# An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing  
[PERSON]  
1 Department of Information Communication Convergence Technology, Soongsil University,  
Seoul 06978, Korea; [EMAIL_ADDRESS]
2 School of Electronic Engineering, Soongsil University, Seoul 06978, Korea  
Corresponded: [EMAIL_ADDRESS]  
[PERSON]  
These authors contributed equally to this work. 22222  
proposed a technique called time series forecasting [3; 5; 6; 7; 8; 9; 10; 11]. However, most of these studies focused on univariate time series analysis, that is they only analyzed the measurement of a single feature (or variate or variable), such as CPU utilization or memory usage, 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 better understand the system and improve the prediction accuracy.  
The large 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]. Owing to the advancement of artificial intelligence (AI), especially in deep learning, the technology has been applied to various industries, including computer vision, natural language processing, and medical image analysis [13]. Although traditional statistical methodologies can model time series data, deep-learning-based time series forecasting is becoming prevalent [3; 9]. In addition, it is promising for designing autoscalers [14]. This paper proposes a multivariate deep-learning-based autoscaling framework using the proactive approach for cloud computing. There are various types of deep learning approaches used in time series forecasting, including recurrent neural networks (RNNs), artificial neural networks, convolutional neural networks (CNNs), and long short-term memory (LSTM). Bidirectional LSTM (Bi-LSTM) [15] is a special type of LSTM with two LSTM layers. These layers run the input data in opposite directions, which helps the model preserve information from the past and the future. Thus, Bi-LSTM can better accumulate knowledge and improve prediction accuracy. The contributions of this study are as follows:  
* First, we propose an efficient multivariate proactive autoscaling framework architecture using Bi-LSTM designed for cloud computing;
* Second, through the implementation of a multivariate Bi-LSTM prediction model for time series forecasting, we demonstrate that it outperforms both the univariate Bi-LSTM model and other multivariate time series prediction algorithms;
* Thirdly, we investigated the under-provisioning and over-provisioning of resources by the proposed multivariate Bi-LSTM autoscaler and other well-known multivariate autoscalers.  
By utilizing different datasets, our studies revealed that the proposed multivariate Bi-LSTM model outperforms not only the univariate Bi-LSTM model, but also other well-known multivariate deep learning models such as LSTM and CNN-LSTM in prediction accuracy. Furthermore, this work also shows the advantage of the multivariate Bi-LSTM autoscaler compared with other multivariate deep-learning-based autoscalers in terms of resource provisioning efficiency.  
The rest of the paper is organized as follows. In Section 2, we review research on autoscaling using the time series forecasting technique and its data. Section 3 presents the system framework architecture followed by the experiments and evaluation in Section 4. Finally, Section 5 concludes the paper.