QoS Multicast Routing Algorithm Based on QGA

Chen, Niansheng; Ke, Zongwu

doi:10.1109/npc.2007.48

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…This paper uses QGA [51] to solve the multi-objective optimization problem. QGA is different from the binary representation of the chromosome of the GA. QGA is based on the principle of qubit and quantum superposition state in quantum science.…”

Section: ) Multi-objective Optimization Problem Solvingmentioning

confidence: 99%

Delay-Aware Multipath Parallel SFC Orchestration

2022

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With the development of network functions virtualization and software defined networking, the service function chain (SFC) orchestration issue is a big challenge for high reliability and low latency services. At present, many studies propose solutions in terms of physical node mapping or link mapping for SFC. In this paper, we consider parallel transmission by dividing the SFC request flow into multiple subflows. To solve the problem of orchestrating parallel SFC under the premise of being able to meet the delay requirements of delay-sensitive classes of services, we divide the problem into two parts: virtual network functions mapped to physical servers and virtual links mapped to physical links. In the first part, we find suitable physical nodes for deployment by the simulated annealing algorithm. In the second part, we construct the link mapping problem as a multi-objective optimization problem. We solve this multi-objective optimization problem by quantum genetic algorithm. Finally, the mapping scheme for parallel SFC is generated. We have conducted comparative analyses of the algorithms through simulation experiments. The results show that the method proposed in this paper can effectively improve the orchestration efficiency of parallel SFC. The algorithm we build can not only minimize the resource consumption and routing energy consumption but also meet the delay requirements well. Therefore, this paper has high practical significance in diverse delay-sensitive service applications and provides a solution for future multipath parallel SFC orchestration.

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Section: ) Multi-objective Optimization Problem Solvingmentioning

confidence: 99%

Delay-Aware Multipath Parallel SFC Orchestration

2022

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“…QGA can represent a linear superposition of state, and there is no need to include many individuals. QGA has an excellent ability of global search due to its diversity caused by the probabilistic representation, and it can approach better solutions than classic GA [5] .…”

Section: Qgamentioning

confidence: 99%

“…If the value of Delta is too small, the speed of convergence will slow down, while if the value of Delta is too great, divergence or premature convergence may happen. So a dynamic regulation strategy of rotating angle is used, which regulates the values of Delta between 0.1π and 0.005π according to the inheritance generation [5] .…”

Section: Quantum Rotating Gatementioning

confidence: 99%

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Search for the Best Polarity of Multi-Output RM Circuits Base on QGA

Wang

Zhang

et al. 2008

2008 Second International Symposium on Intelligent Information Technology Application

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For n-variable logic function, the power dissipation and area of Reed-Muller (RM) circuit corresponding to each polarity are different. This paper proposes a new algorithm based on quantum genetic algorithm (QGA) to search for the best polarity of multi-output RM circuits in terms of the power and area. Experimental results of six large circuits from MCNC benchmark show that the best polarity obtained by the proposed algorithm can achieve average power and area savings by86.8% and 65.4% respectively, compared with polarity 0.

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