Abstract: With the popularization and application of the Internet of Things and the vehicle network, the data at the near user end (data source end) has shown an explosive growth. To effectively deal with these rapidly growing data, mobile edge computing has emerged as a new computing model. Mobile edge computing refers to sinking some resources in the cloud center to the edge of the network, so that data can be processed at the edge of the network. How to efficiently offload tasks and allocate resources reasonably is a hot issue in the field of mobile edge computing research. However, most of the existing studies ignore the security of edge data and computing nodes and only guarantees the security of data and information, and the mobile edge computing can develop comprehensively. Therefore, based on data security, combined with deep reinforcement learning, a task offloading and resource allocation algorithm is proposed under multiple constraints. Experimental results show that, compared with several classic algorithms, the algorithm can effectively improve the task offloading success rate and task successful execution rate, reduces the local energy consumption, and better meets the user's QoS requirements.

Key words: mobile edge computing, task offloading, resource allocation, deep reinforcement learning, QoS