Energy-Efficient Computation Offloading with Multi-MEC Servers in 5G Two-Tier Heterogeneous Networks

Abstract

Recently, Multi-Access Edge Computing (MEC), which has been emerged as a key technology in 5G networks, enhances computation capabilities and power limitations of mobile devices (MDs) by offloading computation task to the nearby MEC servers. However, offloading the computation tasks can increase network traffics and incur extra delays. Most existing approaches focus on the computation offloading with multi-user single-MEC scenarios to decrease energy consumption and latency of the MDs. Towards this goal, we investigate a computation offloading strategy for two-tier 5G heterogeneous networks integrated with multi-MEC. In addition, we propose a random offloading search algorithm, called ROSA, that rapidly achieve the minimized energy consumption of the system considering the computation offloading decision strategies. Simulation results show that our proposed algorithm based on offloading scheme outperforms other two schemes in terms of energy consumption.