Forecasting of Cloud Computing Services Workload using Machine Learning
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Abstract
This paper analyses and compares prediction accuracy of different machine learning algorithms intended to forecast the workloads of server logs. The proposed prediction model conducts comparative study has been applied using Linear Regression (LR), K- Nearest Neighbors (KNN), Support Vector Machine (SVM), ARMA, ARIMA, and Support Vector Regression (SVR) for web applications to select the suitable algorithm as per workload features. The experiments have used real trace files to evaluate the best suitable method to predict the workloads. The experimental results describe that the ARIMA model shows significant improvement in QoS metrics and improve the cloud datacenter availability in a cloud environment and forecasting. Finally results presented and conclusions are drawn.
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