Real-Time and Consistent Route Update Based on Segment Routing for NFV-enabled Networks

Wang, Wan-Chen; Xu, Hongli; Zhao, Gongming; Huang, Liusheng

doi:10.1007/978-3-030-86137-7_37

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…By using the resource-aware algorithm, Hong et al [21] formulate the SFC routing problem as a Binary Integer Programming (BIP) model aiming to minimize the resource consumption costs of flows with SFC requests. Besides, Wang et al [22] formalize the SFC routing problem by jointly optimizing the update delay and the load of VNF. Then they design an algorithm based on randomized rounding to solve it with a bounded approximate ratio.…”

Section: Vnf Placementmentioning

confidence: 99%

Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing

Yang

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

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The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different QoS requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and Service Function Chaining (SFC) routing problem. However, those prior approaches mainly assume that the network state is static and known, disregarding dynamic network variations. To bridge that gap, we leverage Markov Decision Process (MDP) to model the dynamic network state transitions. To jointly minimize the delay and cost of NFV providers and maximize the revenue, we first devise a customized Deep Reinforcement Learning (DRL) algorithm for the VNF placement problem. The algorithm uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). We then propose attention mechanism-based DRL algorithm for the SFC routing problem, which is to find the path to deliver traffic for the VNFs placed on different nodes. The simulation results show that our proposed algorithms outperform the state-of-the-art algorithms in terms of network utility, delay, cost, and acceptance ratio.

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Section: Vnf Placementmentioning

confidence: 99%

Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing

Yang

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

show abstract

Real-Time and Consistent Route Update Based on Segment Routing for NFV-enabled Networks

Cited by 1 publication

References 16 publications

Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing

Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing

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