[loba-papers.git] / loba-besteffort / loba-besteffort.tex

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\begin{document}

\begin{frontmatter}

\journal{Parallel Computing}

\title{Best effort strategy and virtual load for\\
  asynchronous iterative load balancing}

\author{Raphaël Couturier}
\ead{raphael.couturier@univ-fcomte.fr}

\author{Arnaud Giersch\corref{cor}}
\ead{arnaud.giersch@univ-fcomte.fr}

\author{Mourad Hakem}
\ead{mourad.hakem@univ-fcomte.fr}

\address{%
  FEMTO-ST Institute, Univ Bourgogne Franche-Comté, Belfort, France}

\cortext[cor]{Corresponding author.}

\begin{abstract}
  Most of the time, asynchronous load balancing algorithms have extensively been
  studied in a theoretical point of view. The Bertsekas and Tsitsiklis'
  algorithm~\cite
  %[section~7.4]
  {bertsekas+tsitsiklis.1997.parallel} is undeniably 
  the most well known algorithm for which the asymptotic convergence proof is given. 
  From a
  practical point of view, when a node needs to balance a part of its load to
  some of its neighbors, the algorithm's description is unfortunately too succinct, and no details are given on what is really sent and how the load balancing decisions are taken. In this paper, we
  propose a new strategy called \besteffort{} which aims to balance the load
  of a node to all its less loaded neighbors while ensuring that all involved nodes by the load balancing phase have the same amount of load. Moreover, since 
  asynchronous iterative algorithms are less sensitive to communications delays 
  and their variations, both load transfer and load information messages are dissociated. 
  To speedup the convergence time of the load balancing process, we propose {\it a clairvoyant virtual load} heuristic which allows 
  %asynchronous iterative algorithms, in which an asynchronous load balancing
  %algorithm is implemented, can dissociate, most of the time, 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 \emph{virtual load}. This heuristic allows 
  a node receiving a load
  information message to integrate the future virtual load (if any) in its load's list, even if the load has not been received yet. This leads to have predictive snapshots of nodes' loads at each iteration of the load balancing process.  Consequently, the notified node sends a real part of its load to some of
  its neighbors taking into account the virtual load it will receive in the subsequent time-steps. Based on SimGrid simulator, series of test-bed scenarios are considered and many QoS metrics are evaluated to show the usefulness of the proposed algorithm.  %In order to validate our approaches, we have defined a
 % simulator based on SimGrid which allowed us to conduct many experiments.
\end{abstract}

% \begin{keywords}
%   %% keywords here, in the form: keyword \sep keyword
% \end{keywords}

\end{frontmatter}

\section{Introduction}

Load  balancing algorithms  are  widely used  in  parallel and  distributed
applications to achieve high performances in terms of response time, throughput and resources usage. They play an important role and arise in various fields ranging from parallel and distributed 
computing systems to wireless sensor networks (WSN). 
The objective of load balancing is to orchestrate the distribution of the global workload so that 
the load difference between the computational resources of the network is 
minimized as low as possible. Unfortunately, this problem is known to be {\bf NP-hard} in its 
general forms and heuristics are required to achieve sub-optimal solutions but in 
polynomial time complexity. 

In this paper, we focus on asynchronous load balancing of non negative real numbers of {\it divisible loads}
in homogeneous distributed systems. Loads can be divided in arbitrary {\it fine-grain} parallel parts size 
that can be processed independently of each other. This model of divisible loads arise in 
a wide range of real-world applications. Common examples among many, include signal processing, 
feature extraction and edge detection in image processing, records search in a huge databases, 
average consensus in WSN, pattern search in Big data and so on. % c'est pout toi raphael ;-)


In the literature, the problem of load balancing has been formulated and studied in various ways. The first pioneering work is due to Bertsekas  and Tsitsiklis~\cite{bertsekas+tsitsiklis.1997.parallel}. Under some specific hypothesis and {\it ping-pong} awareness conditions (see section~\ref{sec.bt-algo} for more details), an asymptotic convergence proof is derived. 
\begin{comment}
This algorithm has been borrowed and adapted in many works. For instance, in~\cite{CortesRCSL02} a static load balancing (called DASUD) for non negative integer number of divisible loads in arbitrary networks topologies is investigated. The term {\it "static"} stems from the fact that no loads are added or consumed during the load balancing process. The theoretical correctness proofs of the convergence property are given. Some generalizations of the same authors' own work for partially asynchronous discrete load balancing model are presented in~\cite{cedo+cortes+ripoll+al.2007.convergence}. The authors prove that the algorithm's convergence is finite and bounded by the straightforward network's diameter of the global equilibrium threshold in the network. In~\cite{bahi+giersch+makhoul.2008.scalable}, a fault tolerant communication version is addressed to deal with average consensus in wireless sensor networks. The objective is to have all nodes converged to the average of their initial measurements based only on nodes' local information. A slight adaptation is also considered  in~\cite{BahiCG10} for dynamic networks with bounded delays asynchronous diffusion. The dynamical aspect stands at the communication level as links between the network's resources may be intermittent.
\end{comment}
%in  order to  reduce the  execution times. They  can be  applied in
%different scientific  fields from high  performance computation to  micro sensor
%networks.   In a distributed context (i.e. without centralization), 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 computing 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  \emph{ping-pong}  effect,  an  asynchronous
%iterative algorithm  converges to  the uniform load  distribution. This  work has
%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}.
%\FIXME{Rajouter des choses ici.  Lesquelles ?}
Although  Bertsekas  and Tsitsiklis'  describe  the necessary conditions to
ensure the algorithm's convergence,  there is no indication or any strategy to really implement
the load distribution. %In other word, a node  can send some amount of its load to one or   many  of   its  neighbors   while  all   the  convergence   conditions  are followed. 
Consequently,  we propose a  new strategy called  \besteffort{}
that tries to balance the load of  a node to all its less loaded neighbors while
ensuring that all the nodes involved in the load balancing phase have the same
amount of  load.  Moreover, %when real-world asynchronous  applications are considered,
%using  asynchronous load  balancing  algorithms  can  reduce   the  execution
%times. 
most of the times, it is simpler to dissociate load information messages
from  data  migration  messages.  Former  ones  allow  a  node to  inform  its
neighbors about its  current load. These messages are in fact very small and can be sent 
often and very quickly.  For example, if a computing iteration takes  a significant times
(ranging from seconds to minutes), it is possible to send a new load information
message to each involved neighbor at each iteration. Then the load is sent, but the reception may take time when the amount of load is huge and when communication links are slow.  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 more longer that the  time to transfer a load information message. So,
when a node is notified 
%receives the information  
that later it will receive a data message,
it can take this information into account in its load's queue list for preventive purposes.
%and it can consider that its new load is larger.   
Consequently, it can  send a part  of its predictive
%real 
load to some  of its
neighbors if required. We call this trick the \emph{clairvoyant virtual load} transfer mechanism.

