Service Function Chains Deployment for 5G Slice With Bandwidth Coupling

Zhao, Shuang; Chi, Xuefen; Yu, Baozhu; Ji, Fenglei; Meng, Qinglu

doi:10.1109/lcomm.2022.3188678

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Zhao et al. [21] proposed a coupled bandwidth resource allocation model for sharing resources among multiple SFCs. They evaluated the required bandwidth to meet statistical delay QoS requirements and developed an integer linear programming (ILP) model to minimize resource occupancy.…”

Section: Related Workmentioning

confidence: 99%

An improved resource allocation method for mapping service function chains based on A3C

Huang,

Tian,

Zhang

et al. 2024

IET Communications

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Network function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

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Section: Related Workmentioning

confidence: 99%

An improved resource allocation method for mapping service function chains based on A3C

Huang,

Tian,

Zhang

et al. 2024

IET Communications

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show abstract

“…considered different characteristics of nodes and resource loads and proposed a federated learning based SFC resource allocation algorithm that effectively balances resource consumption and allows SFC reconfiguration to reduce service blocking rate. Liu et al [20]. proposed a multi-objective optimal service function chain mapping method based on reinforcement learning (RL), which obtains the mapping probability of each physical node and completes the SFC mapping based on the probability by optimising the resource allocation model and using the information matrix extracted from the physical network as a training environment for the intelligences.…”

Section: Related Workmentioning

confidence: 99%

“…The number of SFCs is [10,100] and the number of SFCs has 70% of priority p 3 , 10% each of p 2 and p 4 , and 5% each of p 1 and p 5 . The length of the VNF sequences in a single f takes a value uniformly distributed between [2,10], and the amount of data carried by each f is normally distributed in [10,20] units.…”

Section: Simulation Experiments Environment Designmentioning

confidence: 99%

An improved resource allocation method for mapping service function chains based on A3C

Huang

Wang

et al. 2023

Preprint

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Network function virtualization(NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, we proposes an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C). Firstly, the SFC mapping model is established, the SFC mapping process is divided into two layers of physical topology resource layer and virtual network function request layer, and the two layers are represented by abstract parameters; Secondly, the relationship between mapping resource allocation and service processing process is analysed, and a mathematical model is established for the minimisation of the processing time of the joint allocation of node computation and link bandwidth communication resources, and the minimisation mathematical model is modelled as a Markov process, defining the ternary containing state, action, and reward; Finally, combining the ternary with the deep reinforcement learning algorithm A3C, using the training master network as a template, and using multi-threading technology to generate multiple sub-networks for parallel training to find the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor-Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

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Container-Aware Service Function Chains Placement in Vehicular Edge Computing

Zhang

2023

Preprint

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Service Function Chains Deployment for 5G Slice With Bandwidth Coupling

Cited by 3 publications

References 14 publications

An improved resource allocation method for mapping service function chains based on A3C

An improved resource allocation method for mapping service function chains based on A3C

An improved resource allocation method for mapping service function chains based on A3C

Container-Aware Service Function Chains Placement in Vehicular Edge Computing

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