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\title{Using Machine Learning to Identify Similar Jobs Based on their IO Behavior}
\author{Julian Kunkel\inst{2} \and Eugen Betke\inst{1}}

\institute{
University of Reading--%
\email{j.m.kunkel@reading.ac.uk}%
\and
DKRZ --
\email{betke@dkrz.de}%
}
\begin{document}
\maketitle

\begin{abstract}

Support staff.
Problem, a particular job found that isn't performing well.
Now how can we find similar jobs?

Problem with definition of similarity.

In this paper, a methodology and algorithms to identify similar jobs based on profiles and time series are  illustrated.
Similar to a study.

Research questions: is this effective to find similar jobs?

The contribution of this paper...
\end{abstract}

\section{Introduction}

%This paper is structured as follows.
%We start with the related work in \Cref{sec:relwork}.
%Then, in TODO we introduce the DKRZ monitoring systems and explain how I/O metrics are captured by the collectors.
%In \Cref{sec:methodology} we describe the data reduction and the machine learning approaches and do an experiment in \Cref{sec:data,sec:evaluation}.
%Finally, we finalize our paper with a summary in \Cref{sec:summary}.

\section{Related Work}
\label{sec:relwork}

\section{Methodology}
\label{sec:methodology}

Given: the reference job ID.
Create from 4D time series data (number of nodes, per file systems, 9 metrics, time) a feature set.

Adapt the algorithms:
\begin{itemize}
	\item iterate for all jobs
		\begin{itemize}
			\item compute distance to reference job
		\end{itemize}
	\item sort the jobs based on the distance to ref job
	\item create cumulative job distribution based on distance for visualization, allow users to output jobs with a given distance
\end{itemize}

A user might be interested to explore say closest 10 or 50 jobs.

Algorithms:
Profile algorithm: job-profiles (job-duration, job-metrics, combine both)
$\rightarrow$ just compute geom-mean distance between profile

Check time series algorithms:

\begin{itemize}
	\item bin
	\item hex\_native
  \item hex\_lev
	\item hex\_quant
\end{itemize}

\section{Evaluation}
\label{sec:evaluation}

Two study examples (two reference jobs):
\begin{itemize}
	\item job-short: shorter length, e.g. 5-10, that has a little bit IO in at least two metadata metrics (more better).
  \item job-mixed:
	\item job-long: a very IO intensive longer job, e.g., length $>$ 20, with IO read or write and maybe one other metrics.
\end{itemize}

For each reference job: create CSV file which contains all jobs with:
\begin{itemize}
	\item JOB ID, for each algorithm: the coding and the computed ranking $\rightarrow$ thus one long row.
\end{itemize}
Alternatively, could be one CSV for each algorithm that contains JOB ID, coding + rank

Create histograms + cumulative job distribution for all algorithms.
Insert job profiles for closest 10 jobs.

Potentially, analyze how the rankings of different similarities look like.

\Cref{fig:refJobs}

\begin{figure}
\begin{subfigure}{0.8\textwidth}
\includegraphics[width=\textwidth]{job-timeseries4296426}
\caption{Job-S} \label{fig:job-S}
\end{subfigure}

\caption{Reference jobs: timeline of mean IO activity}
\label{fig:refJobs}
\end{figure}


\begin{figure}\ContinuedFloat

\begin{subfigure}{0.8\textwidth}
\includegraphics[width=\textwidth]{job-timeseries5024292}
\caption{Job-M} \label{fig:job-M}
\end{subfigure}

\begin{subfigure}{0.8\textwidth}
\includegraphics[width=\textwidth]{job-timeseries7488914-30.pdf}
\caption{Job-L (first 30 segments of 400; remaining segments are similar)}
\label{fig:job-L}
\end{subfigure}
\caption{Reference jobs: timeline of mean IO activity; non-shown timelines are 0}
\end{figure}


\section{Summary and Conclusion}
\label{sec:summary}

%\printbibliography
\end{document}