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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Vydáno v:EURASIP journal on wireless communications and networking Ročník 2018; číslo 1; s. 1 - 12
Hlavní autoři: He, Mengyang, Zhuang, Lei, Tian, Shuaikui, Wang, Guoqing, Zhang, Kunli
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 15.06.2018
Springer Nature B.V
SpringerOpen
Témata:
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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Popis
Shrnutí: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.
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ISSN:1687-1499
1687-1472
1687-1499
DOI:10.1186/s13638-018-1170-x