\begin{tabular}[t]{@{}l@{:~}l@{}}}{%
\end{tabular}}
-\newcommand{\FIXME}[1]{%
- \textbf{$\triangleright$\marginpar{\textbf{[FIXME]}}~#1}}
+\newcommand{\FIXMEmargin}[1]{%
+ \marginpar{\textbf{[FIXME]} {\footnotesize #1}}}
+\newcommand{\FIXME}[2][]{%
+ \ifx #2\relax\relax \FIXMEmargin{#1}%
+ \else \textbf{$\triangleright$\FIXMEmargin{#1}~#2}\fi}
\newcommand{\VAR}[1]{\textit{#1}}
DASUD~\cite{cortes+ripoll+cedo+al.2002.asynchronous}, propose a
version working with integer load. This work was later generalized by
the same authors in \cite{cedo+cortes+ripoll+al.2007.convergence}.
-\FIXME{Rajouter des choses ici.}
+\FIXME{Rajouter des choses ici. Lesquelles ?}
Although the Bertsekas and Tsitsiklis' algorithm describes the condition to
ensure the convergence, there is no indication or strategy to really implement
$x_3^2(t)$. So we consider that the \emph{ping-pong} condition is probably to
strong. Currently, we did not try to make another convergence proof without this
condition or with a weaker condition.
-%
-\FIXME{Develop: We have the feeling that such a weaker condition
- exists, because (it's not a proof, but) we have never seen any
- scenario that is not leading to convergence, even with LB-strategies
- that are not fulfilling these two conditions.}
+
+Nevertheless, we conjecture that such a weaker condition exists. In fact, we
+have never seen any scenario that is not leading to convergence, even with
+load-balancing strategies that are not exactly fulfilling these two conditions.
+
+It may be the subject of future work to express weaker conditions, and to prove
+that they are sufficient to ensure the convergence of the load-balancing
+algorithm.
\section{Best effort strategy}
\label{Best-effort}
-In this section we describe a new load-balancing strategy that we call
-\emph{best effort}. The general idea behind this strategy is that each
-processor, that detects it has more load than some of its neighbors,
-sends some load to the most of its less loaded neighbors, doing its
-best to reach the equilibrium between those neighbors and himself.
+In this section we describe a new load-balancing strategy that we call
+\emph{best effort}. First, we explain the general idea behind this strategy,
+and then we describe some variants of this basic strategy.
+
+\subsection{Basic strategy}
+
+The general idea behind the \emph{best effort} strategy is that each processor,
+that detects it has more load than some of its neighbors, sends some load to the
+most of its less loaded neighbors, doing its best to reach the equilibrium
+between those neighbors and himself.
More precisely, when a processor $i$ is in its load-balancing phase,
he proceeds as following.
\end{equation*}
\end{enumerate}
-\FIXME{describe parameter $k$}
-
-\section{Other strategies}
-\label{Other}
+\subsection{Leveling the amount to send}
-\FIXME{Réécrire en angliche.}
+With the aforementioned basic strategy, each node does its best to reach the
+equilibrium with its neighbors. Since each node may be taking the same kind of
+decision at the same moment, there is the risk that a node receives load from
+several of its neighbors, and then is temporary going off the equilibrium state.
+This is particularly true with strongly connected applications.
-% \FIXME{faut-il décrire les stratégies makhoul et simple ?}
+In order to reduce this effect, we add the ability to level the amount to send.
+The idea, here, is to make smaller steps toward the equilibrium, such that a
+potentially wrong decision has a lower impact.
-% \paragraph{simple} Tentative de respecter simplement les conditions de Bertsekas.
-% Parmi les voisins moins chargés que soi, on sélectionne :
-% \begin{itemize}
-% \item un des moins chargés (vmin) ;
-% \item un des plus chargés (vmax),
-% \end{itemize}
-% puis on équilibre avec vmin en s'assurant que notre charge reste
-% toujours supérieure à celle de vmin et à celle de vmax.
+Concretely, once $s_{ij}$ has been evaluated as before, it is simply divided by
+some configurable factor. That's what we named the ``parameter $k$'' in
+Section~\ref{Results}. The amount of data to send is then $s_{ij}(t) = (\bar{x}
+- x^i_j(t))/k$.
