Workload prediction for SLA performance in cloud environment: ESANN approach

Abstract

Cloud computing offers internet-based services to customers. Infrastructure as a service offers consumers virtual computer resources including networking, hardware, and storage. Cloud-hosting startup delays hardware resource allocation by several minutes. Predicting computer demand will address this problem. The performance comparison showed that combining these algorithms was the best way to create a dynamic cloud data centre that efficiently used its resources. One of these challenges is the need of practicing effective SLA management in order to prevent the possibility of SLA breaches and the repercussions of such violations. Exponential Smoothing and Artificial Neural Network (ANN) models in terms of managing SLAs from the point of view of cloud customers as well as cloud providers. We proposed an Exponential Smoothing and Artificial Neural Network model (ESANN) for SLA violation and predict the CPU utilization from time series data. This model includes SLA monitoring, energy consumption, CPU utilization, and accuracy prediction. Experiments show that the suggested approach helps cloud providers reduce service breaches and penalties. ESANN outperforms Exponential Smoothing, LSTM, RACC-MDT, and ARIMA by attaining 6.28%, 16.2%, 27.33%, and 31.2% on the combined performance indicator of Energy SLA Violation, which measures both energy consumption and SLA compliance.