# An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing
## 5 Conclusions  
Cloud computing plays a significant role in improving the efficiency of enterprises, industries, etc. Autoscaling is an essential mechanism in cloud computing that automatically adds or removes resources. However, over time, the workload demands fluctuate continuously, which makes their prediction difficult. To predict the resource workload, many studies have proposed a technique called time series forecasting. However, most of these studies have focused only on univariate time series forecasting. This study proposed an efficient multivariate autoscaling framework using Bi-LSTM for cloud computing environments. The proposed autoscaling system framework can be flexibly implemented for VM platforms (such as OpenStack [31]) and container platforms (such as Docker Swarm [32] and Kubernetes [33]). The experiments were conducted on actual trace workload datasets from the Bitbrains and Materma data centers.  
Figure 14: Number of VMs provided by the autoscaler using multivariate Bi-LSTM.  
Figure 13: Number of VMs provided by the autoscaler using multivariate CNN-LSTM.  
The evaluation results showed that our proposed multivariate Bi-LSTM model can outperform not only the univariate Bi-LSTM model, but also other well-known multivariate deep learning models such as LSTM and CNN-LSTM in terms of prediction accuracy. Furthermore, this work also showed the advantage in the resource-provision efficiency of the proposed multivariate Bi-LSTM autoscaler, which had better autoscaling gains than those of the other multivariate deep-learning-based autoscalers.  
In future work, we plan to extend our work to hybrid autoscaling (which fuses reactive and proactive autoscaling) and extend the inputs of the proposed prediction model to evaluate its performance.  
N.-M.D.-Q. proposed the idea, performed the analysis, and wrote the manuscript. M.Y. provided the guidance for the data analysis and paper writing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-0-02046 and IITP-2021-2017-0-01633) supervised by the IITP(Institute of Information & Communications Technology Planning & Evaluation).  
Not applicable.  
The authors declare no conflict of interest.  
The Notations used in this paper are shown as follows:  
\begin{tabular}{l l} \(r_{pear}\) & Pearson correlation \\ \(X\) & Features \\ \(x_{i}\) & Average value of feature \(X_{i}\) \\ \(\tilde{x}_{j}\) & Average value of feature \(X_{j}\) \\ \(MD\) & History of multivariate time series dataset \\ \(md_{i,j}|i=1,2;j=t-w,\ldots,t-1\) & History time series data of the selected feature at a \\  & specific time step \\ \(i_{t}\) & Input gate of an LSTM unit \\ \(f_{t}\) & Forward gate of an LSTM unit \\ \(c_{t}\) & Memory cell of an LSTM unit \\ \(o_{t}\) & Output gate of an LSTM unit \\ \(h_{t}\) & Hidden state of an LSTM unit \\ \(\sigma\) & Activation functions \\ \(\odot\) & Hadamard product \\ \(network_{t+1}\) & Predicted workload in the next interval \\ \(VMs_{min}\) & Minimum number of VMs that the system has to maintain \\ CPT & Control duration \\ \(VMs_{t+1}\) & Number of estimated VMs at the next interval \\ \(network_{VM}\) & Maximum network workload that a VM can handle in a minute \\ \(network_{t+1}\) & Estimated network workload in the next time step \\ \(VM_{current}\) & Number of current VMs in the system \\ \(v\) & Predicted values \\ \(l\) & True values \\ \(MAE\) & Mean absolute error \\ \(MSE\) & Mean-squared error \\ \(RMSE\) & Root-mean-squared error \\ \end{tabular}