Multi-objective Optimization Service Function Chain Placement Algorithm Based on Reinforcement Learning

Liu, Hongtai; Ding, Shengduo; Wang, Shunyi; Zhao, Gang; Wang, Chao

doi:10.1007/s10922-022-09673-5

Cited by 12 publications

(4 citation statements)

References 41 publications

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“…Zhang et al [27] 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 [28] 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%

“…Liu et al. [28] 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%

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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%

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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“…More recent research has focused on innovative technologies such as NFV. The authors in [29] presented a multi-objective optimization service function chain placement (MOO-SFCP) algorithm based on reinforcement learning RL. The goal of the algorithm is to optimize the resource allocation model, including several performance indexes such as underlying resource consumption revenue, revenue cost ratio, VNF acceptance rate, and network latency.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Network Service Optimization in the Context of 5G/NFV

Karkazis

Railis

Prekas

et al. 2022

Signals

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Our contemporary society has never been more connected and aware of vital information in real time, through the use of innovative technologies. A considerable number of applications have transitioned into the cyber-physical domain, automating and optimizing their routines and processes via the dense network of sensing devices and the immense volumes of data they collect and instantly share. In this paper, we propose an innovative architecture based on the monitoring, analysis, planning, and execution (MAPE) paradigm for network and service performance optimization. Our study confirms distinct evidence that the utilization of learning algorithms, consuming datasets enriched with the users’ empirical opinions as input during the analysis and planning phases, contributes greatly to the optimization of video streaming quality, especially by handling different packet loss rates, paving the way for the achievable provision of a resilient communications platform for calamity assessment and management.

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“…Reasonable SFC mapping resource allocation strategy can improve network resource utilisation, enhance network and service processing speed, scientific and effective SFC resource allocation decision-making has become a key means to ensure efficient operation of network services [10]. Since SFC requests tend to arrive unpredictably and randomly in real network scenarios, the focus of existing scholars' research has gradually shifted from static scenarios to the study of SFC resource allocation in dynamic scenarios [11]. Meanwhile, the rapid development of deep reinforcement learning (DRL) has had a great impact on SFC applications, and its combination of the perceptual ability of deep learning and the decision-making ability of reinforcement learning can greatly improve the SFC resource allocation capability.…”

Section: Introductionmentioning

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.

show abstract

Multi-objective Optimization Service Function Chain Placement Algorithm Based on Reinforcement Learning

Cited by 12 publications

References 41 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

Intelligent Network Service Optimization in the Context of 5G/NFV

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

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