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+\documentclass[]{llncs}
+
+\usepackage{todonotes}
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+\usepackage{array}
+\usepackage{xcolor}
+\usepackage{color}
+\usepackage{colortbl}
+\usepackage{subcaption}
+\usepackage{hyperref}
+\usepackage{listings}
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+\usepackage{caption}
+
+
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+	belowskip=-10pt,
+	aboveskip=-10pt,
+}
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+	captionpos=b,
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+
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+\usepackage{cleveref}
+\crefname{codecount}{Code}{Codes}
+
+\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/hex\_lev
+	\item pm\_quant
+\end{itemize}
+
+\section{Evaluation}
+\label{sec:evaluation}
+
+Two study examples (two reference jobs):
+\begin{itemize}
+	\item jobA: shorter length, e.g. 5-10, that has a little bit IO in at least two metadata metrics (more better).
+	\item jobB: 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.
+
+\section{Summary and Conclusion}
+\label{sec:summary}
+
+%\printbibliography
+\end{document}