Service Function Chain Scheduling Under the Multi-Cloud Collaborative Service of Information Networks Used for Cross-Domain Remote Surgery

Abstract

Remote surgery is an emerging medical business derived from information networking technology and plays an increasingly essential role in the medical system. In remote surgery, it is imperative to facilitate cross-regional information transmission and processing by leveraging medical information networks to establish a collaborative service model served by multiple data centers in different regions, enabling collaboration and support for surgery operations. Additionally, the implementation of service function chain scheduling technology is crucial for the efficient allocation of computing resources of data centers. In this paper, we design a novel multi-cloud collaborative medical information network framework. Based on this framework, the service function chain (SFC) scheduling problem is investigated to minimize the total weighted end-to-end delay. To solve the scheduling problem, the original problem is reformulated as a Multiple Markov Decision Process (MMDP). Then, a multiple-state-action deep reinforcement learning (MSA-DRL) algorithm is developed to learn the best scheduling policy. Simulation results are presented to demonstrate the superiority of the proposed approach in the aspect of total weighted end to end delay against other benchmark algorithms.