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\begin{frontmatter}

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\title{Performance Analysis of a Middleware Event Demultiplexing Pattern for Network Services\thanksref{label_thks}}
\thanks[label_thks]{This research is supported in part by US National Science Foundation (NSF) Computer Systems Research (CSR) and Next Generation Software (NGS) programs.}
\author[label1]{Swapna S. Gokhale\corauthref{cor1}}
\ead{ssg@engr.uconn.edu}
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\corauth[cor1]{Corresponding Author.}
\address[label1]{Dept. of CSE, Univ. of Connecticut, Storrs, CT 06269.}
\author[label2]{Aniruddha S. Gokhale}
\ead{a.gokhale@vanderbilt.edu}
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\address[label2]{Dept. of EECS, Vanderbilt Univ., Nashville, TN 37235.}
\author[label3]{Jeffrey G. Gray}
\ead{gray@cis.uab.edu}
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\address[label3]{Dept. of CIS, U. of Alabama at Birmingham, Birmingham, AL 35294.}

\begin{abstract}
Society is becoming increasingly reliant on the services provided 
by distributed, performance sensitive software systems. These systems
demand multiple simultaneous quality of service (QoS) properties. 
A key enabler in recent successes in the development of such systems  
has been middleware, which comprises reusable building blocks. 
Typically, a large number of configuration options 
are available for each building block when composing a system end-to-end. 
The choice of the building blocks and their configuration options have
an impact on the performance of the services provided by the
systems. Currently, the effect of these choices can be determined only very 
late in the lifecycle, which can be detrimental to system development
costs and schedules. In order to enable the right design choices, 
a systematic methodology to analyze the performance of these systems 
at design time is necessary. Our methodology consists of
models of individual building blocks of the middleware and their
compositions, which are used to analyze the performance of the
composed system. As a first step towards realizing this methodology,
this paper introduces a model of the Reactor pattern, which provides
important synchronous demultiplexing and dispatching capabilities to
network services and applications. The model is based on the 
Stochastic Reward Net (SRN) modeling paradigm. 
We illustrate how the model could be used to obtain the performance
metrics of a Virtual Private Network (VPN) service provided by a 
Virtual Router (VR). 
\end{abstract}

\begin{keyword}
Middleware, Reactor pattern, Performance analysis, Stochastic
Reward Nets
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\end{frontmatter}

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\vspace*{0.8in}

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