Abstract
As a new form of computing based on the core technology of cloud computing and built on edge infrastructure, edge computing can handle computing-intensive and delay-sensitive tasks. In mobile edge computing (MEC) assisted by 5G technology, offloading computing tasks of edge devices to the edge servers in edge network can effectively reduce delay. Designing a reasonable task offloading strategy in a resource-constrained multi-user and multi-MEC system to meet users’ needs is a challenge issue. In industrial internet of things (IIoT) environment, considering the rapid increase of industrial edge devices and the heterogenous edge servers, a particle swarm optimization (PSO)-based task offloading strategy is proposed to offload tasks from resource-constrained edge devices to edge servers with energy efficiency and low delay style. A multi-objective optimization problem that considers time delay, energy consumption and task execution cost is proposed. The fitness function of the particle represents the total cost of offloading all tasks to different MEC servers. The offloading strategy based on PSO is compared with the genetic algorithm (GA) and the simulated annealing algorithm (SA) through simulation experiments. The experimental results show that the task offloading strategy based on PSO can reduce the delay of the MEC server, balance the energy consumption of the MEC server, and effectively realize the reasonable resource allocation.
Highlights
The fifth-generation mobile communication system, the so-called 5G, has become a hot topic in the industry
In this paper, we evaluate three offloading strategies: offloading strategy based on genetic algorithm (GA), offloading strategy based on simulated annealing algorithm (SA), and offloading strategy based on particle swarm optimization (PSO)
We introduced three algorithms: particle swarm optimization algorithm, genetic algorithm, and simulated annealing algorithm, and modeled the task offloading problem in the industrial internet of things (IIoT) environment as a multi-user and multi-mobile edge computing (MEC) problem
Summary
The fifth-generation mobile communication system, the so-called 5G, has become a hot topic in the industry. In order to cope with the problems of insufficient processing capacity and limited resources of smart devices, the industry has introduced the concept of computation offloading in mobile edge computing (MEC) [1, 2]. In order to use the services provided by the edge network, how to offload the tasks undertaken by the smart device to the edge server and make efficient and reasonable offloading decisions is the main research direction of the current edge computing problem. The problems that need to be tackled are how to offload the tasks from smart devices to the MEC servers nearby, and allocate the MEC server’s computing resources to ensure processing efficiency, guaranteeing the task latency requirements.
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