LAUSR

Mobile edge computing (MEC) introduced a way for mobile users to acquire the benefits of cloud computing and satisfy the continuous growth of data demands. Still, amidst the evolutionary development of cloud computing and MEC, the wireless bandwidth and mobile devices limitations present numerous obstacles which limit the system efficiency, including the energy consumption and latency, these restrictions must be eliminated to realize the determined low energy and millisecond-scale latency for 5G. In this paper, an “Active queue management-based green cloud model for mobile edge computing” referred to as ‘AGCM’ is proposed for 5G to address this issue, in which the mobile users are served more efficiently with less energy waste at both the cloud and the mobile devices and reduced latency. The proposed model achieves this by alleviating the congestion in the cloud by utilizing the enhanced random early detection algorithm and implementing a virtual list to store the packets information and smartly prioritize and serve the packets. The simulation results, implemented in NS2 Green Cloud Simulator, attested that AGCM compared to the conventional cloud and femtolet model provided enhancement in the energy consumption by 90.6% and 24.6% respectively, the results also shows that AGCM can reduce the latency by 84% and 65% than the conventional cloud and femtolet model respectively. The quality of service also improved as the throughput is increased by 420% and 3.48% compared with cloud and femtolet respectively.