Abstract
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.
Highlights
As one of the three major application scenarios of 5G mobile communication networks, Ultra Reliable and Low Latency Communication (URLLC) service is essential for a wide range of delay-sensitive applications, such as autonomous or assisted driving, augmented reality (AR), virtual reality (VR), tactile Internet, and industrial control
SIMULATION AND RESULT ANALYSIS Based on the analyzing result of [5]–[7], [9] that the service reliability can be improved by backing up related virtual network functions (VNFs), in this paper we develop a Q-learning based algorithm (QL-P) for parallel service function chains (SFCs) to obtain the optimized VNF backup strategy efficiently
We have considered the issue of improving reliable backup of parallel SFC, and proposed a Q-learning-based backup mechanism to obtain the optimal backup solutions in resource-constrained networks
Summary
As one of the three major application scenarios of 5G mobile communication networks, Ultra Reliable and Low Latency Communication (URLLC) service is essential for a wide range of delay-sensitive applications, such as autonomous or assisted driving, augmented reality (AR), virtual reality (VR), tactile Internet, and industrial control. The authors of [8] proposed a joint optimization framework called reconfigurable awareness and latency-limited service chain for sequential SFC This framework combines iterative backup selection and routing processes, and allocates resources to the network serving host as much as possible to ensure high reliability and low latency. With aim to minimize reserved resources under the premise of meeting end-to-end reliability and delay constraints under NFP architecture, we propose an intelligent VNF backup node deployment strategy for parallel SFC. Numerical results show that the proposed learning based backup algorithm can achieve higher network throughput on the premise of meeting the reliability and delay requirements, compared with ‘‘The parallel SFC based Q-learning (QL-P)’’, ‘‘lowest reliability first (LRF)’’, ‘‘minimum computing resource first (MCRF)’’, ‘‘Random backup’’ and ‘‘sequential SFC-based Q-learning (QL-S)’’ algorithms.
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