Research Area:  Cloud Computing

Abstract:

In recent years, the usage of smart mobile applications to facilitate day-to-day activities in various domains for enhancing the quality of human life has increased widely. With rapid developments of smart mobile applications, the edge computing paradigm has emerged as a distributed computing solution to support serving these applications closer to mobile devices. Since the submitted workloads to the smart mobile applications changes over the time, decision making about offloading and edge server provisioning to handle the dynamic workloads of mobile applications is one of the challenging issues into the resource management scope. In this work, we utilized learning automata as a decision-maker to offload the incoming dynamic workloads into the edge or cloud servers. In addition, we propose an edge server provisioning approach using long short-term memory model to estimate the future workload and reinforcement learning technique to make an appropriate scaling decision. The simulation results obtained under real and synthetic workloads demonstrate that the proposed solution increases the CPU utilization and reduces the execution time and energy consumption, compared with the other algorithms.

Keywords:  

Author(s) Name:  Ali Shahidinejad, Mostafa Ghobaei-Arani

Publisher name:  Wiley

DOI:  10.1002/spe.2888

Volume Information:  Volume50, Issue12 December 2020,Pages 2212-2230

Paper Link:   https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.2888