Hybrid deep learning and evolutionary algorithms for accurate cloud workload prediction

Cloud computing offers demand-based allocation of required resources to its clients ensuring optimal use of resources in a cost-effective manner. However, due to the massive increase in demand for physical resources by datacenters cloud management suffers from inefficient resource management. To enhance efficiency by reducing resource setup time, workload prediction has become an active research area. It helps to make management decisions proactively and enables the cloud management system to better respond to spikes in the workload. This study proposes a hybrid model combining both state-of-the-art deep learning models and evolutionary algorithms for workload prediction. The proposed cluster-based differential evolution neural network model utilizes differential evolution for the optimization of feature weights of the deep neural network to predict the future workloads of a cloud datacenter. The proposed model uses a novel mutation strategy that clusters the population based on an agglomerative technique and chooses the best gene from randomly chosen clusters. Thus, the strategy creates a balance between the exploration and exploitation of the population and enables the model to avoid local optima and converge rapidly. The datasets used for the experiments are created from Google’s real-world traces and the Alibaba platform. The model is compared with backpropagation, Adam optimizer-based LSTM, and an evolutionary neural network-based three-mutation policy. We evaluated the performance of the proposed model in terms of root mean squared error in predicting the upcoming CPU, RAM, and BW usage. The proposed model achieved an error rate as low as 0.0002 to outperform the existing studies in the relevant literature. To further authenticate the results, we performed the statistical analysis of the obtained results in terms of R-squared, mean bias deviation, 90th percentile score, and Theil’s U statistics. The high accuracy and automaticity of the proposed model have paved the way for its application in diverse areas of cloud computing, including real-time applications.