基于用户延迟感知的移动边缘服务器放置方法

物联网和5G网络的快速发展产生了大量数据,通过将计算任务从移动设备卸载到具有足够计算资源的边缘服务器上,可有效减少网络拥塞和数据传播延迟等问题。边缘服务器放置是任务卸载的核心,高效的边缘服务器放置方法能有效满足移动用户访问低时延、高带宽等需求。为此,文中以最小化访问延迟和最小化负载差异为优化目标,建立边缘服务器放置优化模型;然后,提出了一种基于改进启发式算法的移动边缘服务器放置方法ESPHA(Edge Server Placement Based on Heuristic Algorithm),实现多目标优化。首先将K-means算法与蚁群算法相结合,通过效仿蚁群在觅食过程中共享信息素,将信息素反馈机制引入边缘服务器放置方法中,然后,通过设置禁忌表对蚁群算法进行改进,提高算法的收敛速度;最后,用改进的启发式算法求解模型的最优放置方案。使用上海电信真实数据集进行实验,结果表明提出的ESPHA方法在保证服务质量的前提下取得了低延迟和负载均衡之间的优化平衡,其效果优于现有的其他几种代表性的方法。

Mobile Edge Server Placement Method Based on User Latency-aware

The rapid development of the Internet-of-Things and 5G networks generates a large amount of data.By offloading computing tasks from mobile devices to edge servers with sufficient computing resources,network congestion and data propagation delays can be effectively reduced.The placement of edge server is the core of task offloading,and efficient placement method can effectively satisfy the needs of mobile users to access services with low latency and high bandwidth.To this end,an optimization model of edge server placement is established through minimizing both access delay and load difference as the optimization goal.Then,based on the heuristic algorithm,a mobile edge server placement method called ESPHA(Edge Server Placement Method Based on Heuristic Algorithm)is proposed to achieve multi-objective optimization.Firstly,the K-means algorithm is combined with the ant colony algorithm,the pheromone feedback mechanism is introduced into the placement method by emulating the mechanism of ant colony sharing pheromone in the foraging process,and the ant colony algorithm is improved by setting the taboo table to improve the convergence speed.Finally,the improved heuristic algorithm is used to solve the optimal placement.Experiments using Shanghai Telecom’s real datasets show that the proposed method achieves an optimal balance between low latency and load balancing under the premise of guaranteeing quality of service,and outperforms several existing representative methods.