Energy Efficiency Opposition‐Based Learning and Brain Storm Optimization for VNF‐SC Deployment in IoT

Xuan, Hejun; Zhao, Xuelin; Liu, Zhenghui; Fan, Jianwei; Li, Yanling

doi:10.1155/2021/6651112

Cited by 5 publications

(5 citation statements)

References 32 publications

(28 reference statements)

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“…In recent decade years, there are large number works focusing on the virtual network function service chain deployment problem, such as [12,13]. Zhu et al [8], through calculating K alternative path data center deployment of virtual network function of the virtual network with required function of the longest common subsequence between sequence, determine the function of virtual network routing of service chain and virtual network function deployment plan, designed to maximize the network in the virtual network in the reuse rate.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

Xuan

Yang

Zhao

et al. 2021

Security and Communication Networks

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Network function virtualization (NFV) has the potential to lead to significant reductions in capital expenditure and can improve the flexibility of the network. Virtual network function (VNF) deployment problem will be one of key problems that need to be addressed in NFV. To solve the problem of routing and VNF deployment, an optimization model, which minimizes the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF, is established. In this optimization model, the dependency among the different VNFs is considered. In order to solve the service chain mapping problem of high dynamic virtual network, a new virtual network function service chain mapping algorithm PDQN-VNFSC was proposed by combining prediction algorithm and DQN (Deep Q-Network). Firstly, the real-time mapping of virtual network service chains is modeled into a partial observable Markov decision process. Then, the real-time mapping process of virtual network service chain is optimized by using global and long-term benefits. Finally, the service chain of virtual network function is mapped through the learning decision framework of offline learning and online deployment. The simulation results show that, compared with the existing algorithms, the proposed algorithm has a lower the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF.

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Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

Xuan

Yang

Zhao

et al. 2021

Security and Communication Networks

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“…As one of the key technologies of 5G, the research of NFV has attracted the attention of many experts and scholars. Most studies mainly consider minimizing network resource consumption and maximizing network income [13,14]. Eramo et al [15] proposed, using feedback mechanism, to deploy VNF and select routing, so as to realize load balancing and improve the request acceptance rate.…”

Section: Introductionmentioning

confidence: 99%

HS-MOEA/D: An Oriented Algorithm for Delay and Reliability VNF-SC Deployment

Xuan

You

Liu

et al. 2021

Security and Communication Networks

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Network function virtualization (NFV) technology can realize on-demand distribution of network resources and improve network flexibility. It has become one of the key technologies for next-generation communications. Virtual network function service chain (VNF-SC) deployment is an important problem faced by network function virtualization technology. In this paper, the problem, VNF deployment for VNF-SC, is investigated. First, a two-objective mathematical model, which maximizes balancing and reliability of SFC, is established. In this model, VNFs are divided into two classes, i.e., part of required VNFs in each VNF-SC is dependent, others are independent. Second, harmony search-based MOEA/D (HS-MOEA/D) is proposed to solve the model effectively. In HS-MOEA/D, Chebyshev decomposition mechanism is used to transform multiobjective optimization problem into a series of single-objective optimization subproblems. A new evolutionary strategy is deeply studied in order to propose a new harmony search (HS) algorithm. Finally, to show high performance of the proposed algorithm, a large number of experiments are conducted. The simulation results show that the proposed algorithm enhances the reliability of SFC and reduces the end-to-end delay.

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“…Brain-storm optimization (BO) is a high-performing intelligent optimization algorithm that was first proposed in [35]. It has been widely applied to solve industrial problems [36,37] in recent research and shows great performance. Its principle mimics the processes of social contact and the idea generation of people, where better ideas are constantly stimulated through the interaction of ideas among people.…”

Section: Introductionmentioning

confidence: 99%

Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time

Zhao,

Xiao,

Wang

et al. 2023

Mathematics

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Disassembling discarded electrical products plays a crucial role in product recycling, contributing to resource conservation and environmental protection. While disassembly lines are progressively transitioning to automation, manual or human–robot collaborative approaches still involve numerous workers dealing with hazardous disassembly tasks. In such scenarios, achieving a balance between low risk and high revenue becomes pivotal in decision making for disassembly line balancing, determining the optimal assignment of tasks to workstations. This paper tackles a new disassembly line balancing problem under the limitations of quantified penalties for hazardous component disassembly and the switching time between adjacent tasks. The objective function is to maximize the overall profit, which is equal to the disassembly revenue minus the total cost. A mixed-integer linear program is formulated to precisely describe and optimally solve the problem. Recognizing its NP-hard nature, a metaheuristic algorithm, inspired by human idea generation and population evolution processes, is devised to achieve near-optimal solutions. The exceptional performance of the proposed algorithm on practical test cases is demonstrated through a comprehensive comparison involving its solutions, exact solutions obtained using CPLEX to solve the proposed mixed-integer linear program, and those of competitive peer algorithms. It significantly outperforms its competitors and thus implies its great potential to be used in practice. As computing power increases, the effectiveness of the proposed methods is expected to increase further.

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Energy Efficiency Opposition‐Based Learning and Brain Storm Optimization for VNF‐SC Deployment in IoT

Cited by 5 publications

References 32 publications

Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

HS-MOEA/D: An Oriented Algorithm for Delay and Reliability VNF-SC Deployment

Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time

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