Abstract: In the video task processing of cloud-edge collaboration, due to the existence of a large number of processing and transmission tasks, it is necessary to consider the success rates of task proces- sing and the processing time of tasks to ensure the quality of service. At the same time, various resource costs  need to be taken into account to save system operation costs. To address the above issues, this paper formally models the video task scheduling problem under the cloud-edge collaborative framework and transforms it into a multi-objective optimization problem. For this problem, an algorithm called OCES is proposed. This algorithm sorts tasks within the same time slice to determine task priorities. For each task, it combines task information with the current status information of each edge node and cloud center node, and uses a neural network to judge and select the strategy with the maximum Q-value for scheduling, so as to specify the specific execution node of the task. OCES is an algorithm based on DDQN, which improves the reward function and strategy selection method. By integrating a noise network into the deep neural network, it avoids the algorithm from converging to a local optimal solution prematurely. Compared with the current internationally advanced CPSA algorithm, the proposed algorithm reduces the execution cost by 10.56% and 5.85% respectively in two scenarios with different average arrival rates and different task types, while achieving similar success rates and completion times.

Key words: cloud-edge collaboration, task scheduling, deep reinforce learning, double deep Q-network(DDQN) algorithm, noise network