\medskip 
The main contributions and novelties of our work are summarized in the following section. 

\section{Our contributions}
\label{contributions}
\begin{itemize}
\item We propose a {\it best effort strategy} which proceeds greedily to achieve efficient local neighborhoods equilibrium. Upon local load imbalance detection, a {\it significant amount} of load is moved from a highly loaded node (initiator) to less loaded neighbors.     

\item Unlike earlier works, we use a new concept of virtual loads transfers which allows nodes to predict the future loads they will receive in the subsequent iterations. 
This leads to a noticeable speedup of the global convergence time of the load balancing process.  

\item We use SimGrid simulator which is known to be able to characterize and modelize realistic models of computation and communication in different types of platforms. We show that taking into account both loads transfers' costs and network contention is essential and has a real impact on the quality of the load balancing performances. 

%\item We improve the straightforward network's diameter bound of the global equilibrium threshold in the network. % not sure, it depends on the remaining time before the paper submission ...
\end{itemize}


%{\bf The contributions of this paper are the following:}
%\begin{itemize}
%\item We propose a new strategy to improve the distribution of the
%load and a simple but efficient trick that also improves the load
%balancing.
%\item we have conducted many simulations with SimGrid in order to
%validate that 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.
%\end{itemize}

The reminder of the paper is organized as follows. 
In Section~\ref{sec.related.works}, we review the relevant approaches in the literature. Section~\ref{sec.bt-algo} describes the
Bertsekas and Tsitsiklis' asynchronous load balancing algorithm. %Moreover, we  present a possible problem in the convergence conditions. 
Section~\ref{sec.besteffort} presents the best effort strategy which provides
efficient local loads equilibrium. %This strategy will be compared with the one presented in Section~\ref{sec.other}.  
In Section~\ref{sec.virtual-load}, the clairvoyant virtual load scheme is proposed to speedup the convergence time of the load balancing process.
We provide in Section~\ref{sec.simulations}, a comprehensive set of numerical results that exhibit the usefulness of our proposals when we deal with realistic models of computation and communication. Finally, we give some concluding remarks in Section~\ref{conclusions-remarks}.


\section{Related works}
\label{sec.related.works}
In this section, we fairly review the relevant techniques proposed in the literature to tackle the problem of load balancing in a general context of distributed systems. 

As pointed above, the most interesting approach to this issue has been proposed by Bertsekas  and Tsitsiklis~\cite{bertsekas+tsitsiklis.1997.parallel}. This algorithm which is outlined in Section~\ref{sec.bt-algo} for the sake of comparison, has been borrowed and adapted in many works. For instance, in~\cite{CortesRCSL02} a static load balancing (called DASUD) for non negative integer number of divisible loads in arbitrary networks topologies is investigated. The term {\it "static"} stems from the fact that no loads are added or consumed during the load balancing process. The theoretical correctness proofs of the convergence property are given. Some generalizations of the same authors' own work for partially asynchronous discrete load balancing model are presented in~\cite{cedo+cortes+ripoll+al.2007.convergence}. The authors prove that the algorithm's convergence is finite and bounded by the straightforward network's diameter of the global equilibrium threshold in the network. In~\cite{bahi+giersch+makhoul.2008.scalable}, a fault tolerant communication version is addressed to deal with average consensus in wireless sensor networks. The objective is to have all nodes converged to the average of their initial measurements based only on nodes' local information. A slight adaptation is also considered  in~\cite{BahiCG10} for dynamic networks with bounded delays asynchronous diffusion. The dynamical aspect stands at the communication level as links between the network's resources may be intermittent.

Cybenko~\cite{Cybenko89} propose a {\it diffusion} approach for hypercube multiprocessor networks. 
The author targets both static and dynamic random models of work distribution. 
The convergence proof is derived based on the {\it eigenstructure} of the 
iteration matrices that arise in load balancing of equal amount of
computational works. A static load balancing for  both synchronous and asynchronous ring networks is addressed in~\cite{GehrkePR99}. The authors assume that at any time step, at most one token (units of load) can be transmitted along any edge of the ring and no tokens are created during the balancing phase. They show that for every initial token distribution, the proposed algorithm converges to the stable equilibrium with tighter linear bounds of time step-complexity.

In order to achieve the load balancing of cloud data centers, a LB technique based on Bayes theorem and Clustering is proposed in~\cite{zhao2016heuristic}. The main idea of this approach is that, the Bayes theorem is combined with the clustering process to obtain the optimal clustering set of physical target hosts leading to the overall load balancing equilibrium.  Bidding is a market-technique for task scheduling and load balancing in distributed systems
that characterize a set of negotiation rules for users' jobs. For instance, Izakian et al~\cite{IzakianAL10} formulate a double auction mechanism for tasks-resources matching in grid computing environments where resources are considered as provider agents and users as consumer ones. Each entity participates in the network independently and makes autonomous decisions. A provider agent determines its bid price based on its current workload, and each consumer agent defines its bid value based on two main parameters: average remaining time and remaining resources for bidding. Based on JADE simulator, the proposed algorithm exhibits better performances in terms of successful execution rates, resource utilization rates and fair profit allocation.


