Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee

• Published in: IEEE access 2021, Vol.9, p.121583-121592
• Main Author: Pham, Tuan-Minh
• Format: Article
• Language: English
• Subjects: Algorithms; Approximation; Chaining; Data centers; Delay; Delays; Energy costs; Engineering; Heuristic algorithms; Integer programming; Linear programming; Machine learning; Mixed integer; Network function virtualization; Network topologies; Optimization; Reinforcement learning; Resource management; Routing; service function chaining; Traffic control; Traffic engineering; Traffic flow; Traffic models; Virtual networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3109269

Network Function Virtualization (NFV) is an approach that provides a network service provider with agility and cost-efficiency in managing 6G network services. Standard traffic engineering rules are known limited in assuring a very stringent delay requirement in NFV when a traffic flow is required to follow a sequence of network functions scattered in data center networks. This paper proposes an innovative model and algorithm of traffic engineering for service function chaining (SFC) to maximize cost-efficiency under a delay-guarantee constraint. We first formulate the problem as a mixed-integer linear programming model for obtaining the optimal solution. We then propose an algorithm based on the reinforcement learning principles for finding an approximation solution in a large-scale problem with the dynamics of service demands. Numerical results under both real-world datasets and synthetic network topologies demonstrate that our proposed model and algorithm allow an NFV service provider (NSP) to place a virtual network function and steer a traffic flow efficiently in terms of energy cost for a delay-guarantee SFC. Importantly, the results provide an insight into the optimal and approximation solutions for an NSP to select a suitable traffic engineering approach with regard to network dynamics.