AME-WPC: Advanced model for efficient workload prediction in the cloud

Abstract Workload estimation and prediction has become a very relevant research area in the field of cloud computing. The reason lies in its many benefits, which include QoS (Quality of Service) satisfaction, automatic resource scaling, and job/task scheduling. It is very difficult to accurately predict the workload of cloud applications if they are varying drastically. To address this issue, existing solutions use either statistical methods, which effectively detect repeating patterns but provide poor accuracy for long-term predictions, or learning methods, which develop a complex prediction model but are mostly unable to detect unusual patterns. Some solutions use a combination of both methods. However, none of them address the issue of gathering system-specific information in order to improve prediction accuracy. We propose an Advanced Model for Efficient Workload Prediction in the Cloud (AME-WPC), which combines statistical and learning methods, improves accuracy of workload prediction for cloud computing applications and can be dynamically adapted to a particular system. The learning methods use an extended training dataset, which we define through the analysis of the system factors that have a strong influence on the application workload. We address the workload prediction problem with classification as well as regression and test our solution with the machine-learning method Random Forest on both – basic and extended – training data. To evaluate our proposed model, we compare empirical tests with the machine-learning method kNN (k-Nearest Neighbors). Experimental results demonstrate that combining statistical and learning methods makes sense and can significantly improve prediction accuracy of workload over time.