Choi et al.~\cite{ChoiBH09} address the problem of robust task allocation in arbitrary networks. The proposed
approaches combine bidding approach for task selection and consensus procedure scheme for
decentralized conflict resolution. The developed algorithms are proven to converge to a conflict-free assignment in
both single and multiple task assignment problem. An online stochastic dual gradient LB algorithm which is called DGLB is proposed in~\cite{chen2017dglb}. The authors deal with both workload and energy management for cloud networks consisting of multiple geo-distributed mapping nodes and data Centers. To enable online distributed implementation, tasks are decomposed both across time and space by leveraging a dual decomposition approach. Experimental results corroborate the merits of the proposed algorithm.


In~\cite{tripathi2017non} a LB algorithm based on game theory is proposed for distributed data centers. The authors formulate the LB problem as a non-cooperative game among front-end proxy servers and characterize the structure of Nash equilibrium. Based on the obtained Nash equilibrium structure, they derive a LB algorithm for computing the Nash equilibrium. They show through simulations that the proposed algorithm ensures fairness among the users and good average latency across all client regions. A hybrid task scheduling and load balancing dependent and independent tasks for master-slaves platforms is addressed in~In~\cite{liu2017dems}. To minimize the response time of the submitted jobs, the proposed algorithm which is called DeMS is splitted in three stages: i) communication overhead reduction between masters and slaves,  ii) task migration to keep the workload balanced iii) and precedence task graphs partitioning. 


In~\cite{GrosuC05}, the authors formulate the load balancing problem as a non-cooperative game among users. They use the Nash equilibrium as the solution of this game to optimize the response time of all jobs in the entire system. The proposed scheme guarantees the optimal task allocation for each user with low time complexity. A game theoretic approach to tackle the static load balancing problem is also investigated in~\cite{PenmatsaC11}. To provide fairness to all users in  the system, the load balancing problem is formulated as a non-cooperative game among the users to minimize the response time of the submitted users' jobs. As in~\cite{GrosuC05}, the authors use the concept of Nash equilibrium as the solution of a non-cooperative game. Simulations results show that the proposed scheme perform near optimal solutions compared to other existing techniques in terms of fairness.




\section{Bertsekas  and Tsitsiklis' asynchronous load balancing algorithm}
\label{sec.bt-algo}

In this section, we present a brief description of Bertsekas and Tsitsiklis' algorithm~\cite{bertsekas+tsitsiklis.1997.parallel} using its original notations. 
A network is modeled as a connected undirected graph $G=(N,A)$, where $N$ is set 
of processors and $A$ is a set of communication links. The processors are 
labeled $i = 1,...,n$, and a link between processors $i$ and
$j$ is denoted by $(i, j)\in A$. The set of neighbors of processor $i$ is denoted by $V(i)$.
%In this work, we consider that  
Processors are  considered to be homogeneous for the sake of simplicity.
It is easily extendable to the case of heterogeneous platforms 
by scaling the processor's load by its computing power~\cite{ElsMonPre02}.
%In  order  prove  the  convergence  of  asynchronous  iterative  load  balancing
%Bertsekas         and        Tsitsiklis         proposed         a        model
%in~\cite{bertsekas+tsitsiklis.1997.parallel}.   Here we  recall  some notations.
%Consider  that  $N={1,...,n}$  processors   are  connected  through  a  network.
%Communication links  are represented by  a connected undirected  graph $G=(N,A)$
%where $A$ is the set of links connecting different processors. 
%In this work, we
%consider that  processors are  homogeneous for sake  of simplicity. It  is quite
%easy to tackle the  heterogeneous case~\cite{ElsMonPre02}. 
Load of processor $i$
at  time $t$  is  represented  by $x_i(t)\geq  0$.   
%Let $V(i)$  be  the set  of neighbors of processor  $i$.  
Each processor $i$ has an estimate  of the load of
each  of its  neighbors $j  \in V(i)$  denoted by  $x_j^i(t)$ and this estimate 
may be outdated due to %.  According to
asynchronism and communication  delays. 
%, this estimate may be  outdated.  
%We also
%consider that the load is described by a continuous variable.

%Since we deal with large  {\it fine grain} parallelism of divisible loads, 
%the processor's load is represented by a continuous variable for notational 
%convenience.  
\medskip
When a processor  sends a part of its  load to one or some of  its neighbors, the
transfer takes time to be completed.  Let $s_{ij}(t)$ be the amount of load that
processor $i$ has transferred to processor $j$ at time $t$ and let $r_{ij}(t)$ be the
amount of  load received by  $j$  from  $i$ at  time $t$. Then
the amount of load of processor $i$ at time $t+1$ is given by:

\begin{equation}
x_i(t+1)=x_i(t)-\sum_{j\in V(i)} s_{ij}(t) + \sum_{j\in V(i)} r_{ji}(t)
\label{eq.ping-pong}
\end{equation}


%Some  conditions are  required to  ensure the  convergence. One  of them  can be
%called the \emph{ping-pong} condition which specifies that:
\medskip
The asymptotic convergence is derived based on the {\it ping-pong} awareness 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}

for any  processor $i$ and any  $j \in V(i)$ such  that $x_i(t)>x_j^i(t)$.  
%This condition aims  at avoiding a processor  to send a  part of its load  and being
%less loaded after that.

\medskip
This condition prohibits the possibility that two nodes keep sending load to each
other back and forth, without reaching equilibrium. 

