% $Id: conclusion.tex 3601 2006-07-24 19:35:41Z jai $

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\section{Concluding Remarks}
\label{sec:conclu}
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This paper described a standards-based, multi-layered resource
management (MLRM) architecture developed in the DARPA ARMS program to
support dynamic resource management (DRM) in enterprise distributed
real-time and embedded (DRE) systems.  The ARMS MLRM is designed to
enable enterprise DRE systems to adapt to dynamically changing
conditions ({\em e.g.}, during a tactical engagement) for the purpose
of always utilizing the available computers and networks to the
highest degree possible in support of mission needs under various
operating conditions.  The lessons learned while developing MLRM and
applying it to a shipboard computing environment include:

\vspace {-0.1in}
\begin{itemize}\cramplist
\item ARMS MLRM research and experiments shows that dynamic
resource management (DRM) -- using standards-based middleware technologies --
is not only feasible, but can (1) handle dynamic resource allocations
across a wide array of configurations and capacities, (2) provide continuous
availability for critical functionalities -- even in the presence of node
and pool failures --  through reconfiguration and redeployment and
(3) provide QoS for critical operational strings even in the conditions
of overload and resource constrained environments.

\item Enterprise DRE systems that are provisioned {\em statically}
require a great deal of manual engineering effort to create and
validate any (re)configuration and (re)allocation.  This tedious and
error-prone manual process marginalizes any advantage that might be
gained by leveraging allocation and configuration as a means of
application resilency.  The DRM capabilities provided by ARMS MLRM
help alleviate much of this inflexibility by consistently achieving
well-balanced allocation under varying conditions, and permiting a
range of automated, dynamic management of resources that greatly
extend operational flexibility of the system without human
intervention.  Moreover, our experiments clearly demonstrate that the
performance of ARMS MLRM is enhanced when DRM services are enabled --
and in fact allow MLRM to operate in the presence of failures that can
not be accommodated by static resource management.

\item Enterprise DRE systems have a significant number
of software architecture components that require QoS, configuration,
and deployment information.  Although ARMS MLRM provides an effective
software infrastructure for DRM, it is a complex task to capture the
required application information for all components.  To deal with
this complexity, we are exploring model-driven development (MDD)
tools~\cite{Schmidt:04z} that enable a software infrastructure
consisting of standards-based middleware and components so that MLRM
functions and workflow can be performed more effectively and
productively.
\end{itemize}

\iffalse
At this point of its development, MLRM has focused primarily on the
functional architecture and software infrastructure needed to
demonstrate the capabilities and benefits of dynamic resource
management.  In future work, more focus will be devoted to developing
sophisticated resource management algorithms and a more flexible
framework~\cite{Schmidt:05u} that can allocate and control many DRM
aspects.  We are also developing an efficient MLRM testing methodology
and infrastructure~\cite{Hill:05} so that integration and coordination
problems can be easily identified, thereby enhancing the development
and validation of enterprise DRE systems.
\fi

\iffalse
% Guys, since we are well over our page limit we should omit the
% future work.
Our future work in MLRM will focus on the following:
\begin{itemize}
\item Although the resource pool layer and the physical resources
layer allows us to decouple the resource management algorithms from
the point they are applied in a platform, still lot of work needs to
be done with respect to identifying and collecting requirements for
the algorithms, mixing and matching the right set of allocation and
control algorithms and working with the right set of platform specific
provisioners in the physical resources layer. Hence we would like to
develop an integrated middleware framework to allow handling all those
responsibilities, so that MLRM resource management capabilities can be
totally integrated and also allows plugging in many different
allocation and control algorithms depending on the environment
conditions, so that critical functionalities can be guaranteed in a
prompt manner.

\item Extend the fault monitoring and reconfiguration abilities of
MLRM with an integrated component based fault tolerant middleware
framework, so that MLRM capabilities can be provided with multiple
replication schemes as well as survivable middleware capabilities like
graceful degraded quality of service, dynamic reconfiguration and
component migration.

\item Identify more sophisticated resource allocation algorithms that
can also take into communication characteristics between the
operational strings so that allocation decisions can also exploit the
network bandwidth across the computing resources.
\end{itemize}
\fi

\vspace {-0.2in}
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\section*{Acknowledgments}
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The experiments reported in this paper were conducted in conjunction
with other colleagues on the DARPA ARMS program, including Stephen
Cruikshank, Gary Duzan, Ed Mulholland, David Fleeman, Bala Natarajan,
Rick Schantz, Doug Stuart, and Lonnie Welch.


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