduling
96 evaluation purposes and present MetaSimGrid, a simulator built
98 ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-28.ps.gz
100 \li <b>SimGrid: A Toolkit for the Simulation of Application
102 by <em>Henri Casanova</em>\n
103 Advances in hardware and software technologies have made it
104 possible to deploy parallel applications over increasingly large
105 sets of distributed resources. Consequently, the study of
106 scheduling algorithms for such applications has been an active area
107 of research. Given the nature of most scheduling problems one must
108 resort to simulation to effectively evaluate and compare their
109 efficacy over a wide range of scenarios. It has thus become
110 necessary to simulate those algorithms for increasingly complex
111 distributed, dynamic, heterogeneous environments. In this paper we
112 present SimGrid, a simulation toolkit for the study of scheduling
113 algorithms for distributed application. This paper gives the main
114 concepts and models behind SimGrid, describes its API and
115 highlights current implementation issues. We also give some
116 experimental results and describe work that builds on SimGrid's
118 http://grail.sdsc.edu/papers/simgrid_ccgrid01.ps.gz
120 \section pub_research Papers using SimGrid results
122 This list is a selection of articles. We list only papers written by people
123 external to the development group, but we also use our tool ourselves.
127 \li <b>On Dynamic Resource Management Mechanism using Control
128 Theoretic Approach for Wide-Area Grid Computing</b>\n
129 by <em>Hiroyuki Ohsaki, Soushi Watanabe, and Makoto Imase</em>
130 in Proceedings of IEEE Conference on Control Applications (CCA 2005), Aug. 2005.\n
131 http://www.ispl.jp/~oosaki/papers/Ohsaki05_CCA.pdf
133 \li <b>Evaluation of Meta-scheduler Architectures and Task Assignment Policies for
134 high Throughput Computing</b>
135 by <em>Eddy Caron, Vincent Garonne and Andrei Tsaregorodtsev</em>\n
136 Proceedings of 4th Internationnal Symposium on Parallel and
137 Distributed Computing Job Scheduling Strategies for Parallel
138 Processing (ISPDC'05), July 2005.\n
139 http://www.ens-lyon.fr/LIP/Pub/Rapports/RR/RR2005/RR2005-27.pdf
143 \li <b>Deadline Scheduling with Priority for Client-Server Systems on the Grid</b>\n
144 by <em>E Caron, PK Chouhan, F Desprez</em>
145 in IEEE International Conference On Grid Computing. Super Computing 2004, oct 2004.
147 \li <b>Efficient Scheduling Heuristics for GridRPC Systems</b>
148 by <em>Y. Caniou and E. Jeannot.</em>\n
149 in IEEE QoS and Dynamic System workshop (QDS) of International Conference
150 on Parallel and Distributed Systems (ICPADS), New-Port Beach California, USA,
151 pages 621-630, July 2004\n
152 http://graal.ens-lyon.fr/~ycaniou/QDS04.ps
154 \li <b>Exploiting Replication and Data Reuse to Efficiently Schedule
155 Data-intensive Applications on Grids</b>\n
156 by <em> E. Santos-Neto, W. Cirne, F. Brasileiro, A. Lima.</em>\n
157 Proceedings of 10th Job Scheduling Strategies for Parallel Processing, June 2004.\n
158 http://www.lsd.ufcg.edu.br/~elizeu/articles/jsspp.v6.pdf
162 \li <b>Link-Contention-Aware Genetic Scheduling Using Task Duplication in Grid Environments</b>\n
163 by <em>Wensheng Yao, Xiao Xie and Jinyuan You</em>\n
164 in Grid and Cooperative Computing: Second International Workshop, GCC 2003, Shanghai, China, December 7-10, 2003 (LNCS)
165 http://www.chinagrid.edu.cn/chinagrid/download/GCC2003/pdf/266.pdf
167 \li <b>New Dynamic Heuristics in the Client-Agent-Server Model</b>\n
168 by <em>Y. Caniou and E. Jeannot</em>\n
169 in IEEE 13th Heteregeneous Computing Workshop - HCW'03, Nice, France, April 2003.\n
170 http://graal.ens-lyon.fr/~ycaniou/HCW03.ps
172 \li <b>A Hierarchical Resource Reservation Algorithm for Network Enabled Servers</b>\n
173 by <em>E Caron, F Desprez, F Petit, V Villain</em>\n
174 in the 17th International Parallel and Distributed Processing Symposium -- IPDPS'03, Nice - France, April 2003.
178 \li <b>Optimal algorithms for scheduling divisible workloads on
179 heterogeneous systems</b>\n
180 by <em>Olivier Beaumont and Arnaud Legrand and Yves Robert</em>\n
181 in Proceedings of the 17th International Parallel and Distributed Processing Symposium (IPDPS'03).\n
182 Preliminary version on ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR2002/RR2002-36.ps.gz
184 \li <b>On-line Parallel Tomography</b>\n
185 by <em>Shava Smallen</em>\n
186 Masters Thesis, UCSD, May 2001
187 \li <b>Applying Scheduling and Tuning to On-line Parallel Tomography </b>\n
188 by <em>Shava Smallen, Henri Casanova, Francine Berman</em>\n
189 in Proceedings of Supercomputing 2001
190 \li <b>Heuristics for Scheduling Parameter Sweep applications in
191 Grid environments</b>\n
192 by <em>Henri Casanova, Arnaud Legrand, Dmitrii Zagorodnov and
193 Francine Berman</em>\n
194 in Proceedings of the 9th Heterogeneous Computing workshop
195 (HCW'2000), pp349-363.
