Commit 70a45039 authored by Jürgen Walter's avatar Jürgen Walter
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descartes-research.net ==> descartes.tools

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%!TEX root=../../DML.tex
This technical report introduces the \acrfull{dml}, a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. \gls{dml} is designed to serve as a basis for \emph{self-aware} resource management\footnote{The interpretation of the term "self-aware" is described in detail in Sec.~\ref{Sec:Self-Awareness}}~\cite{KoBrHuRe2010-SCC-Towards,Ko2011-SE-DescartesResearch} during operation ensuring that system quality-of-service requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible. The term Quality-of-Service (QoS) is used to refer to non-functional system properties including performance (considering classical metrics such as response time, throughput, scalability and efficiency) and dependability (considering in addition: availability, reliability and security aspects). The current version of \gls{dml} is focused on performance and availability including capacity, responsiveness and resource efficiency aspects, however, work is underway to provide support for modeling further QoS properties. The meta-model itself is designed in a generic fashion and is intended to eventually support the full spectrum of QoS properties mentioned above. Given that the initial version of \gls{dml} is focussed on performance, in the rest of this document, we mostly speak of performance instead of QoS in general. Information on the latest developments around the \acrfull{dml} can be found at \url{http://www.descartes-research.net}.
This technical report introduces the \acrfull{dml}, a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. \gls{dml} is designed to serve as a basis for \emph{self-aware} resource management\footnote{The interpretation of the term "self-aware" is described in detail in Sec.~\ref{Sec:Self-Awareness}}~\cite{KoBrHuRe2010-SCC-Towards,Ko2011-SE-DescartesResearch} during operation ensuring that system quality-of-service requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible. The term Quality-of-Service (QoS) is used to refer to non-functional system properties including performance (considering classical metrics such as response time, throughput, scalability and efficiency) and dependability (considering in addition: availability, reliability and security aspects). The current version of \gls{dml} is focused on performance and availability including capacity, responsiveness and resource efficiency aspects, however, work is underway to provide support for modeling further QoS properties. The meta-model itself is designed in a generic fashion and is intended to eventually support the full spectrum of QoS properties mentioned above. Given that the initial version of \gls{dml} is focussed on performance, in the rest of this document, we mostly speak of performance instead of QoS in general. Information on the latest developments around the \acrfull{dml} can be found at \url{http://www.descartes.tools}.
%architecture-level performance model to describe the service behavior and the resource landscape of modern distributed virtualized data centers.
% 1. Eingrenzung des Forschungsbereichs (In welchem Themengebiet ist die Arbeit angesiedelt? Wie ist das Verhältnis zum Thema der Konferenz/des Journals?)
......@@ -264,7 +264,7 @@ Given that \gls{dml} supports detailed impact analyses, e.g., workload intensity
\label{Sec:Self-Awareness}
As mentioned above, a major application of the \acrfull{dml} is to serve as a basis for \emph{self-aware} resource management during operation\shorten{ ensuring that system quality-of-service requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible}. Self-aware computing systems are best understood as a sub-class of autonomic computing systems. In this section, we explain in more detail what exactly is meant by \emph{self-awareness} in this context.
\gls{dml} is a major part of our broader long-term research effort\footnote{\url{http://www.descartes-research.net}} aimed at developing novel methods, techniques and tools for the engineering of \emph{self-aware} computing systems~\cite{KoBrHuRe2010-SCC-Towards,Ko2011-SE-DescartesResearch}. The latter are designed with built-in online QoS prediction and self-adaptation capabilities used to enforce QoS requirements in a cost- and energy-efficient manner. Self-awareness in this context is defined by the combination of three properties that a system should possess:
\gls{dml} is a major part of our broader long-term research effort\footnote{\url{http://www.descartes.tools}} aimed at developing novel methods, techniques and tools for the engineering of \emph{self-aware} computing systems~\cite{KoBrHuRe2010-SCC-Towards,Ko2011-SE-DescartesResearch}. The latter are designed with built-in online QoS prediction and self-adaptation capabilities used to enforce QoS requirements in a cost- and energy-efficient manner. Self-awareness in this context is defined by the combination of three properties that a system should possess:
\begin{enum}
\item \mbox{\emph{Self-reflective}:} Aware of its software architecture, execution environment, and hardware infrastructure on which it is running as well as of its operational goals (e.g., QoS requirements, cost- and energy-efficiency targets),
\item \mbox{\emph{Self-predictive}:} Able to predict the effect of dynamic changes (e.g., changing service workloads) as well as predict the effect of possible adaptation actions (e.g., changing system configuration, adding/removing resources),
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