\chapter{Implementation}\label{s:implementation}
In this chapter we describe the implementation of \gls{my_system}.
The main contribution of this chapter towards answering \emph{RQ3} is the prototype of \gls{my_system}.
After reading one should understand the technical decisions, choice of tools and modifications to existing software necessary for evaluation of \gls{my_system} in \Cref{s:evaluation}.

Any complex system is more than the sum of its parts~\cite{Wikipedia:article/Systems_Thinking}.
To understand why \gls{my_system} it is crucial to provide a holistic view on the prototype.
Therefore, the rest of the chapter is structured in a top-down approach: \Cref{ss:implementation_overview}
presents the rationale for using the specific software packages, \Cref{ss:data_flow} shows the flow of data within the system, and \Cref{ss:programming} details the different modifications and new software extensions.

\section{Overview}\label{ss:implementation_overview}

\begin{figure}[t]
	\centering
	\includegraphics[width=0.85\linewidth]{images/implementation.pdf}
	\caption{The prototype and its components based on the architecture.
		The time-series data flows first to the \texttt{Grafana} dashboard, \texttt{PostgreSQL} database and \texttt{Redis} cache as advised in ~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24}.}
	\label{fig:implementation}
\end{figure}

At the onset of the project, we decided \gls{my_system} will use only state-of-the-art software, deployed in the industry or evaluated in peer-reviewed scientific publications.
The mapping of software packages used onto the reference architecture can be seen in \Cref{fig:implementation}.
In order to facilitate visualizations and interactive dashboards, we decided to use \code{Grafana} (\myCircled{2a})~\cite{Wikipedia:article/Grafana}.
To enable the flow of data into the digital twin, we use \code{Kafka} (\myCircled{2b})~\cite{Wikipedia:article/Confluent}.
To store the in-band data we use a \code{Redis} (\myCircled{3b})~\cite{Wikipedia:article/Redis} cache, and for out-of-band data we use a \code{PostgreSQL}(\myCircled{3a})~\cite{Wikipedia:article/Postgresql}.
To enable predictive analytics, we chose a discrete-event simulator, \code{OpenDC}(\myCircled{4a})~\cite{GitHub:software/OpenDC}.
The \code{Analytics Engine} (\myCircled{4b}), \code{Monitoring Service} (\myCircled{4c}), and \code{HTTP Server} (\myCircled{3c}) are described in detail in \Cref{ss:programming}.

\code{Grafana} (\myCircled{2a})is a state-of-the-art industry tool to visualize dashboards.
We posit it is crucial to include a user-friendly \gls{ui}.
A number of previous publications on digital twinning find dashboards important~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24, DBLP:conf/wosp/SumanCNTMI24, DBLP:conf/wosp/NicolaeTKLI26}.
We chose \code{Grafana} instead of other software packages because of its seamless integration with \code{PostgreSQL}.
\code{Grafana} provides good separation of concerns and compartmentalization as it does not store the displayed metrics itself.
Instead, it queries the \code{PostgreSQL} (\myCircled{3a}) database in real-time~\cite{Wikipedia:article/Grafana}, unlike \eg \code{InfluxDB}.
Good alternatives to \code{Grafana} are \code{Kibana}~\cite{Wikipedia:article/Kibana}, \code{Prometheus}~\cite{Wikipedia:article/Prometheus}, and \code{Graphite}~\cite{Wikipedia:article/Graphite}.

\code{Kafka} (\myCircled{2b}), particularly \code{Kafka} developed by Confluent~\cite{Wikipedia:article/Confluent} is a battle-tested message broker that provides versatile capabilities to transfer huge volumes of data with little latency, in real-time.
We decided to use \code{Confluent Kafka} instead of \code{Kafka} developed by the Apache Foundation, because of it's masterful connector system allowing to easily add sources and sinks (\eg \code{PostgreSQL} (\myCircled{3a}) sink, \code{Redis} (\myCircled{3b}) sink, \code{OpenDC} source (\myCircled{4a}) ).
Additionally, as opposed to \code{Apache Kafka}, \code{Confluent Kafka} comes equipped with a \code{Schema Registry}.
The \code{Schema Registry} is a important component that allows the storage of database and cache schemas for easy retrieval.
With \code{Schema Registry}, we ensure that the data stored in \code{PostgreSQL} (\myCircled{3a})  tables and in \code{Redis} (\myCircled{3b}) streams contains the exact same schema.
Moreover, \code{Schema Registry} is compatible with versatile data interchange formats, such as \code{ProtoBuf}~\cite{Wikipedia:article/ProtoBuf}.

\code{Redis} (\myCircled{3b}), is a key value store that provides efficient store and retrieval operations~\cite{Wikipedia:article/Redis}.
In particular, \code{Redis} (\myCircled{3b}) is capable of storing \emph{streams} -- append only logs which allow for fast and quick query of large volumes of data.
\code{Redis} (\myCircled{3b}) is the industry leader in key value caching.
The only alternative to \code{Redis} (\myCircled{3b}) is \code{memcached}~\cite{Wikipedia:article/Memcached}, which does not provide the capability to integrate with \code{Kafka} (\myCircled{2b}).

\code{PostgreSQL} (\myCircled{3a})is a database management system, necessary to store large volumes of out-of-band data coming from the physical datacenter.
The \code{PostgreSQL} (\myCircled{3a}) server provides a simple and straightforward interface to query the data via \code{psql}.
Importantly, to adhere to the single responsibility principle, \code{PostgreSQL} (\myCircled{3a}) does not provide any \gls{ui}.
Additionally, there exist many integrations between \code{PostgreSQL} (\myCircled{3a}) and other software, including \code{Kafka} (\myCircled{2b}).
The many alternatives to \code{PostgreSQL}  (\myCircled{3a}) are listed in~\cite{Wikipedia:article/Postgresql}.
An alternative used in previous work is \code{InfluxDB}~\cite{DBLP:conf/wosp/SumanCNTMI24}.

Lastly, to enable predictive analytics we use a state-of-the-art discrete-event simulator, \code{OpenDC}(\myCircled{4a})~\cite{GitHub:software/OpenDC}.
\code{OpenDC} (\myCircled{4a}) is a leading software package capable of modeling complex datacenter phenomena and workloads (\eg failures, workflows, machine learning).
For a specific overview of advantages of \code{OpenDC}  (\myCircled{4a}) and a thorough comparison with other alternatives, see \Cref{tab:datacenter_simulator_comparison}.

\begin{figure}[t]
	\centering
	\includegraphics[width=\linewidth]{images/flow_diagram.png}
	\caption{The data flow within \gls{my_system}.}
	\label{fig:flow_diagram}
\end{figure}

\section{Data Flow}\label{ss:data_flow}
\input{sources/listing_sinks.tex}

Efficient data flow is of utmost importance to digital twinning.
In \Cref{fig:flow_diagram} we present the moving of data within \gls{my_system}.



\begin{figure}[t]
	\input{sources/listing_schema.tex}
\end{figure}

\section{Programming Effort}\label{ss:programming}