\medskip
Nevertheless,  we  think that  this  condition may  lead  to  deadlocks in  some
cases. For example, if we consider a linear chain graph network of only three processors and that processor $1$
is linked to processor $2$ which is  also linked to processor $3$, but processors $1$ and $3$ are not neighbors. 
%(i.e. a simple chain which 3 processors). 
Now consider that we have the following load values at time~$t$:
\begin{align*}
  x_1(t)   &= 10    \\
  x_2(t)   &= 100   \\
  x_3(t)   &= 99.99 \\
  x_3^2(t) &= 99.99 
\end{align*}
%{\bf RAPH, pourquoi il y a $x_3^2$?. Sinon il faudra reformuler la suite, c'est mal dit}

Owing to the algorithm's specification, processor $2$ can either sends 
load to processor $1$ or processor
$3$.  If it sends load to processor $1$, it will not satisfy condition
\eqref{eq.ping-pong} because after that sending it will be less loaded than
$x_3^2(t)$.  So we consider that the \emph{ping-pong} condition is probably too
strong. %Currently, we did not try to make another convergence proof without this condition or with a weaker condition.

\smallskip  
Nevertheless, we conjecture that a weaker condition may exist since we
have never seen any scenario that is not leading to convergence, even with
load-balancing strategies that are not exactly fulfilling the authors' own conditions. %se 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.

\smallskip

Although this approach is interesting, several practical
questions arise when dealing with realistic models of 
computation and communication. As reported above, the 
algorithm's description is too succinct and no details are 
given on what is really sent and how the load balancing decision 
are taken. To our knowledge, the only first attempt for a possible 
implementation of this algorithm is investigated in~\cite{bahi+giersch+makhoul.2008.scalable} under the same conditions. Thus, in order to assess the performances
of the new \besteffort{}, 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, through out the paper, we will
reference it under the original name {\it Bertsekas and Tsitsiklis} for the sake of convenience and readability. 

\smallskip 
Here is an outline of the main principle of the borrowed algorithm.  When a given node  $i$ has to take
a load balancing decision, it starts by sorting its neighbors by non-increasing
order of their loads.  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
$1/(|V(i)|+1)$ of the load difference%, with $n$ being the number of neighbors
. This process is iterated as long as the node is more loaded than the considered
neighbors.


\section{Best effort strategy}
\label{sec.besteffort}

In this section, we describe a new load-balancing strategy that we call
\besteffort{}.  First, we explain the general idea behind this strategy,
and then we present some variants of this basic strategy.

\subsection{Basic strategy}
The description of our algorithm will be given from the point of view a processor~$i$.
The principle of the \besteffort{} 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 the involved neighbors and itself.

More precisely, %when a processor $i$ is in its load-balancing phase,
at each iteration of the load balancing process, processor~$i$ 
 proceeds as follows.
\begin{enumerate}
\item First, the neighbors are sorted in non-decreasing order of their
  known loads $x^i_j(t)$.

\item Then, this sorted list is used to find its largest
  prefix such as the load of each selected neighbor is smaller than:
  \begin{itemize}
  \item the load of processor $i$, and
  \item the mean of the loads of the selected neighbors and processor i.
  \end{itemize}
  Let $S_i(t)$ be the set of the selected neighbors, and
  $\bar{x}(t)$ be the mean of the loads between the selected neighbors and processor $i$ is given as follows:
  \begin{equation*}
    \bar{x}(t) = \frac{1}{\abs{S_i(t)} + 1}
      \left( x_i(t) + \sum_{j\in S_i(t)} x^i_j(t) \right)
  \end{equation*}
  so that the following properties hold: %{\bf RAPH : la suite tombe du ciel :-)}
  \begin{equation*}
    \begin{cases}
      S_i(t) \subset V(i) \\
      x^i_j(t) < x_i(t) & \forall j \in S_i(t) \\
      x^i_j(t) < \bar{x} & \forall j \in S_i(t) \\
      x^i_j(t) \leq x^i_k(t) & \forall j \in S_i(t), \forall k \in V(i) \setminus S_i(t) \\
      \bar{x} \leq x_i(t)
    \end{cases}
  \end{equation*}

\item Once this selection is done, processor $i$ sends to each selected neighbor $j\in S_i(t)$ an amount of load $s_{ij}(t) =
  \bar{x} - x^i_j(t)$.

  %From the above equations, and notably from the definition of $\bar{x}$, it can easily be verified that:
  
  \smallskip
  In this way we obtain: 
  
  \begin{equation*}
    \begin{cases}
      x_i(t) - \sum_{j\in S_i(t)} s_{ij}(t) = \bar{x} \\
      x^i_j(t) + s_{ij}(t) = \bar{x} & \forall j \in S_i(t)
    \end{cases}
  \end{equation*}
\end{enumerate}



\subsection{Leveling the amount of load to move}

With the aforementioned basic strategy, each node does its best to reach the
equilibrium with its neighbors. However, one question should be outlined here:
How can we handle the case where two (or more) node initiators that may send 
concurrently some amount of loads to the the same less loaded neighbor? Indeed,  
%since each node may take the same kind of decision at the same time, 
there is a risk that a node will receive load from
several of its neighbors, and then is temporary going off the equilibrium state.
This is particularly true with strongly connected applications.



In order to reduce this effect, we add the ability to level the amount of load to send.
The idea, here, is to make as few steps as possible toward the equilibrium, such that a
potentially unsuitable decision pointed above has a lower impact on the local equilibrium.
Roughtly speaking, once $s_{ij}$ is estimated as previously explained, it is simply weighted by
a given prescribed threshold parameter which we call 
%.  This parameter is called 
$k$ in
Section~\ref{sec.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{sec.results}]{}



%\section{Other strategies}
%\label{sec.other}

%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 \besteffort{}, 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 naïve implementation of Bertsekas' load balancing algorithm.  {\bf : RAPH j'ai renommé MAKHOUL en naive, il faut valider !!!! LE SOUCI, il faudrait refaire les figures}

%Here is an outline of the \makhoul{} 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.


