% $Id: conclusions.tex,v 1.4 2005/12/09 12:50:23 gokhale Exp $

\section{Concluding Remarks}
\label{sec:conclusions}

Time to market pressures and economic reasons are requiring the next
generation of distributed networked services to be developed via
composition and assembly of off-the-shelf reusable components.  The
service providers are now required to reason about the performance and
dependability of such composed systems.  This paper discusses a
framework for design-time performance analysis and validation of
services that are composed, configured and deployment using
patterns-based middleware building blocks.  The performance analysis
models use Stochastic Reward Nets.  However, as shown due to the
substantial variability that exists within every building block and in
their compositions, it is infeasible to manually develop performance
models for large systems.  We then present a model-driven generative
framework that can be used to automatically synthesize complex SRN models,
simulations and empirical benchmarks for the composed systems.  We
demonstrate the results of solving a SRN model for the Reactor pattern
and validate these results using simulations generated by our MDD
approach. 

Current shortcoming in our work will be addressed as we continue
enhancing our framework. In particular, in future work we will extend
POSAML to include the additional patterns used in network systems.
This will include the ability to compose large-scale systems and
addressing their correctness issues of their compositions.  Moreover,
we will explore the integration of the separate languages we have
developed i.e., POSAML and SRNML.  The goal is to use this integrated
framework to drive performance analysis using analytical models,
simulations and empirical benchmarking of large systems.  Additional
concerns, such as dependability and security, will also be addressed
in combination with the performance dimension addressed in this
paper. 

%%% Local Variables: 
%%% mode: latex
%%% TeX-master: "dsn_pds06"
%%% End: 
