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Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing
Mobile Information Systems ( IF 1.863 ) Pub Date : 2021-06-21 , DOI: 10.1155/2021/9911332 Runfu Liang 1 , Gaocai Wang 1 , Jintian Hu 2
Mobile Information Systems ( IF 1.863 ) Pub Date : 2021-06-21 , DOI: 10.1155/2021/9911332 Runfu Liang 1 , Gaocai Wang 1 , Jintian Hu 2
Affiliation
As computing-intensive mobile applications become increasingly diversified, mobile devices’ computing power is hard to keep up with demand. Mobile devices migrate tasks to the Mobile Edge Computing (MEC) platform and improve the performance of task processing through reasonable allocation and caching of resources on the platform. Small cellular networks (SCN) have excellent short-distance communication capabilities, and the combination of MEC and SCN is a promising research direction. This paper focuses on minimizing energy consumption for task migration in small cellular networks and proposes a task migration energy optimization strategy with resource caching by combining optimal stopping theory with migration decision-making. Firstly, the process of device finding the MEC platform with the required task processing resources is formulated as the optimal stopping problem. Secondly, we prove an optimal stopping rule’s existence, obtain the optimal processing energy consumption threshold, and compare it with the device energy consumption. Finally, the platform with the best energy consumption is selected to process the task. In the simulation experiment, the optimization strategy has lower average migration energy consumption and higher average data execution energy efficiency and average distance execution energy efficiency, which improves task migration performance by 10% ∼ 60%.
中文翻译:
移动边缘计算下小蜂窝网络的资源缓存和任务迁移策略
随着计算密集型移动应用越来越多样化,移动设备的计算能力难以跟上需求。移动设备将任务迁移到移动边缘计算(MEC)平台,通过平台上资源的合理分配和缓存来提高任务处理的性能。小型蜂窝网络(SCN)具有出色的短距离通信能力,MEC与SCN的结合是一个很有前景的研究方向。本文着眼于最小化小蜂窝网络中任务迁移的能量消耗,并通过将最优停止理论与迁移决策相结合,提出了一种具有资源缓存的任务迁移能量优化策略。首先,设备寻找具有所需任务处理资源的 MEC 平台的过程被表述为最优停止问题。其次,我们证明了最优停止规则的存在,得到最优处理能耗阈值,并与设备能耗进行比较。最后,选择能耗最好的平台来处理任务。在仿真实验中,优化策略具有更低的平均迁移能耗和更高的平均数据执行能效和平均距离执行能效,使任务迁移性能提高了10% ∼ 60%。选择能耗最好的平台来处理任务。在仿真实验中,优化策略具有更低的平均迁移能耗和更高的平均数据执行能效和平均距离执行能效,使任务迁移性能提高了10% ∼ 60%。选择能耗最好的平台来处理任务。在仿真实验中,优化策略具有更低的平均迁移能耗和更高的平均数据执行能效和平均距离执行能效,使任务迁移性能提高了10% ∼ 60%。
更新日期:2021-06-21
中文翻译:
移动边缘计算下小蜂窝网络的资源缓存和任务迁移策略
随着计算密集型移动应用越来越多样化,移动设备的计算能力难以跟上需求。移动设备将任务迁移到移动边缘计算(MEC)平台,通过平台上资源的合理分配和缓存来提高任务处理的性能。小型蜂窝网络(SCN)具有出色的短距离通信能力,MEC与SCN的结合是一个很有前景的研究方向。本文着眼于最小化小蜂窝网络中任务迁移的能量消耗,并通过将最优停止理论与迁移决策相结合,提出了一种具有资源缓存的任务迁移能量优化策略。首先,设备寻找具有所需任务处理资源的 MEC 平台的过程被表述为最优停止问题。其次,我们证明了最优停止规则的存在,得到最优处理能耗阈值,并与设备能耗进行比较。最后,选择能耗最好的平台来处理任务。在仿真实验中,优化策略具有更低的平均迁移能耗和更高的平均数据执行能效和平均距离执行能效,使任务迁移性能提高了10% ∼ 60%。选择能耗最好的平台来处理任务。在仿真实验中,优化策略具有更低的平均迁移能耗和更高的平均数据执行能效和平均距离执行能效,使任务迁移性能提高了10% ∼ 60%。选择能耗最好的平台来处理任务。在仿真实验中,优化策略具有更低的平均迁移能耗和更高的平均数据执行能效和平均距离执行能效,使任务迁移性能提高了10% ∼ 60%。