Multivariate time series ensemble model for load prediction on hosts using anomaly detection techniques

Host load prediction is essential in computing to improve resource utilization and for achieving service level agreements. However, due to variations in load and the inefficiency of feature extraction, prediction of load on hosts in fog computing an immense challenge. A predictive model with variable load patterns can better estimate future resource needs, which is crucial for capacity planning, service-level goals and energy efficiency. To improve workload prediction accuracy, proposed framework proposes a time-series-based multivariate ensemble model using anomaly detection techniques. In the proposed work, virtual machines are deployed on different platforms and numerous parameters such as CPU utilization, number of cores, RAM, allocated memory, available memory, disk I/O and network I/O are extracted. There may exist inconsistencies in load prediction due to the enormous volume of data. To reduce redundancy in data, various anomaly detection techniques are utilized. The performance of the proposed ensemble model is compared with various time series models using Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and Mean Absolute Percentage Error (MAPE) and Accuracy. Moreover, the effectiveness of the proposed ensemble model is demonstrated on the generated dataset and performance is measured based on the performance evaluation metrics. The ensemble model exhibits higher accuracy in workload prediction as compared to the current state-of-the-art models, it achieves the lowest MAPE and provides an accuracy of about 88%.