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authorYigit Sever2020-12-01 23:08:08 +0300
committerYigit Sever2020-12-01 23:08:08 +0300
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tree02da6ea6082d48b16a16818222d27d79a911895d /answer_sheet
parentb4996a0504e0049311523aac1df4a8e6d8891458 (diff)
downloadhw3-c488bc4970823dfc740d5473d6fa928edc58242e.tar.gz
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Include the answer to first question
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11 11
12\maketitle 12\maketitle
13 13
14\section{Job Scheduling}%
15\label{sec:job_scheduling}
16
17First we will state the variables we will use.
18A job $n_i \in n$ is split into preprocessing $p_i \in p$ and indexing $f_i \in f$ per the question text.
19With the given question text, we will ignore any kind of utilisation or efficiency concerns.
20In other words, we will not care about the number of PCs we employ nor the time they will stay idle.
21So, every indexing job $f_i \in f$ will be run on a separate PC\@.
22
23Since we cannot pipeline the preprocessing jobs $p_{i} \in p$, any kind of real choice we have will be done on the $f_i \in f$.
24The trivial case is when the preprocessing jobs are the \enquote{bottleneck} of the system, where;
25
26\begin{equation*}
27 \forall p_{i} \in p > \forall f_{i} \in f
28\end{equation*}
29
30The completion time in this case is minimized by sorting the $f_{i} \in f$ and placing the \emph{shortest} $f_{i}$ last.
31
32\begin{figure}[htpb]
33 \centering
34 \includegraphics[width=0.5\linewidth]{q1_1}
35 \caption{The trivial case where preprocessing jobs are all longer than the indexing jobs}%
36 \label{fig:q1_1}
37\end{figure}
38
39This approach extends to the case where the preprocessing time can be shorter than the indexing time as well.
40In the example given in Figure~\ref{fig:1_cases}, two preprocessing jobs $p_1$ and $p_2$ both take 1 unit of time.
41Whereas, the indexing jobs tied to the preprocessing jobs $f_1$ and $f_2$ take 5 and 1 units of time, respectively.
42By scheduling the preprocessing task that is tied to the longest indexing job $p_1$ first, we can finish the whole computation in 6 time units which takes 7 time units in the other case.
43
44Formally, the algorithm we propose simply sorts the $f_i \in f$ in $\mathcal{O}(n\log(n))$ time and schedules the jobs with respect to $f_i \in f$ (so the $p_i \in p$ tied to $f_i \in f$) from longest to shortest.
45
46\begin{figure}
47 \centering
48 \begin{subfigure}[b]{0.45\textwidth}
49 \includegraphics[width=\textwidth]{q1_2}
50 \caption{The longest task is scheduled first}%
51 \label{subfig:1_1}
52 \end{subfigure}
53 ~
54 \begin{subfigure}[b]{0.45\textwidth}
55 \includegraphics[width=\textwidth]{q1_3}
56 \caption{The longest task is scheduled last}%
57 \label{subfig:1_2}
58 \end{subfigure}
59 \caption{The worked example of the cases}%
60 \label{fig:1_cases}
61\end{figure}
62
63
14\end{document} 64\end{document}
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33\usepackage[super]{nth} 33\usepackage[super]{nth}
34\usepackage{caption} 34\usepackage{caption}
35\usepackage{tikz} 35\usepackage{tikz}
36\usepackage{subcaption}
36\usetikzlibrary{arrows,fit,positioning} 37\usetikzlibrary{arrows,fit,positioning}
37\usepackage[style=authoryear]{biblatex} 38\usepackage[style=authoryear]{biblatex}
38 39