Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Science Direct
Abstract
Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and perfor-
mance 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 en-
ergy 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.
Description
Journal Article
Keywords
Mobile edge computing, Quality of experience, Task offloading, Communication networks, Particle swarm optimization