Multi-objective virtual network embedding algorithm based on Q-learning and curiosity-driven

Network virtualization is a vital technology that helps overcome shortcomings such as network ossification of the current Internet architecture. However, virtual network embedding (VNE) involving the allocation of resources for heterogeneous virtual network requests (VNRs) on the substrate network (...

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Published in:	EURASIP journal on wireless communications and networking Vol. 2018; no. 1; pp. 1 - 12
Main Authors:	He, Mengyang, Zhuang, Lei, Tian, Shuaikui, Wang, Guoqing, Zhang, Kunli
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 15.06.2018 Springer Nature B.V SpringerOpen
Subjects:	Algorithms Communications Engineering Curiosity-driven Data mining Embedding Energy conservation Energy consumption Energy-aware Engineering Falling Information Systems Applications (incl.Internet) Integer programming Machine learning Markov processes Multiple objective analysis Network virtualization Networks Optimization Q-learning Recent Challenges & Avenues in Wireless Communication through Advance computational Intelligence or Deep learning methods Signal,Image and Speech Processing Substrates Virtual networks Q-learning Network virtualization Energy-aware Curiosity-driven
ISSN:	1687-1499, 1687-1472, 1687-1499
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Abstract	Network virtualization is a vital technology that helps overcome shortcomings such as network ossification of the current Internet architecture. However, virtual network embedding (VNE) involving the allocation of resources for heterogeneous virtual network requests (VNRs) on the substrate network (SN) is considered as NP-hard problem. VNE process may involve conflicting objectives, including energy saving and VNR acceptance rate as the most critical. In this paper, we propose a virtual network multi-objective embedding algorithm based on Q-learning and curiosity-driven (Q-CD-VNE) for improving the performance of the system by optimizing conflicting objectives, namely energy saving and acceptance rate. The proposed algorithm employs Q-learning and curiosity-driven mechanism by considering other non-deterministic factors to avoid falling into a local optimum. The major contributions of this work involve (1) modeling of the multi-objective deterministic factors as binary (0, 1) integer programming problem, (2) formulating the virtual node mapping problem using the Markov decision process (MDP), (3) solving the VNE problem using Q-learning algorithm, (4) mining non-deterministic factors using curiosity-driven mechanism for avoiding prematurely falling into the Exploration-Exploitation dilemma and local optimal. Experimental results in comparison with representative researches in the field prove that the proposed algorithm can reduce energy consumption, improve the request acceptance rate, and improve the long-term average income.
AbstractList	Network virtualization is a vital technology that helps overcome shortcomings such as network ossification of the current Internet architecture. However, virtual network embedding (VNE) involving the allocation of resources for heterogeneous virtual network requests (VNRs) on the substrate network (SN) is considered as NP-hard problem. VNE process may involve conflicting objectives, including energy saving and VNR acceptance rate as the most critical.In this paper, we propose a virtual network multi-objective embedding algorithm based on Q-learning and curiosity-driven (Q-CD-VNE) for improving the performance of the system by optimizing conflicting objectives, namely energy saving and acceptance rate. The proposed algorithm employs Q-learning and curiosity-driven mechanism by considering other non-deterministic factors to avoid falling into a local optimum. The major contributions of this work involve (1) modeling of the multi-objective deterministic factors as binary (0, 1) integer programming problem, (2) formulating the virtual node mapping problem using the Markov decision process (MDP), (3) solving the VNE problem using Q-learning algorithm, (4) mining non-deterministic factors using curiosity-driven mechanism for avoiding prematurely falling into the Exploration-Exploitation dilemma and local optimal. Experimental results in comparison with representative researches in the field prove that the proposed algorithm can reduce energy consumption, improve the request acceptance rate, and improve the long-term average income. Abstract Network virtualization is a vital technology that helps overcome shortcomings such as network ossification of the current Internet architecture. However, virtual network embedding (VNE) involving the allocation of resources for heterogeneous virtual network requests (VNRs) on the substrate network (SN) is considered