is certainly the most well known algorithm for which the convergence proof is
given. From a practical point of view, when a node wants to balance a part of
its load to some of its neighbors, the strategy is not described. In this
-paper, we propose a strategy called \texttt{best effort} which tries to balance
+paper, we propose a strategy called \emph{best effort} which 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, asynchronous iterative algorithms in which an asynchronous load
balancing algorithm is implemented most of the time can dissociate messages
concerning load transfers and message concerning load information. In order to
increase the converge of a load balancing algorithm, we propose a simple
-heuristic called \texttt{virtual load} which allows a node that receives an load
+heuristic called \emph{virtual load} which allows a node that receives an load
information message to integrate the load that it will receive later in its
load (virtually) and consequently sends a (real) part of its load to some of its
neighbors. In order to validate our approaches, we have defined a simulator
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
+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
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}
+followed. Consequently, we propose a new strategy called \emph{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,
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.
+neighbors if required. We call this trick the \emph{virtual load} mecanism.
Some conditions are required to ensure the convergence. One of them can be
-called the \texttt{ping-pong} condition which specifies that:
+called the \emph{ping-pong} condition which specifies that:
\begin{equation}
x_i(t)-\sum _{k\in V(i)} s_{ik}(t) \geq x_j^i(t)+s_{ij}(t)
\end{equation}
In this case, processor $2$ can either sends load to processor $1$ or processor
$3$. If it sends load to processor $1$ it will not satisfy condition
(\ref{eq:ping-pong}) because after the sending it will be less loaded that
-$x_3^2(t)$. So we consider that the \texttt{ping-pong} condition is probably to
+$x_3^2(t)$. 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.
