With the advent of mobile Internet and IoT era, various smart terminals generate a large amount of data at the edge of the network, and how to transmit and process these data at high speed poses a challenge to the traditional communication networks. Edge computing, as an emerging framework, can improve the communication capability and data processing capacity of traditional communication networks by improving their architecture. Edge server placement (ESP) technology is one of the key technologies of edge computing, which can effectively reduce data transmission delay and improve data processing efficiency by placing edge servers (ESs) with computing and data storage functions at base stations to sink some functions of the core network to the edge of the network. In this paper, we study the k edge servers placement problem (KESP problem) in smart cities. We first elaborate it as a multi-objective optimization problem for optimal workload balancing and system delay under constraints. Then a modified multi-objective non-dominated sorting genetic algorithm with elite policy (MNSGA-II) is proposed to optimize this problem. Finally, simulations are performed based on real network datasets. The simulation results show that MNSGA-II reduces the system overhead by about 38.4%, 40.6%, and 59.3% on average compared to Random, K-Means, and Top-K.

随着移动互联网和物联网时代的到来，各种智能终端在网络边缘产生大量数据，如何高速传输和处理这些数据，对传统通信网络提出了挑战。边缘计算作为一种新兴的框架，可以通过改进传统通信网络的架构来提高其通信能力和数据处理能力。边缘服务器放置（ESP）技术是边缘计算的关键技术之一，通过在基站放置具有计算和数据存储功能的边缘服务器（ES）来下沉部分功能，可以有效降低数据传输延迟，提高数据处理效率。核心网到网络边缘。在本文中，我们研究了k智慧城市中的边缘服务器放置问题（KESP 问题）。我们首先将其阐述为一个多目标优化问题，用于在约束条件下实现最佳工作负载平衡和系统延迟。然后提出一种改进的具有精英策略的多目标非支配排序遗传算法（MNSGA-II）来优化该问题。最后，基于真实网络数据集进行模拟。仿真结果表明，与Random、K-Means和Top-K相比，MNSGA-II平均降低了约38.4%、40.6%和59.3%的系统开销。