基于时延与能量感知的边缘服务器放置方法

针对移动边缘计算中无线城域网环境下的边缘服务器放置（WESP）问题，建立时延和能耗模型并将WESP问题转化为带约束条件的单目标优化问题，进而提出一种基于混沌麻雀搜索算法的边缘服务器放置方法。使用精英反向学习策略初始化种群，增加初始种群的多样性，加快算法搜索速度。通过设计新的个体编码方式准确描述WESP问题，优化算法更新过程。采用逻辑混沌映射策略改进麻雀个体，保证迭代后期的种群多样性，加快算法收敛速度。仿真结果表明，与主流放置方法相比，该方法在时延和能耗优化方面表现突出，并且系统开销下降了18.1%。

Edge Server Placement Method Based on Time Delay and Energy Awareness

To address the Edge Server(ES) placement problem in the Wireless Metropolitan Area Network(WMAN) environment of mobile edge computing,the time delay and energy consumption of servers are modeled to simplify the WESP problem into a single-objective optimization problem with constraints.On this basis,an edge server placement method based on the Chaotic Sparrow Search Algorithm(CSSA) is proposed.The method uses an Elite Opposition-Based Learning(EOBL) method to initialize the population,which increases the diversity of the initial population and speeds up the search of the algorithm.Then a new individual coding method is proposed to effectively describe the WESP problem and improve the update process of the algorithm.The sparrow individuals are improved by using the logical chaotic mapping method to ensure the population diversity at the later stage of the iteration and accelerate the convergence of the algorithm.Simulation results indicate excellent performance of the proposed method in optimization time delay and energy consumption compared with the mainstream placement methods,and a reduction of 18.1% in system overhead.