Ni Mingchang, Zhang Guo, Yang Qi, Yin Liqiong
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China.
Kunming Iron & Steel Holding Co., Ltd. Kunming 650302, China.
Math Biosci Eng. 2024 Jun 17;21(6):6336-6358. doi: 10.3934/mbe.2024276.
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.
计算卸载的决策过程是移动边缘计算的一个关键方面，各种卸载决策策略与移动边缘计算系统的计算延迟和能耗密切相关。本文针对边缘计算中的“边缘-终端”架构场景，提出了一种基于增强型正弦余弦优化算法（SCAGA）的卸载方案。本文的研究涵盖以下几个方面：（1）建立边缘计算场景的计算资源分配模型和计算成本模型；（2）引入一种基于莱维飞行策略正弦余弦优化算法原理构建的增强型正弦余弦优化算法，融入遗传算法中常见的轮盘赌选择和基因突变概念；（3）进行仿真实验以评估基于SCAGA的卸载方案，证明其有效降低系统延迟和优化卸载效用的能力。对比实验还突出了与其他卸载方案相比，该方案在系统延迟、移动用户能耗和卸载效用方面的改进。
