# An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing
## 2 Related Work  
In this section, we review recent works related to autoscaling and resource estimation using time series forecasting in cloud computing. Related studies using multiple performance metrics, such as CPU utilization, memory usage, disk I/O, and network throughput, are still limited [11]. Table 1 lists some of these recent related studies. Time series forecasting is a popular technique for analyzing the behavior of temporal data and predicting future values. It has been widely used in business [16], as well as traffic flow forecasting [17] and anomaly detection [18]. In general, time series data have a natural temporal ordering and can be categorized into two types: univariate and multivariate time series data. Univariate time series data are a collection of measurements taken for a single variable over time. Univariate refers to a single variable, variate, or feature. [PERSON] et al. [8] proposed a cloud workload prediction module for software as a service (SaaS) using the autoregressive integrated moving average (ARIMA) method to adjust the number of VMs dynamically. They evaluated the proposed prediction model with a realistic HTTP request workload dataset from the Wikimedia Foundation [19]. For seasonal data, the ARIMA model had a 91% accuracy rate. However, the authors did not compare this with other approaches. [PERSON] and [PERSON] [7] presented a hybrid cloud autoscaling architecture known as the cloud resource prediction and provisioning scheme (RPPS). Their proposed prediction model used ARIMA to analyze the historical data of CPU utilization and predict its future usage. The proposed autoscaling architecture, evaluated using varying workloads, showed that their model could outperform the Kubernetes horizontal pod autoscaler (HPA) [20]. [PERSON] et al. [6] presented a novel autoscaling framework using LSTM and multilayer perceptron (MLP) models to analyze and predict the number of HTTP requests coming to the system. They evaluated the models on a realistic HTTP request workload dataset of the FIFA World Cup 98 [21], and the results showed that the LSTM model could outperform MLP models in terms of accuracy. LSTM was also used in [9] as a prediction service to predict the number of HTTP requests in the near future. The experiments were conducted on the FIFA World Cup 98 dataset. The authors showed that the LSTM model had a slight prediction error on the root-mean-squared error (RMSE) metric compared with the ARIMA model, indicating that ARIMA had a slightly better accuracy. However, the prediction speed of LSTM was 100-times faster than that of ARIMA. [PERSON] et al. [10] proposed to use a proactive forecasting method for proactive scaling decisions. The proactive forecasting method consisted of three different prediction models: autoregression (AR), hierarchical temporal memory (HTM), and LSTM. These models were used to learn and predict the incoming HTTP request workload to the system. The authors also proposed HPA+, a backtesting plugin that alternates between the AI-based forecasting technique and HPA automatically. It switches to the HPA if the AI-based forecasting method yields a poor prediction accuracy. The results showed that HPA+ could dramatically reduce the number of rejected requests at the cost of slightly higher resource usage. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Related Studies**} & **Performance Metric(s)** & **Type** & **Method** \\ \hline
[8] & HTTP requests & Univariate & ARIMA \\
[7] & CPU utilization & Univariate & ARIMA \\
[6] & HTTP requests & Univariate & LSTM and MLP \\
[9] & HTTP requests & Univariate & LSTM \\
[10] & HTTP requests & Univariate & AR, HTM, LSTM \\
[3] & HTTP requests & Univariate & Bi-LSTM \\
[5] & CPU utilization, memory usage & Multivariate & Bi-LSTM \\ \hline
[11] & CPU utilization, memory usage, network usage and disk usage & Multivariate & CNN-LSTM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Autoscaling- and resource estimation-related studies using time series forecasting in cloud computing.