Delay-Sensitive Compound Service Function Chain Deployment in Multi-Provider Edge Cloud: A Learning-Based Approach

Multi-Provider Edge Cloud (MPEC) is an important 5G network paradigm where Virtual Network Functions (VNFs) can be deployed on capacity-limited edge servers from different providers and near users, delivering low latency services. Compound Service Function Chains (SFCs) are ordered chains consisting of user-requested VNFs and Supplementary VNFs (S-VNFs) that bridge requested VNFs from different providers. However, different deployment schemes of requested VNFs determine the presence of distinct S-VNFs with varying capacity demands and processing delays. How to deploy VNFs of compound SFCs on edge servers and route their traffic through these VNFs within MPEC networks to minimize total service delay poses a great challenge. In this paper, we first formulate the compound SFC deployment problem as an integer nonlinear program and prove its NP-hard. To solve this problem, we analyze the components of service delay, decompose the problem into three subproblems and propose a heuristic algorithm. We further consider a condition that collaborations among providers are constrained to a limited scope and devise a reinforcement learning-based scheme to address this condition. Next, we present a theoretical analysis of the two proposed algorithms, including time complexity and worst-case performance bounds. Finally, extensive experiments on real-world datasets demonstrate that our algorithms outperform several existing methods.