+\FIXME[check that it's still named $k$ in Sec.~\ref{Results}]{}
-% On envoie donc (avec "self" pour soi-même) :
-% \[
-% \min\left(\frac{load(self) - load(vmin)}{2}, load(self) - load(vmax)\right)
-% \]
+\section{Other strategies}
+\label{Other}
-\paragraph{makhoul} Ordonne les voisins du moins chargé au plus chargé
-puis calcule les différences de charge entre soi-même et chacun des
-voisins.
+Another load balancing strategy, working under the same conditions, was
+previously developed by Bahi, Giersch, and Makhoul in
+\cite{bahi+giersch+makhoul.2008.scalable}. In order to assess the performances
+of the new \emph{best effort}, we naturally chose to compare it to this anterior
+work. More precisely, we will use the algorithm~2 from
+\cite{bahi+giersch+makhoul.2008.scalable} and, in the following, we will
+reference it under the name of Makhoul's.
-Ensuite, pour chaque voisin, dans l'ordre, et tant qu'on reste plus
-chargé que le voisin en question, on lui envoie 1/(N+1) de la
-différence calculée au départ, avec N le nombre de voisins.
+Here is an outline of the Makhoul's algorithm. When a given node needs to take
+a load balancing decision, it starts by sorting its neighbors by increasing
+order of their load. Then, it computes the difference between its own load, and
+the load of each of its neighbors. Finally, taking the neighbors following the
+order defined before, the amount of load to send $s_{ij}$ is computed as
+$1/(N+1)$ of the load difference, with $N$ being the number of neighbors. This
+process continues as long as the node is more loaded than the considered
+neighbor.
-C'est l'algorithme~2 dans~\cite{bahi+giersch+makhoul.2008.scalable}.
\section{Virtual load}
\label{Virtual load}
available at
\url{http://info.iut-bm.univ-fcomte.fr/staff/giersch/software/loba.tar.gz}.
-\FIXME{ajouter des détails sur la gestion de la charge virtuelle ?}
+\FIXME{ajouter des détails sur la gestion de la charge virtuelle ?
+par ex, donner l'idée générale de l'implémentation. l'idée générale est déja décrite en section~\ref{Virtual load}}
\subsection{Experimental contexts}
\label{Contexts}
number of computing iterations (2000 in our case).
Note that this convergence detection was implemented in a centralized manner.
-This is easy to do within the simulator, but it's obviously not realistic. In
-a real application we would have chosen a decentralized convergence detection algorithm, like the one described in \cite{10.1109/TPDS.2005.2}.
+This is easy to do within the simulator, but it's obviously not realistic. In a
+real application we would have chosen a decentralized convergence detection
+algorithm, like the one described by Bahi, Contassot-Vivier, Couturier, and
+Vernier in \cite{10.1109/TPDS.2005.2}.
\paragraph{Platforms}
sorts of platforms differ by their underlaid network topology. On the one hand,
we have homogeneous platforms, modeled as a cluster. On the other hand, we have
heterogeneous platforms, modeled as the interconnection of a number of clusters.
+
+The clusters were modeled by a fixed number of computing nodes interconnected
+through a backbone link. Each computing node has a computing power of
+1~GFlop/s, and is connected to the backbone by a network link whose bandwidth is
+of 125~MB/s, with a latency of 50~$\mu$s. The backbone has a network bandwidth
+of 2.25~GB/s, with a latency of 500~$\mu$s.
+
The heterogeneous platform descriptions were created by taking a subset of the
Grid'5000 infrastructure\footnote{Grid'5000 is a French large scale experimental
Grid (see \url{https://www.grid5000.fr/}).}, as described in the platform file
\texttt{g5k.xml} distributed with SimGrid. Note that the heterogeneity of the
-platform only comes from the network topology. The processor speeds, and
-network bandwidths were normalized since our algorithms currently are not aware
-of such heterogeneity. We arbitrarily chose to fix the processor speed to
-1~GFlop/s, and the network bandwidth to 125~MB/s, with a latency of 50~$\mu$s,
-except for the links between geographically distant sites, where the network
-bandwidth was fixed to 2.25~GB/s, with a latency of 500~$\mu$s.
