Deep Learning-Driven Workload Prediction and Optimization for Load Balancing in Cloud Computing Environment

Abstract Cloud computing revolutionizes as a technology that succeeds in serving large-scale user demands. Workload prediction and scheduling tend to be factors dictating cloud performance. Forecasting the future workload in due to avoid unfair resource allocation, emerges to be a crucial inspecting feature for enhanced performance. The aforementioned issues of interest are addressed in our work by soliciting a Deep Learning driven Max-out prediction model, which efficiently forecasts the future workload by providing a balanced approach for enhanced scheduling with the Tasmanian Devil-Bald Eagle Search (TDBES) optimization algorithm. The results obtained proved that the TDBES scored efficacy in makespan with 16.75%, migration cost with 14.78%, and a migration efficiency rate of 9.36% over other existing techniques like DBOA, WACO, and MPSO, with additional error analysis of prediction performance using RMSE, MAP, and MAE, among which our contributed approach overrides traditional methods with least error.