School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Zhang Fengli
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Ayantu Tesfaye Kenea
Department of Computer Science and Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama P.O. Box 1888, Ethiopia
Negalign Wake Hundera
School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
Tewodros Gizaw Tohye
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Abebe Tamrat Tegene
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
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.