\section{Virtual load}
\label{sec.virtual-load}

In this section,  we present the new concept of \emph{virtual load} which aims to improve the global convergence time. For this end, both load transfer messages and load information messages are dissociated. 
%In order to
%use this concept, load balancing messages must be sent using two different kinds
%of  messages:  load information  messages  and  load  balancing messages.   
More
precisely, a node wanting to send some amount of its load to one (or more) of its neighbors
can first send a load information message about the load it will send, and
later it can send the load  message containing data to be transferred.
Load information  messages are in fact short
%, consequently they 
and will be received soon.
%very quickly.  
In contrast, load  transfer messages are often larger ones and thus
require more time to be transferred.

The  concept  of  \emph{virtual load}  allows  a  node receiving a  load
information message to integrate (virtually) the future load it will receive later in its load's list
 even if the load has not been received yet. Consequently, the notified node can send  a (real)  part of  its load  to some  of its
neighbors when needed. By and large, this allows a node on the one hand, to predict the load it will receive in the subsequent time steps, and on the other hand, to take suitable decisions when detecting load imbalance in its closed neighborhoods. Doing so, we expect faster convergence time since nodes can take 
into account the information about the predictive loads not 
received yet.

% repetition !
%In fact,  a node that receives a load  information message knows that
%later it  will receive the  corresponding load balancing message  containing the
%corresponding data.  So, if this node detects it is too  loaded compared to some
%of its neighbors  and if it has enough  load (real load), then it  can send more
%load  to  some of  its  neighbors  without waiting  the  reception  of the  load
%balancing message.

%Doing  this, we  can  expect a  faster  convergence since  nodes  have a  faster
%information of the load they will receive, so they can take it into account.

%\FIXME{Est ce qu'on donne l'algo avec virtual load?}

%With integer load, this algorithm has been adapted by rounding the load value. In fact, we consider that the total amount of load is big enough and that it can be split with integer numbers.

\medskip 
{\bf ****** je suis arrivé ici ******** la conclusion est déjà écrite ******}
{\bf ****** ça serait, peut être, mieux de déplacer la section de "Threads manangement" ici dans cette section  et ll'appler éventuellement "Computation (ou Load) and message passing threads management" ******}
\medskip


%\FIXME{describe integer mode}

\section{Simulations}
\label{sec.simulations}

In order to test and validate our approaches, we wrote a simulator
using the SimGrid
framework~\cite{simgrid.web,casanova+giersch+legrand+al.2014.simgrid}.  This
simulator, which consists of about 2,700 lines of C++, allows to run
the different load-balancing strategies under various parameters, such
as the initial distribution of load, the interconnection topology, the
characteristics of the running platform, etc.  Then several metrics
are issued that permit to compare the strategies.

The simulation model is detailed in the next section (\ref{sec.model}), and the
experimental contexts are described in section~\ref{sec.exp-context}.  Then the
results of the simulations are presented in section~\ref{sec.results}.

\subsection{Simulation model}
\label{sec.model}

In the simulation model the processors exchange messages which are of
two kinds.  First, there are \emph{control messages} which only carry
information that is exchanged between the processors, such as the
current load, or the virtual load transfers if this option is
selected.  These messages are rather small, and their size is
constant.  Then, there are \emph{data messages} that carry the real
load transferred between the processors.  The size of a data message
is a function of the amount of load that it carries, and it can be
pretty large.  In order to receive the messages, each processor has
two receiving channels, one for each kind of messages.  Finally, when
a message is sent or received, this is done by using the non-blocking
primitives of SimGrid\footnote{That are \texttt{MSG\_task\_isend()},
  and \texttt{MSG\_task\_irecv()}.}.

During the simulation, each processor concurrently runs three threads:
a \emph{receiving thread}, a \emph{computing thread}, and a
\emph{load-balancing thread}, which we will briefly describe now.

For the sake of simplicity, a few details were voluntary omitted from
these descriptions.  For an exhaustive presentation, we refer to the
actual source code that was used for the experiments%
\footnote{As mentioned before, our simulator relies on the SimGrid
  framework~\cite{casanova+giersch+legrand+al.2014.simgrid}.  For the
  experiments, we used a pre-release of SimGrid 3.7 (Git commit
  67d62fca5bdee96f590c942b50021cdde5ce0c07, available from
  \url{https://gforge.inria.fr/scm/?group_id=12})}, and which is
available at
\url{http://info.iut-bm.univ-fcomte.fr/staff/giersch/software/loba.tar.gz}.

\subsubsection{Receiving thread}

The receiving thread is in charge of waiting for messages to come, either on the
control channel, or on the data channel.  Its behavior is sketched by
Algorithm~\ref{algo.recv}.  When a message is received, it is pushed in a buffer
of received message, to be later consumed by one of the other threads.  There
are two such buffers, one for the control messages, and one for the data
messages.  The buffers are implemented with a lock-free FIFO
\cite{sutter.2008.writing} to avoid contention between the threads.

\begin{algorithm}
  \caption{Receiving thread}
  \label{algo.recv}
  \KwData{
    \begin{algodata}
      \VAR{ctrl\_chan}, \VAR{data\_chan}
      & communication channels (control and data) \\
      \VAR{ctrl\_fifo}, \VAR{data\_fifo}
      & buffers of received messages (control and data) \\
    \end{algodata}}
  \While{true}{%
    wait for a message to be available on either \VAR{ctrl\_chan},
    or \VAR{data\_chan}\;
    \If{a message is available on \VAR{ctrl\_chan}}{%
      get the message from \VAR{ctrl\_chan}, and push it into \VAR{ctrl\_fifo}\;
    }
    \If{a message is available on \VAR{data\_chan}}{%
      get the message from \VAR{data\_chan}, and push it into \VAR{data\_fifo}\;
    }
  }
\end{algorithm}

