Using Gray Wolf Optimization for Joint Request Offloading and Resource Scheduling in 5G Network which Use Mobile Edge Computing

Safaa Alali; Abdulkarim  Assalem; Boushra Alali

doi:10.37934/arca.37.1.6586

Authors

Safaa Alali Department of Electronic and Communication Engineering, Faculty of Electrical and Mechanical Engineering, Al-Baath University, Homs, Syria.
Abdulkarim Assalem Department of Electronic and Communication Engineering, Faculty of Electrical and Mechanical Engineering, Al-Baath University, Homs, Syria.
Boushra Alali Software Engineering, Faculty of Information Engineering, Al-Baath University, Homs, Syria

DOI:

https://doi.org/10.37934/arca.37.1.6586

Keywords:

Mobile Edge Computing, 5G networks, gray wolf optimization, computational offloading

Abstract

Mobile Edge Computing (MEC) is considered one of the enabling and promising technologies in 5G networks, especially with the massive data movement of various devices and the increased demand for computing. Here, computational offloading of tasks to edge clouds provides an effective, flexible, low-latency solution for mobile users in the network. However, the limited computing resources in edge clouds and the dynamic demands of mobile users make it difficult to schedule computing requests to appropriate edge clouds. Therefore, we model the joint request offloading and resource scheduling (JRORS) problem as a mixed- nonlinear program to maximize the system welfare of requests. Then we proposed gray wolf optimization algorithm referred as (GWO) to solve this problem. The simulation results showed that (GWO) outperforms existing methods in terms of system welfare and can maintain good performance in a dynamic network.