PSO-Based Ensemble Meta-Learning Approach for Cloud Virtual Machine Resource Usage Prediction
Habte Lejebo Leka, Zhang Fengli, Ayantu Tesfaye Kenea, Negalign Wake Hundera, Tewodros Gizaw Tohye, Abebe Tamrat Tegene
Abstract
To meet the increasing demand for its services, a cloud system should make optimum use of its available resources. Additionally, the high and low oscillations in cloud workload are another significant symmetrical issue that necessitates consideration. A suggested particle swarm optimization (PSO)-based ensemble meta-learning workload forecasting approach uses base models and the PSO-optimized weights of their network inputs. The proposed model employs a blended ensemble learning strategy to merge three recurrent neural networks (RNNs), followed by a dense neural network layer. The CPU utilization of GWA-T-12 and PlanetLab traces is used to assess the method’s efficacy. In terms of RMSE, the approach is compared to the LSTM, GRU, and BiLSTM sub-models.