\subsubsection{Computing thread}

The computing thread is in charge of the real load management.  As exposed in
Algorithm~\ref{algo.comp}, it iteratively runs the following operations:
\begin{itemize}
\item if some load was received from the neighbors, get it;
\item if there is some load to send to the neighbors, send it;
\item run some computations, whose duration is function of the current
  load of the processor.
\end{itemize}
Practically, after the computation, the computing thread waits for a
small amount of time if the iterations are looping too fast (for
example, when the current load is near zero).

\begin{algorithm}
  \caption{Computing thread}
  \label{algo.comp}
  \KwData{
    \begin{algodata}
      \VAR{data\_fifo} & buffer of received data messages \\
      \VAR{real\_load} & current load \\
    \end{algodata}}
  \While{true}{%
    \If{\VAR{data\_fifo} is empty and $\VAR{real\_load} = 0$}{%
      wait until a message is pushed into \VAR{data\_fifo}\;
    }
    \While{\VAR{data\_fifo} is not empty}{%
      pop a message from \VAR{data\_fifo}\;
      get the load embedded in the message, and add it to \VAR{real\_load}\;
    }
    \ForEach{neighbor $n$}{%
      \If{there is some amount of load $a$ to send to $n$}{%
        send $a$ units of load to $n$, and subtract it from \VAR{real\_load}\;
      }
    }
    \If{$\VAR{real\_load} > 0.0$}{
      simulate some computation, whose duration is function of \VAR{real\_load}\;
      ensure that the main loop does not iterate too fast\;
    }
  }
\end{algorithm}

\subsubsection{Load-balancing thread}

The load-balancing thread is in charge of running the load-balancing algorithm,
and exchange the control messages.  As shown in Algorithm~\ref{algo.lb}, it
iteratively runs the following operations:
\begin{itemize}
\item get the control messages that were received from the neighbors;
\item run the load-balancing algorithm;
\item send control messages to the neighbors, to inform them of the
  processor's current load, and possibly of virtual load transfers;
\item wait a minimum (configurable) amount of time, to avoid to
  iterate too fast.
\end{itemize}

\begin{algorithm}
  \caption{Load-balancing}
  \label{algo.lb}
  \While{true}{%
    \While{\VAR{ctrl\_fifo} is not empty}{%
      pop a message from \VAR{ctrl\_fifo}\;
      identify the sender of the message,
      and update the current knowledge of its load\;
    }
    run the load-balancing algorithm to make the decision about load transfers\;
    \ForEach{neighbor $n$}{%
      send a control messages to $n$\;
    }
    ensure that the main loop does not iterate too fast\;
  }
\end{algorithm}

%\paragraph{}\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{sec.virtual-load}}

\subsection{Experimental contexts}
\label{sec.exp-context}

In order to assess the performances of our algorithms, simulations with various parameters have been achieved out, and several metrics are described in this section.

\subsubsection{Load balancing strategies}

Several load balancing strategies were compared.  Experiments with
the \besteffort{}, and with the \makhoul{} strategies have been performed.  \emph{Best
  effort} was tested with parameter $k = 1$, $k = 2$, and $k = 4$.  Secondly,
each strategy was run in its two variants: with, and without the management of
\emph{virtual load}.  Finally, each configuration with \emph{real},
and with \emph{integer} load is considered.

To summarize the different load balancing strategies, we have:
\begin{description}
\item[\textbf{strategies:}] \makhoul{}, or \besteffort{} with $k\in
  \{1,2,4\}$
\item[\textbf{variants:}] with, or without virtual load
\item[\textbf{domain:}] real load, or integer load
\end{description}
%
This gives us as many as $4\times 2\times 2 = 16$ different strategies.

\subsubsection{End of the simulation}

The simulations were run until the load was nearly balanced among the
participating nodes.  More precisely the simulation stops when each node holds
an amount of load at less than 1\% of the load average, during an arbitrary
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 is obviously not realistic.  In a
real application we would have chosen a decentralized convergence detection
algorithm, like the one described in \cite{ccl09:ij}.

\subsubsection{Platforms}

In order to show the behavior of the different strategies in different
settings, we simulated the executions on two sorts of platforms.  These two
sorts of platforms differ by their 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 are 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 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 each kind of platform with four different numbers of computing
nodes: 16, 64, 256, and 1024 nodes is built in a similar way.

\subsubsection{Configurations}

The distributed processes of the application were then logically organized along
three possible topologies: a line, a torus or an hypercube.  Tests were performed with the total load initially on only one node (at one end for the line topology).
Other tests for which the load was initially randomly distributed across all the
participating nodes are also considered.  The total amount of load was fixed to a number of load
units equal to 1000 times the number of node.  The average load is then of 1000
load units.

For all the previous configurations, the
computation and communication costs of a load unit are defined.  We chose them, such as to
have three different computation over communication cost ratios, and hence model
three different kinds of applications:
\begin{itemize}
\item mainly communicating, with a computation/communication cost ratio of $1/10$;
\item mainly computing, with a computation/communication cost ratio of $10/1$ ;
\item balanced, with a computation/communication cost ratio of $1/1$.
\end{itemize}

To summarize the various configurations, we have:
\begin{description}
\item[\textbf{platforms:}] homogeneous (cluster), or heterogeneous (subset of
  Grid'5000)
\item[\textbf{platform sizes:}] platforms with 16, 64, 256, or 1024 nodes
\item[\textbf{process topologies:}] line, torus, or hypercube
\item[\textbf{initial load distribution:}] initially on a only node, or
  initially randomly distributed over all nodes
\item[\textbf{computation/communication cost ratio:}] $10/1$, $1/1$, or $1/10$
\end{description}
%
This gives us as many as $2\times 4\times 3\times 2\times 3 = 144$ different
configurations.
%
Combined with the various load balancing strategies,  $16\times 144 =
2,304$ distinct settings have been evaluated.  In fact, as it will be shown later, only configations with a maximum number of 1,024 nodes are considered in order to limit the time of experiments.


