Commit 0be8abc4 authored by Simon Eismann's avatar Simon Eismann
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updated due to addition of BYTE_SIZE characterisation

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......@@ -485,7 +485,7 @@ As shown in Figure~\ref{fig:mm-influencedvariables_influencingparameters}, an in
An \model{InfluencingParameter} represents a parameter that has an \emph{influence on} other model variables. Such parameters are either \model{CallParameter}s or \model{ShadowParameter}s, modeled as subtypes of \model{InfluencingParameter}.
A \model{CallParameter}, see Figure~\ref{fig:mm-callparameter_hierarchy}, is either a service input parameter, an external call parameter, or a return parameter of an external call. Given that in performance models, a service call parameter is only modeled if it is performance-relevant (see Figure~\ref{fig:mm-callparameter}), each modeled service call parameter can be considered to have a performance influence. Furthermore, the proposed modeling abstractions support referring not only to a parameter \instance{VALUE}, but also to other characterizations such as \model{NUMBER\_OF\_ELEMENTS} if the referenced data type is a collection (cf.~\cite{becker2008a}).
A \model{CallParameter}, see Figure~\ref{fig:mm-callparameter_hierarchy}, is either a service input parameter, an external call parameter, or a return parameter of an external call. Given that in performance models, a service call parameter is only modeled if it is performance-relevant (see Figure~\ref{fig:mm-callparameter}), each modeled service call parameter can be considered to have a performance influence. Furthermore, the proposed modeling abstractions support referring not only to a parameter \instance{VALUE}, but also to other characterizations such as \model{NUMBER\_OF\_ELEMENTS} if the referenced data type is a collection (cf.~\cite{becker2008a}). Additionally the model{BYTE\_SIZE} of a call parameter can be modeled, which is used to analyze network traffic.
A \model{ShadowParameter} is an \model{InfluencingParameter} with a designated \model{name} and \model{description}. These attributes are intended to provide a human-understandable description that could be used by component developers, system architects, or performance engineers to identify and model relationships between model variables. A \model{ShadowParameter} can be considered as an auxiliary model entity allowing to model parameter dependencies in a flexible way. In order to resolve parameter dependencies using monitoring data, \model{ShadowParameter}s need to be mapped to some observable parameters that are accessible at run-time.
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