+\section{Introduction}
+
+Load balancing algorithms are extensively used in parallel and distributed
+applications in order to reduce the execution times. They can be applied in
+different scientific fields from high performance computation to micro sensor
+networks. They are iterative by nature. In literature many kinds of load
+balancing algorithms have been studied. They can be classified according
+different criteria: centralized or decentralized, in static or dynamic
+environment, with homogeneous or heterogeneous load, using synchronous or
+asynchronous iterations, with a static topology or a dynamic one which evolves
+during time. In this work, we focus on asynchronous load balancing algorithms
+where computer nodes are considered homogeneous and with homogeneous load with
+no external load. In this context, Bertsekas and Tsitsiklis have proposed an
+algorithm which is definitively a reference for many works. In their work, they
+proved that under classical hypotheses of asynchronous iterative algorithms and
+a special constraint avoiding \texttt{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}.
+
+Although the Bertsekas and Tsitsiklis' algorithm describes the condition to
+ensure the convergence, there is no indication or strategy to really implement
+the load distribution. In other word, a node can send a part of its load to one
+or many of its neighbors while all the convergence conditions are
+followed. Consequently, we propose a new strategy called \texttt{best effort}
+that tries to balance the load of a node to all its less loaded neighbors while
+ensuring that all the nodes concerned by the load balancing phase have the same
+amount of load. Moreover, when real asynchronous applications are considered,
+using asynchronous load balancing algorithms can reduce the execution
+times. Most of the times, it is simpler to distinguish load information messages
+from data migration messages. Formers ones allows a node to inform its
+neighbors of its current load. These messages are very small, they can be sent
+quite often. For example, if an computing iteration takes a significant times
+(ranging from seconds to minutes), it is possible to send a new load information
+message at each neighbor at each iteration. Latter messages contains data that
+migrates from one node to another one. Depending on the application, it may have
+sense or not that nodes try to balance a part of their load at each computing
+iteration. But the time to transfer a load message from a node to another one is
+often much nore longer that to time to transfer a load information message. So,
+when a node receives the information that later it will receive a data message,
+it can take this information into account and it can consider that its new load
+is larger. Consequently, it can send a part of it real load to some of its
+neighbors if required. We call this trick the \texttt{virtual load} mecanism.
+
+
+
+So, in this work, we propose a new strategy for improving the distribution of
+the load and a simple but efficient trick that also improves the load
+balacing. Moreover, we have conducted many simulations with simgrid in order to
+validate our improvements are really efficient. Our simulations consider that in
+order to send a message, a latency delays the sending and according to the
+network performance and the message size, the time of the reception of the
+message also varies.
+
+In the following of this paper, Section~\ref{BT algo} describes the Bertsekas
+and Tsitsiklis' asynchronous load balancing algorithm. Moreover, we present a
+possible problem in the convergence conditions. Section~\ref{Best-effort}
+presents the best effort strategy which provides an efficient way to reduce the
+execution times. In Section~\ref{Virtual load}, the virtual load mecanism is
+proposed. Simulations allowed to show that both our approaches are valid using a
+quite realistic model detailed in Section~\ref{Simulations}. Finally we give a
+conclusion and some perspectives to this work.
+
+
+
