Time-Aware Workload Charactrization And Prediction For Proactive Auto-Scaling Of Web Applications

Proactive auto-scaling techniques aim to predict the future workload of web applications to provision the required resources, such as virtual machines (VMs), ahead of time. Nevertheless, deciding the optimal number of resources to allocate is a challenging task due to the dynamic nature of workload characteristics and the difficulty of predicting them. Most of the existing workload approaches only consider one workload feature which is typically the volume of requests to characterize and predict the workload. In this thesis, we report the design and development of a time aware workload prediction model that considers the request time features in order to achieve better workload characterization and prediction. We explore two different approaches, namely Time-Aware Single-Modeling and Time-Aware Multi-Modeling. The Time-Aware Single-Modeling approach builds one model for the entire time-space and has three variations: multivariate regression, univariate Long Short-Term Memory Neural Networks (LSTM), and multivariate LSTM neural network model. While, Time Aware Multi-Modeling approach develops a prediction model for each time partition discovered using a periodicity detection component. The proposed solutions are evaluated using two real workload datasets: Library portal at Qatar University and NewsLink portal in Pakistan. The results demonstrate that the time-aware approaches achieve more accurate predictions of the workload patterns compared to other existing approaches. Also, it has been shown that the achieved improvements are statistically different than existing approaches