Big Data Oriented Multi-Objective SFC Placement in Dynamic MEC: A Distributed DRL Approach
Huanlai Xing, Yutong Pu, Xinhan Wang, Fuhong Song, Zhiwen Xiao, Li Feng, Lexi Xu · 2024
Network function virtualization (NFV) enables the provision of different quality of service (QoS) levels through service function chains (SFCs), where NFV outsources big data tasks of end users to nearby edge servers. In multi-access edge computing (MEC), its dynamic and uncertainty nature poses great challenges to the SFC placement problem, which requires optimizing multiple potentially-conflicting objectives, such as network latency and load balancing. Moreover, user preferences may vary along with time, adding another layer of complexity to the problem. To address the problem above, we propose a novel distributed deep reinforcement learning (DRL) architecture based on a spatio-temporal encoder (STE), denoted as DDRL-STE. DDRL-STE is featured with equal-weight pre-training and transformer-based STE. Experimental results show that DDRL-STE outperforms three state-of-the-art DRL algorithms regarding latency and load balancing under three well-known network topologies, exhibiting its excellent potential in exploration and generalization.