+platform here only comes from the network topology. Indeed, since our
+algorithms currently do not handle heterogeneous computing resources, the
+processor speeds were normalized, and we arbitrarily chose to fix them to
+1~GFlop/s.
Then we derived each sort of platform with four different number of computing
nodes: 16, 64, 256, and 1024 nodes.
that can be compared between simulations of different sizes.
This metric is expected to give an idea of the ability of the strategy to
- diffuse the load quickly, lesser is better.
+ diffuse the load quickly. A smaller value is better.
\item[\textbf{average convergence date:}] that's the average of the dates when
all nodes reached the convergence state. The dates are measured as a number
reached the convergence state.
These two dates give an idea of the time needed by the strategy to reach the
- equilibrium state, lesser is doubtlessly better.
+ equilibrium state. A smaller value is better.
-\item[\textbf{data transfer amount:}] that's the sum of the amount of data of
- all transfers during the simulation. This sum is then normalized by dividing
- it by the total amount of data present in the system.
+\item[\textbf{data transfer amount:}] that's the sum of the amount of all data
+ transfers during the simulation. This sum is then normalized by dividing it
+ by the total amount of data present in the system.
This metric is expected to give an idea of the efficiency of the strategy in
terms of data movements, i.e. its ability to reach the equilibrium with fewer
- transfers.
+ transfers. Again, a smaller value is better.
\end{description}
\subsection{Validation of our approaches}
\label{Results}
+Dans cet ordre:
+...
+- comparer be/makhoul -> be tient la route
+ -> en réel uniquement
+
+- valider l'extension virtual load -> c'est 'achement bien
+- proposer le -k -> ça peut aider dans certains cas
+- conclure avec la version entière -> on n'a pas l'effet d'escalier !
+Q: comment inclure les types/tailles de platesformes ?
+Q: comment faire des moyennes ?
+...
+On constate quoi (vérifier avec les chiffres)?
+\begin{itemize}
+\item cluster ou grid, entier ou réel, ne font pas de grosses différences
+
+\item bookkeeping? améliore souvent les choses, parfois au prix d'un retard au démarrage
+
+\item makhoul? se fait battre sur les grosses plateformes
+
+\item taille de plateforme?
+
+\item ratio comp/comm?
+
+\item option $k$? peut-être intéressant sur des plateformes fortement interconnectées (hypercube)
+
+\item volume de comm? souvent, besteffort/plain en fait plus. pourquoi?
+
+\item répartition initiale de la charge ?
+
+\item integer mode sur topo. line n'a jamais fini en plain? vérifier si ce n'est
+ pas à cause de l'effet d'escalier que bk est capable de gommer.
+
+\end{itemize}
+
+\begin{itshape}
On veut montrer quoi ? :
+\FIXME{remove that part}
1) best plus rapide que les autres (simple, makhoul)
2) avantage virtual load
Simulation avec temps définies assez long et on mesure la qualité avec : volume de calcul effectué, volume de données échangées
Mais aussi simulation avec temps court qui montre que seul best converge
-
Expés avec ratio calcul/comm rapide et lent
Quelques expés avec charge initiale aléatoire plutot que sur le premier proc
Prendre un réseau hétérogène et rendre processeur homogène
Taille : 10 100 très gros
+\end{itshape}
\section{Conclusion and perspectives}
+\FIXME{conclude!}
+
\begin{acknowledgements}
Computations have been performed on the supercomputer facilities of
the Mésocentre de calcul de Franche-Comté.
\end{acknowledgements}
+\FIXME{find and add more references}
\bibliographystyle{spmpsci}
\bibliography{biblio}
% LocalWords: Raphaël Couturier Arnaud Giersch Abderrahmane Sider Franche ij
% LocalWords: Bertsekas Tsitsiklis SimGrid DASUD Comté Béjaïa asynchronism ji
-% LocalWords: ik isend irecv Cortés et al chan ctrl fifo Makhoul
+% LocalWords: ik isend irecv Cortés et al chan ctrl fifo Makhoul GFlop xml pre
+% LocalWords: FEMTO Makhoul's fca bdee cdde Contassot Vivier underlaid
