摘要
在移动边缘计算系统中,通过将计算任务从移动设备迁移到移动边缘计算服务器,可以大幅度提高计算质量。考虑将可再生能源纳入多用户移动边缘计算系统中,并在模型中加入电池作为能量收集装置以实现能量收集和存储。通过提出的基于强化学习的资源管理算法制定了移动边缘计算系统中的任务分配策略,实现了移动设备成本最优化(包括时延成本和能耗成本)。仿真结果表明,与其他算法相比,该算法显著减少了移动设备的成本。
In mobile edge computing system,the quality of computing experience can be improved greatly by offloading computing tasks from mobile devices to mobile edge computing servers.Consider incorporating renewable energy into a multi-user mobile edge system.Moreover,a battery as an energy harvesting device was added to the model to harvest energy and storage.The task allocation strategy in mobile edge computing system was formulated through the resource management algorithm based on reinforcement learning,which achieved the cost minimization of mobile devices(including delay cost and computing cost).The simulation results show that the proposed algorithm significantly minimizes the cost of mobile devices compared with other algorithms。
作者
王璐瑶
张文倩
张光林
WANG Luyao;ZHANG Wenqian;ZHANG Guanglin(College of Science Technology and Information,DongHua University,Shanghai 201620,China)
出处
《物联网学报》
2019年第1期73-81,共9页
Chinese Journal on Internet of Things
关键词
能量收集
可再生能源
移动边缘计算
成本优化
强化学习
energy harvesting
renewable energy
mobile edge computing
cost optimization
reinforcement learning
