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\section{Related Work}
\label{sec:relwork}

A significant amount of research on dynamic resource management (DRM)
appears in the literature, spanning several orthogonal dimensions.
For example, DRM capabilities have been applied to specific layers,
such as the application layer~\cite{moreira97dynamic} or network
layer~\cite{Das:2005}.  DRM capabilities have also been applied either
in standalone~\cite{Banga:99} or distributed~\cite{Welch:98}
deployments.  In this section we summarize related DRE research along
the two dimensions.

\iffalse
% Andy, as long as we cite this work earlier let's omit it here!
% OK, I am not sure if we do this in intro or somewhere - andy
The precursor to the multi-layered resource management (MLRM) work in
this paper was described in~\cite{Cross:01}.  That paper described a
pattern language for reactive and proactive resource allocation for
defense applications.  These patterns and principles are at the heart
of the MLRM work described in our work.
\fi

Application-driven distributed DRM was described
in~\cite{moreira97dynamic}, where the authors leverage the
reconfigurability of their parallel applications to dynamically change
the number of executing tasks and allocate resources based on changes
in resource availability.  This work deals with systems in which the
arrival of jobs is unpredictable and highly variable, which is similar
to the TSCE scenarios that are the focus of this paper.  This work,
however, is application-specific and provides a point solution that is
not based on standard middleware platforms.  In contrast, our ARMS
MLRM work provides standards-based reusable middleware that are
designed to support a broad class of enterprise DRE systems that can
benefit from DRM.

The work described by~\cite{kyhcm:01:coots} presents an integrated
architecture for automatic configuration of component-based
applications and DRM for distributed heterogeneous environments.  The
authors, however, do not describe how end-to-end QoS capabilities are
maintained for mission-critical applications.  In contrast, our work
provides the results of empirical benchmarks that evaluate how well
our MLRM supports end-to-end QoS.

Early work on middleware-based DRM solutions in the context of DRE
shipboard computing systems is described in~\cite{Welch:98}, where the
authors describe the DeSiDeRaTa middleware that provides DRM for
QoS-sensitive applications.  This work and related efforts
in~\cite{shirazi99dynbench} describe the characteristics necessary for
DRM and provide the conceptual foundations for much of the ARMS MLRM
efforts.  Our work on MLRM extends this earlier work to support more
finer grained DRE capabilities that ties mission needs to the
management of physical resources, while also allowing for capability
upgrades.

Network layer DRM capabilities conducted in the DARPA ARMS program are
described in~\cite{Das:2005}.  The network layer DRM solution in that
paper focus on realizing DRM capabilities for one layer (the network
layer) and for a specific resource (bandwidth).  The MLRM approach
described in this paper extends the network layer DRM capabilities to
manage multiple resources, including CPU and network bandwidth,
thereby providing a more comprehensive multi-layered and end-to-end
solution.

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