Network Function Parallelization for High Reliability and Low Latency Services

• Published in: IEEE access Vol. 8; pp. 75894 - 75905
• Main Authors: Zhou, Jianhong, Feng, Gang, Gao, Yi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bandwidth; Bandwidths; Bottlenecks; Computer architecture; Computer network reliability; Delays; Machine learning; Markov processes; Network latency; Network reliability; NFP; NFV; Numerical models; parallel network service function chain; Parallel processing; Reliability; Traffic delay; URLLC; Virtual networks; Wireless communication systems; Wireless communications
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2988719

In 5G-and-beyond wireless communication systems, Network Function Virtualization (NFV) has been widely acknowledged as an important network architecture solution to meet diverse service requirements in various scenarios. However, with the increase of network functions, the introduction of NFV may significantly increase the delay of traffic flows, which is much undesired, especially for Ultra Reliable and Low Latency Communication (URLLC) service. Network Function Parallelism (NFP) architecture has been recently proposed as an effective technique to address the bottleneck of NFV technology. NFP can potentially improve the reliability and reduce the delay of service function chains (SFCs). In this paper, we propose a learning based SFC deployment strategy under NFP architecture with aim to improve the service reliability while reducing the end-to-end service delay. Specifically, service reliability is improved by deploying back-up virtual network function (VNF) nodes, while the flow delay is reduced via parallel network function processing. We formulate the VNF deployment as an integer-programming problem with objective of minimizing the reserved computing and bandwidth resources, while guaranteeing the service reliability and end-to-end delay. Considering the hardness and properties of the problem, we transform it as a Markov Decision Process (MDP), and employ a reinforcement-learning algorithm to solve it. We conduct simulations and the numerical results demonstrate that the proposed strategy can significantly improve the service reliability and delay performance, which are crucial for URLLC service.