Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing

Yanpei Liu; Wei Huang; Liping Wang; Yunjing Zhu; Ningning Chen; Yanpei Liu; Wei Huang; Liping Wang; Yunjing Zhu; Ningning Chen

doi:10.3934/mbe.2021452

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 9163-9189. doi: 10.3934/mbe.2021452

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Research article Special Issues

Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing

School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China

Academic Editor: Fernando De la Prieta

Received: 19 July 2021 Accepted: 19 October 2021 Published: 25 October 2021

The current computation offloading algorithm for the mobile cloud ignores the selection of offloading opportunities and does not consider the uninstall frequency, resource waste, and energy efficiency reduction of the user's offloading success probability. Therefore, in this study, a dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in a multi-access edge computing environment is proposed (DCO-PSOMO). According to the CPU utilization and the memory utilization rate of the mobile terminal, this method can dynamically obtain the overload time by using a strong, locally weighted regression method. After detecting the overload time, the probability of successful downloading is predicted by the mobile user's dwell time and edge computing communication range, and the offloading is either conducted immediately or delayed. A computation offloading model was established via the use of the response time and energy consumption of the mobile terminal. Additionally, the optimal computing offloading algorithm was designed via the use of a particle swarm with a mutation operator. Finally, the DCO-PSOMO algorithm was compared with the JOCAP, ECOMC and ESRLR algorithms, and the experimental results demonstrated that the DCO-PSOMO offloading method can effectively reduce the offloading cost and terminal energy consumption, and improves the success probability of offloading and the user's QoS.
- computation offloading,
- multi-access edge computing,
- offloading success rate,
- overload time
Citation: Yanpei Liu, Wei Huang, Liping Wang, Yunjing Zhu, Ningning Chen. Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9163-9189. doi: 10.3934/mbe.2021452

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Abstract

The current computation offloading algorithm for the mobile cloud ignores the selection of offloading opportunities and does not consider the uninstall frequency, resource waste, and energy efficiency reduction of the user's offloading success probability. Therefore, in this study, a dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in a multi-access edge computing environment is proposed (DCO-PSOMO). According to the CPU utilization and the memory utilization rate of the mobile terminal, this method can dynamically obtain the overload time by using a strong, locally weighted regression method. After detecting the overload time, the probability of successful downloading is predicted by the mobile user's dwell time and edge computing communication range, and the offloading is either conducted immediately or delayed. A computation offloading model was established via the use of the response time and energy consumption of the mobile terminal. Additionally, the optimal computing offloading algorithm was designed via the use of a particle swarm with a mutation operator. Finally, the DCO-PSOMO algorithm was compared with the JOCAP, ECOMC and ESRLR algorithms, and the experimental results demonstrated that the DCO-PSOMO offloading method can effectively reduce the offloading cost and terminal energy consumption, and improves the success probability of offloading and the user's QoS.

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