\author{Raphaël Couturier, David Laiymani and Sébastien Miquée}
\authorrunning{R. Couturier, D. Laiymani and S. Miquée}
\institute{\vspace{-0.2cm}
- University of Franche-Comté \qquad LIFC laboratory\\%[1mm]
+ University of Franche-Comté \qquad LIFC laboratory\\
IUT Belfort-Montb\'eliard, 2 Rue Engel Gros \\ BP 27 90016 Belfort,
France\\\{{\tt
raphael.couturier,david.laiymani,sebastien.miquee}\}{\tt
(for \textit{Farhat Edges-Cuts}) showed an important performances
improvement by significantly reducing the application execution
time. These experiments were performed by using the fully fault
-tolerant JaceP2P-V2 environment, described in next section.
-%This Java based platform is an executing and developing
-%environment dedicated to the AIAC model. By implementing a distributed
-%backup/restore mechanism it is also fully fault
-%tolerant\cite{jaceP2P-v2}.
-In our previous experiments we did not introduce computing nodes
-failures during the computation. As architecture heterogeneity
-continually evolves according to computing nodes volatility, we have
-to take care more precisely about the heterogeneity of the target
-platform. Thus in this paper our main contribution is to propose a new
-mapping algorithm called MAHEVE (\textit{Mapping Algorithm for
- HEterogeneous and Volatile Environments}). This algorithm
-explicitly tackles the heterogeneity issue and introduces a level of
-dynamism in order to adapt itself to the fault tolerance
-mechanisms. Our experiments show gains up to $65\%$ on application
-execution time, with faults during executions, which is about 10
-points better than AIAC-QM and about 25 points better than F-EC, and
-MAHEVE also outperforms them in experiments with no fault during executions.
+tolerant JaceP2P-V2 environment, described in next section. In our
+previous experiments we did not introduce computing nodes failures
+during the computation. As architecture heterogeneity continually
+evolves according to computing nodes volatility, we have to take care
+more precisely about the heterogeneity of the target platform. Thus in
+this paper our main contribution is to propose a new mapping algorithm
+called MAHEVE (\textit{Mapping Algorithm for HEterogeneous and
+ Volatile Environments}). This algorithm explicitly tackles the
+heterogeneity issue and introduces a level of dynamism in order to
+adapt itself to the fault tolerance mechanisms. Our experiments show
+gains up to $65\%$ on application execution time, with faults during
+executions, which is about 10 points better than AIAC-QM and about 25
+points better than F-EC, and MAHEVE also outperforms them in
+experiments with no fault during executions.
The rest of this paper is organized as
\label{sec:pbmodelapp}
-%With the AIAC model, all tasks compute in parallel at the same time,
-%without precedence nor synchronization. During an iteration, each task
-%computes its job and sends its results to its neighbors, and
-%immediately starts the next iteration.
The TIG\cite{tig1} (\textit{Task Interaction Graph}) model is the most
appropriate to our problem, as it only models relationships between
tasks. In this model, all the tasks are considered simultaneously
