-
\documentclass[smallextended]{svjour3}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{mathptmx}
+\usepackage{amsmath}
+\usepackage{courier}
\usepackage{graphicx}
+\newcommand{\abs}[1]{\lvert#1\rvert} % \abs{x} -> |x|
+
\begin{document}
-\title{Best effort strategy and virtual load for asynchronous iterative load balancing}
+\title{Best effort strategy and virtual load
+ for asynchronous iterative load balancing}
\author{Raphaël Couturier \and
Arnaud Giersch \and
Abderrahmane Sider
}
-\institute{F. Author \at
- first address \\
- Tel.: +123-45-678910\\
- Fax: +123-45-678910\\
- \email{fauthor@example.com} % \\
-% \emph{Present address:} of F. Author % if needed
+\institute{R. Couturier \and A. Giersch \at
+ LIFC, University of Franche-Comté, Belfort, France \\
+ % Tel.: +123-45-678910\\
+ % Fax: +123-45-678910\\
+ \email{%
+ raphael.couturier@univ-fcomte.fr,
+ arnaud.giersch@univ-fcomte.fr}
\and
- S. Author \at
- second address
+ A. Sider \at
+ University of Béjaïa, Béjaïa, Algeria \\
+ \email{ar.sider@univ-bejaia.dz}
}
\maketitle
\begin{abstract}
Most of the time, asynchronous load balancing algorithms have extensively been
-studied in a theoretical point of view. The Bertsekas' algorithm 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 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 information message to
-integrate the load that it will receive latter 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 based on SimGrid which
-allowed us to conduct many experiments.
+studied in a theoretical point of view. The Bertsekas and Tsitsiklis'
+algorithm~\cite[section~7.4]{bertsekas+tsitsiklis.1997.parallel}
+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 \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 \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
+based on SimGrid which allowed us to conduct many experiments.
\end{abstract}
+\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 \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}.
+
+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 \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,
+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 more 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 \emph{virtual load} mechanism.
+
+
+
+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
+balancing. 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 mechanism 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.
+
+
+
+
+\section{Bertsekas and Tsitsiklis' asynchronous load balancing algorithm}
+\label{BT algo}
+
+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 $G=(N,V)$
+where $V$ 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 $j \in V(i)$ represented by $x_j^i(t)$. According to
+asynchronism and communication delays, this estimate may be outdated. We also
+consider that the load is described by a continuous variable.
+
+When a processor send 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 processor $j$ from processor $i$ 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:
+\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.
+
+Nevertheless, we think that this condition may lead to deadlocks in some
+cases. For example, if we consider only three processors and that processor $1$
+is linked to processor $2$ which is also linked to processor $3$ (i.e. a simple
+chain which 3 processors). Now consider we have the following values at time $t$:
+\begin{eqnarray*}
+x_1(t)=10 \\
+x_2(t)=100 \\
+x_3(t)=99.99\\
+ x_3^2(t)=99.99\\
+\end{eqnarray*}
+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 \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.
+
+
+\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
+best to reach the equilibrium between those neighbors and himself.
+
+More precisely, when a processors $i$ is in its load-balancing phase,
+he proceeds as following.
+\begin{enumerate}
+\item First, the neighbors are sorted in non-decreasing order of their
+ known loads $x^i_j(t)$.
+
+\item Then, this sorted list is traversed in order to find its largest
+ prefix such as the load of each selected neighbor is lesser than:
+ \begin{itemize}
+ \item the processor's own load, and
+ \item the mean of the loads of the selected neighbors and of the
+ processor's load.
+ \end{itemize}
+ Let's call $S_i(t)$ the set of the selected neighbors, and
+ $\bar{x}(t)$ the mean of the loads of the selected neighbors and of
+ the processor load:
+ \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*}
+ The following properties hold:
+ \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 completed, processor $i$ sends to each of
+ the 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:
+ \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}
+
+\section{Other strategies}
+\label{Other}
+
+\textbf{Question} faut-il décrire les stratégies makhoul et simple ?
+
+\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.
+
+On envoie donc (avec "self" pour soi-même) :
+\[
+ \min\left(\frac{load(self) - load(vmin)}{2}, load(self) - load(vmax)\right)
+\]
+
+\paragraph{makhoul} Ordonne les voisins du moins chargé au plus chargé
+puis calcule les différences de charge entre soi-même et chacun des
+voisins.
+
+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.
+
+C'est l'algorithme~2 dans~\cite{bahi+giersch+makhoul.2008.scalable}.
+\section{Virtual load}
+\label{Virtual load}
+\section{Simulations}
+\label{Simulations}
-qsdqsd
+In order to test and validate our approaches, we wrote a simulator
+using the SimGrid
+framework~\cite{casanova+legrand+quinson.2008.simgrid}. The process
+model is detailed in the next section (\ref{Sim model}), then the
+results of the simulations are presented in section~\ref{Results}.
+\subsection{Simulation model}
+\label{Sim model}
+\begin{verbatim}
+Communications
+==============
+There are two receiving channels per host: control for information
+messages, and data for load transfers.
+
+Process model
+=============
+
+Each process is made of 3 threads: a receiver thread, a computing
+thread, and a load-balancer thread.
+
+* Receiver thread
+ ---------------
+
+ Loop
+ | wait for a message to come, either on data channel, or on ctrl channel
+ | push received message in a buffer of received messages
+ | -> ctrl messages on the one side
+ | -> data messages on the other side
+ +-
+
+ The loop terminates when a "finalize" message is received on each
+ channel.
+
+* Computing thread
+ ----------------
+
+ Loop
+ | if we received some real load, get it (data messages)
+ | if there is some real load to send, send it
+ | if we own some load, simulate some computing on it
+ | sleep a bit if we are looping too fast
+ +-
+ send CLOSE on data for all neighbors
+ wait for CLOSE on data from all neighbors
+
+ The loop terminates when process::still_running() returns false.
+ (read the source for full details...)
+
+* Load-balancing thread
+ ---------------------
+
+ Loop
+ | call load-balancing algorithm
+ | send ctrl messages
+ | sleep (min_lb_iter_duration)
+ | receive ctrl messages
+ +-
+ send CLOSE on ctrl for all neighbors
+ wait for CLOSE on ctrl from all neighbors
+
+ The loop terminates when process::still_running() returns false.
+ (read the source for full details...)
+\end{verbatim}
+
+\subsection{Validation of our approaches}
+\label{Results}
+
+
+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 and perspectives}
+
+
+\bibliographystyle{spmpsci}
+\bibliography{biblio}
\end{document}
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% ispell-local-dictionary: "american"
+%%% End:
+
+% LocalWords: Raphaël Couturier Arnaud Giersch Abderrahmane Sider Franche ij
+% LocalWords: Bertsekas Tsitsiklis SimGrid DASUD Comté Béjaïa asynchronism ji
+% LocalWords: ik
