Improved deep network-based load predictor and optimal load balancing in cloud-fog services

Abstract

Cloud computing is commonly utilized in remote contexts to handle user demands for resources and services. Each assignment has unique processing needs that are determined by the time it takes to complete. However, if load balancing is not properly managed, the effectiveness of resources may suffer dramatically. Consequently, cloud service providers have to emphasize rapid and precise load balancing as well as proper resource supply. This paper proposes a novel enhanced deep network-based load predictor and load balancing in cloud-fog services. In prior, the workload is predicted using a deep network called Multiple Layers Assisted in LSTM (MLA-LSTM) model that considers the capacity of virtual machine (VM) and task as input and predicts the target label as underload, overload and equally balanced. According to this prediction, the optimal load balancing is performed through a hybrid optimization named Osprey Assisted Pelican Optimization Algorithm (OAPOA) while taking into account several parameters such as makespan, execution cost, resource consumption, and server load. Additionally, a process known as load migration is carried out, in which machines with overload tasks are assigned to machines with underload tasks. This migration is applied optimally via the OAPOA strategy under the consideration of constraints including migration cost and migration efficiency.