Deep Learning Approach for Workload Prediction and Balancing in Cloud Computing

Abstract

Cloud Computing voted as one of the most revolutionized technologies serving huge user demand engrosses a prominent place in research. Despite several parameters that influence the cloud performance, factors like Workload prediction and scheduling are triggering challenges for researchers in leveraging the system proficiency. Contributions by practitioners given workload prophesy left scope for further enhancement in terms of makespan, migration efficiency, and cost. Anticipating the future workload in due to avoid unfair allocation of cloud resources is a crucial aspect of efficient resource allocation. Our work aims to address this gap and improve efficiency by proposing a Deep Max-out prediction model, which predicts the future workload and facilitates workload balancing paving the path for enhanced scheduling with a hybrid Tasmanian Devil-assisted Bald Eagle Search (TES) optimization algorithm. The results evaluated proved that the TES scored efficiency in makespan with 16.342%, and migration efficiency of 14.75% over existing approaches like WACO, MPSO, and DBOA (Weighted Ant Colony Optimization Modified Particle Swarm Optimization, Discrete Butterfly Optimization Algorithm). Similarly, the error analysis during the evaluation of prediction performance has been figured out using different approaches like MSE, RMSE, MAE, and MSLE, among which our proposed method overwhelms with less error than the traditional methods.