# Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Paper** & **Virtualization Technology** & **Monitored Metrics** & **Method** & **Technique** & **Oscillation Mitigation** \\ \hline [PERSON] et al. [6] & VM & Request rate & Reactive & ARIMA & No \\ [PERSON] et al. [11] & Container & CPU, Memory & Reactive & Rule-based & Yes \\ HPA [12] & Container & CPU & Reactive & Rule-based & Yes \\ [PERSON] and [PERSON] [13] & Container & CPU & Proactive & ARMA & Yes \\ [PERSON] et al. [7] & VM & Request rate & Proactive & ARIMA & No \\ [PERSON] et al. [14] & Container & Request rate & Proactive & ARIMA & No \\ [PERSON] et al [15] & - & Request rate & Proactive & GAs & No \\ [PERSON] et al. [23] & - & Task length & Proactive & ANN & No \\ [PERSON] et al. [8] & VM & Request rate & Proactive & ANN, RNN & No \\ [PERSON] et al. [16] & Container & Request rate & Proactive & LSTM & Yes \\ [PERSON] et al. [17] & Container & CPU & Proactive & Bi-LSTM & Yes \\ [PERSON] et al. [24] & Container & CPU, Memory & Hybrid & Bi-LSTM & No \\ [PERSON] et al. [25] & Container & Request rate & Proactive & AR, HTM, LSTM & No \\ \hline \end{tabular}
\end{table} 
Table 1: Overview of recent works related to autoscaling methods. [PERSON] et al. [7] proposed a cloud workload prediction module using ARIMA, which can dynamically provision VMs for serving the predicted requests. For the performance evaluation, they used real traces of HTTP web server requests from Wikimedia Foundation [26]. This model provides 91% accuracy for seasonal data; however, it is not suited for non-seasonal workload. In addition, the authors did not compare this model with any approach. [PERSON] et al. [14] also used ARIMA to predict the amount of future workload requested, as it can achieve higher accuracy for short-term forecasting. They performed evaluations using the following four ARIMA models, which had the same degree of differencing (d) as 1 and order of moving average (q) as 0 while varying the lag order value (p) from 1 to 4. The results showed that the model with lag order of 4 had the lowest prediction error for the incoming requests compared to the other models. [PERSON] et al. [15] proposed a prediction model using genetic algorithms (GAs) to combine many statistical methods. They used three logs extracted from real web servers to evaluate their model. The results showed that the proposed model yields the best results. However, with the NASA web server logs [27], the ARIMA model achieves the best result with the best prediction accuracy. The ARIMA model's configuration was chosen by the auto.ari-ma(0) function of the R package proposed in [28] to automatically choose the value for \(p,d,q\). The prediction model proposed in [23] used Artificial Neural Network (ANN) to forecast the task duration and resource utilization. To prepare the ANN dataset, a crawler was actualized to gather the number of files and the size of the repositories from GitHub and the task length. Then the ANN model was trained offline. The proposed model incurred less than 20% prediction error compared to a simple linear prediction model. However, as it was trained offline, this model is not suitable for real applications. [PERSON] et al. in [8] introduced a new autoscaling framework using Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) models to predict workload in the future. Then, based on the forecasted workload, they converted to the needed RAM and CPU core to keep services working under the Service Level Agreement (SLA). The performance was evaluated by using access logs from the 1998 World Cup Web site [26] for multiple-step ahead forecasting. The result showed the accuracy of ARIMA model became worse with more predicted step ahead. Furthermore, the LSTM model archived better accuracy than the MLP model. [PERSON] et al. in [16] also proposed a machine learning-based autoscaling architecture. The resource estimator used the LSTM model and conducted an experiment on the 1998 World Cup website dataset.