Based on the computation and storage resources limitation of single edge node and the demand for efficient computing services in big data scenarios

this paper proposes a deep reinforcement learning based cloud-edge collaborative computation offloading mechanism. Specifically

based on a comprehensive consideration of computing resources

bandwidth and offloading policy

an optimization problem is formulated to minimize the weight sum of execution delay and energy consumption of all user tasks. An asynchronous cloud-edge collaborative deep reinforcement learning (ACEC-DRL) algorithm is proposed to solve such optimization problem. This algorithm can effectively satisfy the demand of efficient computing services in big data scenario by jointly leveraging the computation capabilities of cloud and edge nodes. Meanwhile

under the various and dynamic environments of edge nodes in the edge cloud

this algorithm can adaptively adjust offloading policy to achieve the minimization of system cost. Finally

the extensive simulation results show that the proposed ACEC-DRL algorithm has the characteristics of fast convergence rate and high robustness

and its optimal offloading policy closely approximates to the solution of greedy algorithm with the lowest computation cost.