To reduce the delay and energy consumption of application execution,a task offloading strategy based on deep rein-forcement learning in edge-cloud collaboration scenario was proposed for mobile edge computing environment.The network model,communication model and calculation model under the edge-cloud collaborative architecture were established,and taking the minimization of delay and energy consumption as the system goal,the DQN offloading strategy based on deep reinforcement learning was designed,the tasks generated by each user were placed independently and efficiently in the local,server or cloud for calculation,and the calculation results were compared with other methods.The results show that compared with other baseline algorithms,this method can more effectively reduce the cost of task execution and obtain a better offloading strategy.
关键词
移动边缘计算/任务卸载/边云协同/深度强化学习/系统模型/时延/能耗
Key words
mobile edge computing/task offloading/edge-cloud collaboration/deep reinforcement learning/system model/time delay/energy consumption
维普
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