计算卸载
计算机科学
移动边缘计算
云计算
边缘计算
强化学习
能源消耗
分布式计算
计算
移动设备
移动云计算
传输延迟
GSM演进的增强数据速率
传输(电信)
算法
人工智能
操作系统
生物
电信
生态学
作者
Yan Wang,Haibo Ge,Feng Anqi,Wenhao Li,Linhuan Liu,Haobo Jiang
出处
期刊:International Conference on Cloud Computing
日期:2020-04-10
被引量:8
标识
DOI:10.1109/icccbda49378.2020.9095689
摘要
Mobile edge computing (MEC) is a new computing paradigm that migrates rich computing and storage resources to the edge of the network. However, compared with traditional cloud computing, mobile edge computing is constrained in computing capacity, especially under the scenario of dense population. In this paper, a Cloud-Assisted Mobile Edge (CAME) computing framework is used to study the problem of computation offloading and resource allocation. First, the transmission delay as well as computation delay that computation jobs may experience, the transmission energy as well as computation energy that the computing system would consume were modeled. Then, the weighted sum of the delay and energy-efficient minimization computation offloading problem was formulated, constrained to the maximum latency and server resources. After that, a DQN algorithm based on reinforcement learning is proposed. In order to avoid the problem of excessive state space and overestimation, a DDQN offloading algorithm is proposed. Simulation results show that the offloading algorithm DDQN proposed in this paper can reduces the weighted sum of delay and energy consumption effectively.
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