$RMSE$ value obtained after is $4655$. $$T_{shuffle} = 10.45 \times S_d + 579.48 \times M_t + 6144.6\footnote{Linear equation, however we used the SVM model in the prediction.} \label{eq:genericshfmodel}$$

**Write Model** {#writemodel}
---------------

From Figure \[genericmodels\] (Write Phase), the fitted line shows a less-linear relationship between the amount of data and the time taken to write that data to HDFS. The cross-validation plot also shows a strong relationship when the test values are used in the predictive model. The model has an acceptable $RMSE$ of $2427$ $$T_{write} = 6.94 \times R_d + 2139.98
\label{eq:genericwritemodel}$$

**Custom Map and Reduce**
-------------------------

Since these phases have a custom implementation that depends on the problem being solved, we, therefore, used Equation and Equation for approximation.

**Generating the Input Parameters**
-----------------------------------

With the parameters from profiling, $T_{job}$ in is: $$\small
\begin{aligned}
&\frac{M_t}{N_c}[(0.01 \times D + 1.33)+T_{map}+(0.01 \times M_{sel} + 0.97)+\\ 
&\;\;\;\;\;\;\;(0.02 \times M_{sel} + 0.98)+(0.002 \times M_{sel}logM_{sel} + 4.80)] + \\ 
& \;\;\;\;\;\;\frac{R_t}{N_c}[(10.45 \times S_d + 579.48 \times M_t + 6144.6)+T_{reduce}+\\
& \;\;\;\;\;\;\;\;\;(6.94 \times R_d + 2139.98)] + \epsilon 
\end{aligned}
\label{eq:totaljobcomplete}$$ Users now need to provide the actual values for the input parameters listed in Table \[table:logextracted\].

         **Name**         **Variable**          **Description**
  ---------------------- -------------- -------------------------------
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