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An Optimization Method for Mobile Edge Service Migration in Cyberphysical Power System
Wireless Communications and Mobile Computing ( IF 2.146 ) Pub Date : 2021-02-15 , DOI: 10.1155/2021/6610654
Qian Cao 1 , Qilin Wu 1 , Bo Liu 1 , Shaowei Zhang 2 , Yiwen Zhang 3
Affiliation  

To relieve the pressure of processing computation-intensive applications on mobile devices and avoid high latency during data transmission, edge computing is proposed to solve this problem. Mobile edge computing (MEC) allows the deployment of MEC servers at the edge of the network to interact with users on the premise of low transmission delay, thereby improving the quality of service (QoS) for users. However, due to the high mobility of users, with the continuous change of geographical location, when users exceed the signal range of the MEC server, the services they request on the MEC server will also be migrated to other MEC servers. The handoff process may involve high response delays caused by service forwarding, thereby greatly degrading QoS. For the above problems, in this paper, a service migration optimization method based on transmission power control is proposed. First, according to the transmission power of the MEC server, the user’s activity range is divided into multiple subregions based on a Voronoi diagram. Therefore, there is one MEC server in each subregion, and the size of each subregion is adjusted by controlling the transmission power of the MEC server to minimize the number of wireless handoffs and the energy consumption of the MEC server. Then, the particle swarm optimization (PSO) is adopted to solve the above multiobjective optimization problem. Finally, the effectiveness of the proposed method is verified through extensive experiments.

中文翻译:

网络物理电源系统中移动边缘服务迁移的优化方法

为了减轻移动设备上处理计算密集型应用程序的压力并避免数据传输期间的高延迟,提出了边缘计算来解决此问题。移动边缘计算(MEC)允许在低传输延迟的前提下在网络边缘部署MEC服务器与用户进行交互,从而提高用户的服务质量(QoS)。但是,由于用户的高度移动性,随着地理位置的不断变化,当用户超出MEC服务器的信号范围时,他们在MEC服务器上请求的服务也将迁移到其他MEC服务器。切换过程可能涉及由服务转发引起的高响应延迟,从而大大降低了QoS。针对上述问题,本文 提出了一种基于传输功率控制的业务迁移优化方法。首先,根据MEC服务器的传输功率,基于Voronoi图将用户的活动范围划分为多个子区域。因此,每个子区域中只有一个MEC服务器,并且通过控制MEC服务器的传输功率来调整每个子区域的大小,以最大程度地减少无线切换的次数和MEC服务器的能耗。然后,采用粒子群算法(PSO)解决了上述多目标优化问题。最后,通过大量实验验证了该方法的有效性。根据Voronoi图,将用户的活动范围分为多个子区域。因此,每个子区域中只有一个MEC服务器,并且通过控制MEC服务器的传输功率来调整每个子区域的大小,以最大程度地减少无线切换的次数和MEC服务器的能耗。然后,采用粒子群算法(PSO)解决了上述多目标优化问题。最后,通过大量实验验证了该方法的有效性。根据Voronoi图,将用户的活动范围分为多个子区域。因此,每个子区域中只有一个MEC服务器,并且通过控制MEC服务器的传输功率来调整每个子区域的大小,以最大程度地减少无线切换的次数和MEC服务器的能耗。然后,采用粒子群算法(PSO)解决了上述多目标优化问题。最后,通过大量实验验证了该方法的有效性。采用粒子群算法(PSO)解决了上述多目标优化问题。最后,通过大量实验验证了该方法的有效性。采用粒子群算法(PSO)解决了上述多目标优化问题。最后,通过大量实验验证了该方法的有效性。
更新日期:2021-02-15
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