Research on MEC computing offload strategy for joint optimization of delay and energy consumption

Mingchang Ni; Guo Zhang; Qi Yang; Liqiong Yin; Mingchang Ni; Guo Zhang; Qi Yang; Liqiong Yin

doi:10.3934/mbe.2024276

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 6: 6336-6358. doi: 10.3934/mbe.2024276

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Research on MEC computing offload strategy for joint optimization of delay and energy consumption

Mingchang Ni ¹,
Guo Zhang ^{1
,
,},
Qi Yang ^{2
,
,},
Liqiong Yin ²

1.
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China
2.
Kunming Iron & Steel Holding Co., Ltd. Kunming 650302, China

Received: 02 January 2024 Revised: 18 March 2024 Accepted: 16 May 2024 Published: 17 June 2024

The decision-making process for computational offloading is a critical aspect of mobile edge computing, and various offloading decision strategies are strongly linked to the calculated latency and energy consumption of the mobile edge computing system. This paper proposes an offloading scheme based on an enhanced sine-cosine optimization algorithm (SCAGA) designed for the "edge-end" architecture scenario within edge computing. The research presented in this paper covers the following aspects: (1) Establishment of computational resource allocation models and computational cost models for edge computing scenarios; (2) Introduction of an enhanced sine and cosine optimization algorithm built upon the principles of Levy flight strategy sine and cosine optimization algorithms, incorporating concepts from roulette wheel selection and gene mutation commonly found in genetic algorithms; (3) Execution of simulation experiments to evaluate the SCAGA-based offloading scheme, demonstrating its ability to effectively reduce system latency and optimize offloading utility. Comparative experiments also highlight improvements in system latency, mobile user energy consumption, and offloading utility when compared to alternative offloading schemes.
- mobil edge computing,
- computing offloading,
- computational resource allocation,
- sine and cosine optimization algorithms
Citation: Mingchang Ni, Guo Zhang, Qi Yang, Liqiong Yin. Research on MEC computing offload strategy for joint optimization of delay and energy consumption[J]. Mathematical Biosciences and Engineering, 2024, 21(6): 6336-6358. doi: 10.3934/mbe.2024276

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Abstract

The decision-making process for computational offloading is a critical aspect of mobile edge computing, and various offloading decision strategies are strongly linked to the calculated latency and energy consumption of the mobile edge computing system. This paper proposes an offloading scheme based on an enhanced sine-cosine optimization algorithm (SCAGA) designed for the "edge-end" architecture scenario within edge computing. The research presented in this paper covers the following aspects: (1) Establishment of computational resource allocation models and computational cost models for edge computing scenarios; (2) Introduction of an enhanced sine and cosine optimization algorithm built upon the principles of Levy flight strategy sine and cosine optimization algorithms, incorporating concepts from roulette wheel selection and gene mutation commonly found in genetic algorithms; (3) Execution of simulation experiments to evaluate the SCAGA-based offloading scheme, demonstrating its ability to effectively reduce system latency and optimize offloading utility. Comparative experiments also highlight improvements in system latency, mobile user energy consumption, and offloading utility when compared to alternative offloading schemes.

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