# An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing
## 2 Related Work
[PERSON] and [PERSON] [3] proposed a deep-learning-based autoscaling framework for Kubernetes. The proposed autoscaler used Bi-LSTM to analyze and predict the number of HTTP requests and then generate scaling actions. The results of the experiments on the FIFA World Cup 98 dataset showed that Bi-LSTM could outperform LSTM and ARIMA in terms of prediction accuracy, while maintaining the same prediction speed as that of LSTM. Multivariate time series data are a collection of measurements taken for multiple variables across time. Studying multivariate time series data helps researchers examine and compute the relationship between features. To manage the relationship between variables, they can utilize cross-tabulation, partial correlation, and multiple regressions, as well as add other variables to determine the relationships between the independent and dependent variables or to show the conditions under which the association occurs. The advantage of multivariate analysis over single-variable analysis is that it provides a more realistic picture, thus providing a more powerful test of significance [22]. [PERSON] et al. [5] proposed a hybrid autoscaling system for Kubernetes [20]. The proposed prediction model used Bi-LSTM to analyze and predict the CPU utilization and memory usage of both VMs and pods. To produce elastic scaling actions, the proposed system integrated online reinforcement learning into the proposed Bi-LSTM prediction model, which is a reactive model. The results of the experiments showed that the proposed Bi-LSTM model had better accuracy than those of the ARIMA, RNN, and LSTM models. However, no comparison with the univariate model was performed to analyze the improvement, and the configuration of the proposed Bi-LSTM model was not available. Furthermore, this work did not compare its proposed model with the most recently proposed CNN-LSTM model, and no technique for oscillation mitigation was addressed. [PERSON] and [PERSON] [11] proposed a multivariate prediction model for cloud computing using a CNN-LSTM network. The proposed model receives multiple features, such as CPU utilization, memory usage, network usage, and disk usage, as inputs to the prediction model. The proposed model was evaluated on the GWA-T-12 dataset from the Bitbrains data center [23] and showed that it could outperform the ARIMA-LSTM model in terms of accuracy. However, this study did not use any feature selection method to select the suitable subset features for the inputs of the model. Most of the elated studies focused only on univariate time series analysis, which means that they only analyzed the measurement of a single feature (or variate or variable), such as CPU utilization, or memory usage, or network throughput (HTTP requests), and produced predictions. However, there are multiple features to be considered in the cloud system, some of which might have hidden correlations that depend on each other. Multivariate time series analysis can be used to study these hidden correlations. Thus, it helps the prediction model to better understand the system and improve the prediction accuracy. Furthermore, as mentioned earlier, the huge dimensionality and spatial-temporal dependence features of multivariate time series data, as well as the existence of noisy data make traditional statistical methodologies difficult to model effectively [12]. In addition, statistical methods have been relatively slow in matching dynamic workload demands [3,9], which is not suitable for designing real-time autoscaling systems. For these reasons and given the great success of AI, especially deep learning in various fields, this paper proposes an efficient multivariate autoscaling framework using Bi-LSTM for cloud computing environments.