proved that under classical hypotheses of asynchronous iterative algorithms and
a special constraint avoiding \emph{ping-pong} effect, an asynchronous
iterative algorithm converge to the uniform load distribution. This work has
-been extended by many authors. For example,
-DASUD~\cite{cortes+ripoll+cedo+al.2002.asynchronous} propose a version working
-with integer load. {\bf Rajouter des choses ici}.
+been extended by many authors. For example, Cortés et al., with
+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}.
+{\bf Rajouter des choses ici}.
Although the Bertsekas and Tsitsiklis' algorithm describes the condition to
ensure the convergence, there is no indication or strategy to really implement
\section{Best effort strategy}
\label{Best-effort}
-We will describe here a new load-balancing strategy that we called
-\emph{best effort}. The general idea behind this strategy is, for a
-processor, to send some load to the most of its neighbors, doing its
+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.
-More precisely, when a processors $i$ is in its load-balancing phase,
+More precisely, when a processor $i$ is in its load-balancing phase,
he proceeds as following.
\begin{enumerate}
\item First, the neighbors are sorted in non-decreasing order of their
