.png){kind=icon}
.png){kind=icon}
Return
Quantum Particle Swarm Optimization for Task Offloading in Mobile Edge Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2022.3225313.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Mobile edge computing (MEC) deploys servers on the edge of the mobile network to reduce the data transmission delay between servers and mobile devices, and can meet the computing demand of mobile computing tasks. It alleviates the problem of computing power and delay requirements of mobile computing tasks and reduces the energy consumption of mobile devices. However, the MEC server has limited computing and storage resources and mobile network bandwidth, making it impossible to offload all mobile computing tasks to MEC servers for processing. Therefore, MEC needs to reasonably offload and schedule mobile computing tasks, to achieve efficient utilization of server resources. To solve the above-mentioned problems, in this article, the task offloading problem is formulated as an optimization problem, and particle swarm optimization (PSO) and quantum PSO based task offloading strategies are proposed. Extensive simulation results show that the proposed algorithm can significantly reduce the system energy consumption, task completion time, and running time compared with recent advanced strategies, namely ant colony optimization, multiagent deep deterministic policy gradients, deep meta reinforcement learning-based offloading, iterative proximal algorithm, and parallel random forest.
Keywords:
Task analysis
Servers
Energy consumption
Delays
Optimization
Heuristic algorithms
Resource management
Mobile edge computing (MEC)
particle swarm optimization (PSO)
quantum PSO (QPSO)
task offloading
Journal
IF:
9.9
Papers:
8.3K
Citations:
6.0W
Organization
View more organizations