Privacy-Preserving Deployment Mechanism for Service Function Chains Across Multiple Domains
Jun Cai, Zirui Zhou, Zhongwei Huang, Wenlong Dai, F. Richard Yu
Abstract
Network function virtualization (NFV) has attracted attention because of its flexible configuration and management of network functions. Based on NFV, the service function chain (SFC) defines a group of virtual network functions (VNFs) connected sequentially, enabling flexible customization and provisioning of network services. In the large-scale and heterogeneous Internet of Things (IoT) environment, e.g., industrial IoT, servers provided by a single infrastructure provider (InP) cannot support the deployment of all VNFs, and SFCs must be deployed across multiple domains. However, SFCs deployed across multiple domains will inevitably bring privacy leakage and resource coordination difficulties, thereby reducing the efficiency of network services. To address these issues, this paper proposes a privacy-preserving deployment mechanism (PPDM) for SFCs that achieves near-optimal SFC deployment across multiple domains while protecting resource and topology privacy. PPDM first performs virtual resource prediction and forms the service intention response matrix (SIRM) based on SFC requests (SFCRs). Second, the multi-domain controller (MDC) discovers a near-optimal SFCs deployment strategy by deep Q-network (DQN) using SIRM as input to protect domains’ privacy. Finally, the learned strategies are distributed to intra-domain controllers (IDCs) to implement specific services. Simulation results demonstrate that the proposed method outperforms privacy-preserving and non-privacy-preserving methods.