# Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction
## 2 Related Work
### Workload Prediction in the Cloud
Thus, LSTM demonstrates superior performance in single-task scenarios, particularly for short-term time-series prediction. Neither LSTM nor Transformer architectures, as standalone models, are likely to achieve optimal prediction performance when addressing complex, volatile, and seasonally varying workloads in cloud computing. To overcome these limitations, some researchers have employed multiple models for workload prediction. The multi-model approach categorizes workloads into different groups and applies corresponding sub-models to predict the workload for each category. In [23], based on a normality test, the workload was divided into two categories: if the workload satisfied the normality test, ARIMA was used to predict the CPU workload; otherwise, NNs were applied. Similarly, the authors in [24] divided the resource usage of tasks into different categories according to the priorities and change rates of the tasks. They employed SVR and LR models to predict workloads for different categories. The results showed that multiple models can achieve higher prediction accuracy than a single model. However, few studies have focused on multiple model-based methods. Additionally, the CPU workload must be predicted for each VM in the cloud center if predictions are processed using CPU workload information. As the number of VMs increases, the workload prediction task requires significantly more computing resources. Apart from CPU workload prediction, the task arrival workload can represent the task workload directly and reduce the prediction cost using the center node, which avoids the need for workload prediction regarding the distributed VMs in the cloud center. Thus, we focus on task arrival workload prediction, which represents a typical single-task forecasting problem. In contrast to one-step prediction, which utilizes actual historical data, multi-step prediction relies on uncertain forecasted values as inputs. Moreover, the additional predictions in multi-step forecasting offer limited practical value for cloud resource scheduling. Consequently, our focus is on short-term predictions, as the immediate forecast has a more direct and actionable impact on resource allocation. While LSTM has demonstrated strong performance in short-term, single-target time-series forecasting, due to the limitations of single models in capturing the complex patterns of volatile and seasonally varying workloads, we adopted a multi-model approach to predict the number of arriving tasks. Yet several key challenges remain, listed below:  
1. The task arrival workload only contains numerical information, and classifying the workload adaptively is the key problem. 2. Due to different workload categories, building sub-training data sets for training of the corresponding sub-models is another key problem. To address the challenges above, we propose a method involving multiple models based on an LSTM that can adaptively classify the workload into suitable categories and predict the workload using the corresponding sub-prediction model. Furthermore, to minimize the human and material costs associated with manually annotating two-category sub-training data sets, we propose an unsupervised machine learning method that utilizes workload trend features to automatically generate sub-training data sets for the sub-prediction model.