\subsubsection{Metrics}
\label{sec.metrics}

In order to evaluate and compare the different load balancing strategies we had
to define several metrics.  Our goal, when choosing these metrics, was to have
something tending to a constant value, i.e. to have a measure which is not
changing anymore once the convergence state is reached.  Moreover, we wanted to
have some normalized value, in order to be able to compare them across different
settings.

With these constraints in mind, we define the following metrics:
%
\begin{description}
\item[\textbf{average idle time:}] that is the total time spent, when the nodes
  do not hold any share of load, and thus have nothing to compute.  This total
  time is divided by the number of participating nodes, such as to have a number
  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.  A smaller value is better.

\item[\textbf{average convergence date:}] that is the average of the dates when
  all nodes reached the convergence state.  The dates are measured as a number
  of (simulated) seconds since the beginning of the simulation.

\item[\textbf{maximum convergence date:}] that is the date when the last node
  reached the convergence state.

  These two dates give an idea of the time needed by the strategy to reach the
  equilibrium state.  A smaller value is better.

\item[\textbf{data transfer amount:}] that is 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.  Again, a smaller value is better.

\end{description}


\subsection{Experimental results}
\label{sec.results}

In this section, the results for the different simulations are presented,
and our observations are explained.

\subsubsection{Cluster versus grid platforms}

As mentioned earlier, different algorithms have been simulated on two kinds of
physical platforms: clusters and grids.  A first observation,
is that the graphs we draw from the data have a similar aspect for the two kinds
of platforms.  The only noticeable difference is that the algorithms need a bit
more time to achieve the convergence on the grid platforms, than on clusters.
Nevertheless their relative performances remain generally similar.

This suggests that the relative performances of the different strategies are not
influenced by the characteristics of the physical platform.  The differences in
the convergence times can be explained by the fact that on the grid platforms,
distant sites are interconnected by links of smaller bandwidth.

Therefore, in the following, we only discuss the results for the grid
platforms.

\subsubsection{Main results}

\begin{figure*}[p]
  \centering
  \includegraphics[width=.5\linewidth]{data/graphs/R1-10:1-grid-line}%
  \includegraphics[width=.5\linewidth]{data/graphs/R1-1:10-grid-line}
  \includegraphics[width=.5\linewidth]{data/graphs/R1-10:1-grid-torus}%
  \includegraphics[width=.5\linewidth]{data/graphs/R1-1:10-grid-torus}
  \includegraphics[width=.5\linewidth]{data/graphs/R1-10:1-grid-hcube}%
  \includegraphics[width=.5\linewidth]{data/graphs/R1-1:10-grid-hcube}
  \caption{Real mode, initially on an only mode, comp/comm cost ratio = $10/1$ (left), or $1/10$ (right).}
  \label{fig.results1}
\end{figure*}

\begin{figure*}[p]
  \centering
  \includegraphics[width=.5\linewidth]{data/graphs/RN-10:1-grid-line}%
  \includegraphics[width=.5\linewidth]{data/graphs/RN-1:10-grid-line}
  \includegraphics[width=.5\linewidth]{data/graphs/RN-10:1-grid-torus}%
  \includegraphics[width=.5\linewidth]{data/graphs/RN-1:10-grid-torus}
  \includegraphics[width=.5\linewidth]{data/graphs/RN-10:1-grid-hcube}%
  \includegraphics[width=.5\linewidth]{data/graphs/RN-1:10-grid-hcube}
  \caption{Real mode, random initial distribution, comp/comm cost ratio = $10/1$ (left), or $1/10$ (right).}
  \label{fig.resultsN}
\end{figure*}

The main results for our simulations on grid platforms are presented on Figures~\ref{fig.results1} and~\ref{fig.resultsN}.
%
The results on Figure~\ref{fig.results1} are when the load to balance is
initially on an only node, while the results on Figure~\ref{fig.resultsN} are
when the load to balance is initially randomly distributed over all nodes.

On both figures, the computation/communication cost ratio is $10/1$ on the left
column, and $1/10$ on the right column.  With a computation/communication cost
ratio of $1/1$ the results are just between these two extrema, and definitely
don not give additional information, so we chose not to show them here.

On each of the figures~\ref{fig.results1} and~\ref{fig.resultsN}, the results
are given for the process topology being, from top to bottom, a line, a torus or
an hypercube.

Finally, on the graphs, the vertical bars show the measured times for each of
the algorithms.  These measured times are, from bottom to top, the average idle
time, the average convergence date, and the maximum convergence date (see
Section~\ref{sec.metrics}).  The measurements are repeated for the different
platform sizes.  Some bars are missing, specially for large platforms.  This is
either because the algorithm did not reach the convergence state in the
allocated time, or because we simply decided not to run it.

\FIXME{annoncer le plan de la suite}

\subsubsection{The \besteffort{} and  \makhoul{} strategies without virtual load}

Before looking  at the different variations,  we will first show  that the plain
\besteffort{}  strategy  is valuable,  and  may be  as  good  as the  \makhoul{}
strategy.  On  Figures~\ref{fig.results1} and~\ref{fig.resultsN},
these strategies are respectively labeled ``b'' and ``a''.

We  can  see  that  the  relative  performance of  these  strategies  is  mainly
influenced by  the application topology.  It  is for the line  topology that the
difference is the  more important.  In this case,  the \besteffort{} strategy is
nearly faster than the \makhoul{} strategy.  This can  be explained by the
fact that the \besteffort{} strategy tries to distribute the load fairly between
all the nodes  and with the line topology,  it is easy to load  balance the load
fairly.

On the contrary, for the hypercube topology, the \besteffort{} strategy performs
worse than the \makhoul{} strategy. In this case, the \makhoul{} strategy which
tries to give more load to few neighbors reaches the equilibrium faster.

