Mobile edge computing(MEC)deploys servers with capabilities functions such as computation and storage at the edge of the network to satisfy certain tasks with demanding latency requirements.In order to meet the requirements of real-time task processing in industrial scenarios,we consider the offloading decisions of multi-user Directed Acyclic Graph(DAG)tasks,communication bandwidth and online allocation of computational resources.By constructing the Markov decision pro-cess model,this paper adopted DQN based on reinforcement discrete action space collaborating with TD3 based on continuous action space network to optimize the binary task offloading decision of DAG nodes and bandwidth computational resources al-location,aiming to maximize the success rate of long-term real-time task offloading.The simulation results show that the DQN+TD3 algorithm adopted in this paper has the highest success rate of real-time task offloading,which verifies the effec-tiveness of the algorithm.
维普
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