Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications

• Published in: Digital communications and networks Vol. 8; no. 6; pp. 1048 - 1058
• Main Authors: Mahenge, Michael Pendo John, Li, Chunlin, Sanga, Camilius A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2022; Department of Informatics and Information Technology,Sokoine University of Agriculture,Morogoro,3038,Tanzania; School of Computer Science and Technology,Wuhan University of Technology,Wuhan,Hubei,430063,China%School of Computer Science and Technology,Wuhan University of Technology,Wuhan,Hubei,430063,China%Department of Informatics and Information Technology,Sokoine University of Agriculture,Morogoro,3038,Tanzania; KeAi Communications Co., Ltd
• Subjects: Communication networks; Mobile edge computing; Particle swarm optimization; Quality of experience; Task offloading; Quality of experience; Communication networks; Particle swarm optimization; Mobile edge computing; Task offloading
• DOI: 10.1016/j.dcan.2022.04.001
• ISSN: 2352-8648

Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.