For the torus  topology, for which the  number of links is between  the line and
the hypercube, the \makhoul{} strategy  is slightly better but the difference is
more nuanced when the initial load is  only on one node. The only case where the
\makhoul{} strategy is really faster than the \besteffort{} strategy is with the
random initial distribution when the communication are slow.

Globally   the  number  of   interconnection  is   very  important.    The  more
the interconnection links are, the  faster the \makhoul{} strategy is because
it distributes quickly significant amount of load, even if this is unfair, between
all the  neighbors.  In opposition,  the \besteffort{} strategy  distributes the
load fairly so this strategy is better for low connected strategy.


\subsubsection{Virtual load}

The influence of virtual load is most of the time really significant compared to
the  same configuration  without  it. Sometimes  it  has no  effect  but, based on our observations,  it has never a negative effect on the load balancing we tested.

On Figure~\ref{fig.results1}, when the load is  initially on one node, it can be
noticed that the  average idle times are generally longer  with the virtual load
than without  it. This  can be explained  by the  fact that, with  virtual load,
processors  will exchange all  the load  they need  to exchange  as soon  as the
virtual load has been balanced  between all the processors. So consequently they
cannot  compute  at  the  beginning.  This is  especially  noticeable  when  the
communication are slow (on the left part of Figure ~\ref{fig.results1}.

%Dans ce cas  légère amélioration de la cvg. max.  Temps  moyen de cvg. amélioré,
%mais plus de temps passé en idle, surtout quand les comms coutent cher.

%\subsubsection{The \besteffort{} strategy with an initial random load
%  distribution, and larger platforms}

%In 
%Mêmes conclusions pour line et hcube.
%Sur tore, BE se fait exploser quand les comms coutent cher.

%\FIXME{virer les 1024 ?}

%\subsubsection{With the virtual load extension with an initial random load
%  distribution}

%Soit c'est équivalent, soit on gagne -> surtout quand les comms coutent cher et
%qu'il y a beaucoup de voisins.

\subsubsection{The $k$ parameter}
\label{results-k}

As  explained  previously when  the  communication  are  slow the  \besteffort{}
strategy is efficient. This is due to the fact that it tries to balance the load
fairly and consequently  a significant amount of the  load is transfered between
processors.  In this situation, it is possible to reduce the convergence time by
using  the leveler  parameter  (parameter  $k$).  The  advantage  of using  this
solution is particularly efficient when the initial load is randomly distributed
on  the nodes with  torus and  hypercube topologies  and slow  communication. When
virtual load  mechanism is used,  the effect of  this parameter is  also visible
with the same condition.



\subsubsection{With integer load}

We also performed  some experiments with integer load instead  of load with real
value.  In  this case, the  results have globally  the same behavior.   The most
interesting  result, from  our point  of view,  is that  the virtual  mode allows
processors in a line topology to converge to the uniform load balancing. Without
the virtual  load, most  of the time,  processors converge  to what we  call the
``stairway effect'', that  is to say that  there is only a difference  of one in
the load of each processor and its neighbors (for example with 10 processors, we
obtain 10 9 8 7 6 6 7 8 9 10 instead of 8 8 8 8 8 8 8 8 8 8).

%Cas normal, ligne -> converge pas (effet d'escalier).
%Avec vload, ça converge.

%Dans les autres cas, résultats similaires au cas réel: redire que vload est
%intéressant.

\FIXME{ajouter une courbe avec l'équilibrage en entier}

\FIXME{virer la metrique volume de comms}

\FIXME{ajouter une courbe ou on voit l'évolution de la charge en fonction du
  temps : avec et sans vload}

% \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}}

% On veut montrer quoi ? :

% 1) best plus rapide que les autres (simple, makhoul)
% 2) avantage virtual load

% Est ce qu'on peut trouver des contre exemple?
% Topologies variées


% 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

% Cadre processeurs homogènes

% Topologies statiques

% On ne tient pas compte de la vitesse des liens donc on la considère homogène

% Prendre un réseau hétérogène et rendre processeur homogène

% Taille : 10 100 très gros

\section{Conclusion}
\label{conclusions-remarks}

In this paper, we have presented a new asynchronous load balancing algorithm for non negative real numbers
of divisible loads in distributed systems. The proposed algorithm which is called {\it best effort strategy} 
seeks greedily for loads imbalance detection and tries to achieve efficient local equilibrium threshold 
between neighbors. Our proposal is based on {\it a clairvoyant virtual loads' transfer} scheme which allows nodes to predict the future loads they will receive in the subsequent iterations. 
This leads to a noticeable speedup of the global convergence time of the load balancing process. 
Based on SimGrid simulator, we have demonstrated that, when we deal with realistic models of computation and communication, our algorithm exhibits better performances than its direct competitors from the literature. This makes it a viable choice for load balancing of both non negative real and integer divisible loads in distributed computing systems. % un peu gonflé peut être pour la dernière phrase.

\section*{Acknowledgments}

Computations have been performed on the supercomputer facilities of the
Mésocentre de calcul de Franche-Comté.

\bibliographystyle{elsarticle-num}
\bibliography{biblio}
\FIXME{find and add more references}

\end{document}

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% LocalWords:  Raphaël Couturier Arnaud Giersch Franche ij Bertsekas Tsitsiklis
% LocalWords:  SimGrid DASUD Comté asynchronism ji ik isend irecv Cortés et al
% LocalWords:  chan ctrl fifo Makhoul GFlop xml pre FEMTO Makhoul's fca bdee
% LocalWords:  cdde Contassot Vivier underlaid du de Maréchal Juin cedex calcul
% LocalWords:  biblio Institut UMR Université UFC Centre Scientifique CNRS des
% LocalWords:  École Nationale Supérieure Mécanique Microtechniques ENSMM UTBM
% LocalWords:  Technologie Bahi