# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 4 Evaluation and Design Consideration
### Evaluation
#### 4.1.3 Perceptron and LSTM  
Perceptron and LSTM are used in the reviewed papers. It is difficult to find a suitable network structure for the technology, but it is important to select a suitable neural network. Based on the paper introduced in Section 2, Table 3 summarizes the characteristics, advantages, and disadvantages of the two models. This is assumed that all models are computed in a single layer. The model parameter means a parameter whose value changes during training, such as weight. Operation refers to the computation method of neural networks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Advantage** & **Disadvantage** \\ \hline \multirow{3}{*}{**Machine Learning Model**} & (1) & Can learn various data distribution & \multirow{3}{*}{(1)} \\  & & according to the complexity or & & \\  & & characteristics of the model. & & \\ \hline \multirow{3}{*}{**Traditional Model**} & (1) & Fast result (The model is already implemented, and results are quickly out). & \multirow{3}{*}{(1)} \\  & & Not sensitive to data distribution. & & \\ \hline \multirow{3}{*}{**Traditional Model**} & (2) & Fast result (The model is already implemented, and results are quickly out). & \multirow{3}{*}{(1)} \\  & & Not sensitive to data distribution. & & \\ \hline \multirow{3}{*}{**Traditional Model**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & & Not sensitive to data distribution. & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Difference between machine learning model and traditional model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Perceptron** & **LSTM** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} **Model** \\ **parameter** \\ \end{tabular} } & Properties & \multicolumn{2}{c}{Proportional to the number of inputs.} & Parameters exist for each gate as well as input. \\ \cline{2-4}  & Advantage & \(\bullet\) & Fast training & The relationship of inputs can be saved in \\  & & \(\bullet\) & Small model size & more detail. \\ \cline{2-4}  & Disadvantage & Difficult to remember complex distribution. & \(\bullet\) & Model size increases. \\  & & \(\bullet\) & Slow training. \\ \hline \multirow{3}{*}{**Operation**} & Properties & \multicolumn{2}{c}{Feed forward} & Hidden layer is used as input again. \\ \cline{2-4}  & Advantage & \(\bullet\) & Simple operation & \\ \cline{1-1}  & Advantage & \(\bullet\) & Back-propagation algorithm isn’t needed & Can take advantage of the previous \\ \cline{1-1}  & Advantage & \multicolumn{2}{c}{to update the weight.} & long history. \\ \cline{1-1} \cline{2-4}  & Disadvantage & \multicolumn{2}{c}{It is not remembering long-term history} & \multirow{3}{*}{Not suitable for time-variant system.} \\  & & because it is only affected by the current input. & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} **Offline\&** \\ **Online learning** \\ **learning** \\ \end{tabular} } & Properties & Offline \& Online learning are available & Offline learning is available \\ \cline{1-1} \cline{2-4}  & Advantage & \multicolumn{2}{c}{Even at runtime, the model can be flexibly changed according to the influx of workload.} & Can give a variety of models and features. \\ \cline{1-1} \cline{2-4}  & Disadvantage & \multicolumn{2}{c}{Online learning requires limitations (storage space, data operation), so a less complex model is required.} & When limited resources are given, learning becomes impossible if the amount of \\ \cline{1-1} \cline{2-4}  & & \multicolumn{2}{c}{is required.} & data increases. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties and Advantages and Disadvantages of Perceptron and LSTM.