as NP-hard problem. VNE process may involve conflicting objectives, including energy saving and VNR acceptance rate as the most critical. In this paper, we propose a virtual network multi-objective embedding algorithm based on Q-learning and curiosity-driven (Q-CD-VNE) for improving the performance of the system by optimizing conflicting objectives, namely energy saving and acceptance rate. The proposed algorithm employs Q-learning and curiosity-driven mechanism by considering other non-deterministic factors to avoid falling into a local optimum. The major contributions of this work involve (1) modeling of the multi-objective deterministic factors as binary (0, 1) integer programming problem, (2) formulating the virtual node mapping problem using the Markov decision process (MDP), (3) solving the VNE problem using Q-learning algorithm, (4) mining non-deterministic factors using curiosity-driven mechanism for avoiding prematurely falling into the Exploration-Exploitation dilemma and local optimal. Experimental results in comparison with representative researches in the field prove that the proposed algorithm can reduce energy consumption, improve the request acceptance rate, and improve the long-term average income. Network virtualization is a vital technology that helps overcome shortcomings such as network ossification of the current Internet architecture. However, virtual network embedding (VNE) involving the allocation of resources for heterogeneous virtual network requests (VNRs) on the substrate network (SN) is considered as NP-hard problem. VNE process may involve conflicting objectives, including energy saving and VNR acceptance rate as the most critical. In this paper, we propose a virtual network multi-objective embedding algorithm based on Q-learning and curiosity-driven (Q-CD-VNE) for improving the performance of the system by optimizing conflicting objectives, namely energy saving and acceptance rate. The proposed algorithm employs Q-learning and curiosity-driven mechanism by considering other non-deterministic factors to avoid falling into a local optimum. The major contributions of this work involve (1) modeling of the multi-objective deterministic factors as binary (0, 1) integer programming problem, (2) formulating the virtual node mapping problem using the Markov decision process (MDP), (3) solving the VNE problem using Q-learning algorithm, (4) mining non-deterministic factors using curiosity-driven mechanism for avoiding prematurely falling into the Exploration-Exploitation dilemma and local optimal. Experimental results in comparison with representative researches in the field prove that the proposed algorithm can reduce energy consumption, improve the request acceptance rate, and improve the long-term average income.
ArticleNumber	150
Author	Zhuang, Lei Wang, Guoqing Tian, Shuaikui He, Mengyang Zhang, Kunli
Author_xml	– sequence: 1 givenname: Mengyang surname: He fullname: He, Mengyang organization: School of Information and Engineering, Zhengzhou University – sequence: 2 givenname: Lei surname: Zhuang fullname: Zhuang, Lei email: ielzhuang@zzu.edu.cn organization: School of Information and Engineering, Zhengzhou University – sequence: 3 givenname: Shuaikui surname: Tian fullname: Tian, Shuaikui organization: School of Information and Engineering, Zhengzhou University – sequence: 4 givenname: Guoqing surname: Wang fullname: Wang, Guoqing organization: School of Information and Engineering, Zhengzhou University – sequence: 5 givenname: Kunli surname: Zhang fullname: Zhang, Kunli organization: School of Information and Engineering, Zhengzhou University
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Snippet	Network virtualization is a vital technology that helps overcome shortcomings such as network ossification of the current Internet architecture. However,... Abstract Network virtualization is a vital technology that helps overcome shortcomings such as network ossification of the current Internet architecture....
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SubjectTerms	Algorithms Communications Engineering Curiosity-driven Data mining Embedding Energy conservation Energy consumption Energy-aware Engineering Falling Information Systems Applications (incl.Internet) Integer programming Machine learning Markov processes Multiple objective analysis Network virtualization Networks Optimization Q-learning Recent Challenges & Avenues in Wireless Communication through Advance computational Intelligence or Deep learning methods Signal,Image and Speech Processing Substrates Virtual networks
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Title	Multi-objective virtual network embedding algorithm based on Q-learning and curiosity-driven
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Volume	2018
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