Cloud-Edge-End Collaborative Dependent Computing Schedule Strategy for Immersive Media

Abstract

Immersive media applications often create an immersive experience for users through head-mounted displays. However, the computing power and storage capacity of terminal devices are limited, and the local computing architecture cannot meet the high resolution and low latency requirements of panoramic video frames. As a new computing paradigm, cloud, edge and end collaborative computing architecture selectively schedules computing tasks to cloud servers and edge servers with higher computing power, which can effectively improve computing efficiency. However, for dependent computational tasks, the scheduling of each task needs to consider its previous tasks, network state, and computational resources of different servers. Therefore, how to make computational offloading decisions and resource allocation for dependent tasks is a key issue for collaborative computing architectures. This paper investigates and analyzes the immersive media scenarios and the basic computation offloading strategies, and construct a dependent task model graph and optimization problem model. Based on threshold strategy, greedy strategy of heuristic algorithm and deep reinforcement learning model, a scheduling strategy under collaborative computing architecture is designed to maximize the reward related to delay and cost. Finally, the basic performance of the computational task scheduling strategy based on deep reinforcement learning and greedy policy is verified through simulation experiments. The experimental results show that the algorithm reduces the latency by more than 1.8 ms and increases the timely completion rate by more than $$ relative to several basic scheduling schemes, which can effectively improve the service quality and user experience.