Dynamic Scheduling for Security Protection Re-2 Sources in Cloud–Edge Collaboration Scenarios Using Deep Reinforcement Learning

Lin Guan, Hongmei Shi, Haoran Chen and Yi Wang ()
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Lin Guan: Institute of Big Data, Fudan University, Shanghai 200433, China
Hongmei Shi: College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200433, China
Haoran Chen: School of Software, Fudan University, Shanghai 200433, China
Yi Wang: Institute of Big Data, Fudan University, Shanghai 200433, China

Mathematics, 2025, vol. 13, issue 19, 1-23

Abstract: Current cloud–edge collaboration collaboration architectures face challenges in security resource scheduling due to their mostly static nature, which cannot keep up with real-time attack patterns and dynamic security needs. To address this, this paper proposes a dynamic scheduling method using Deep Reinforcement Learning (DQN) and SRv6 technology. The method establishes a multi-dimensional feature space by collecting network threat indicators and security resource states; constructs a dynamic decision-making model with DQN to optimize scheduling strategies online by encoding security requirements, resource constraints, and network topology into a Markov Decision Process; and enables flexible security service chaining through SRv6 for precise policy implementation. Experimental results demonstrate that this approach significantly reduces security service deployment delays (by up to 56.8%), enhances resource utilization, and effectively balances the security load between edge and cloud.

Keywords: cloud–edge collaboration; security protection resources; dynamic scheduling; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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