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Network function virtualization can increase the versatility of computing and network platforms, enabling them to provide more flexible services. In terms of a softwaredefined network and network function virtualization environment, this paper explores the joint virtual network function (VNF) deployment and traffic routing in multisource multicast for VNF service chain deployment to minimize the deployment overhead, transmission delay. Firstly, a multi-objective optimization model, which minimizes the deployment overhead and transmission delay, is established to tackle this challenge problem. Then, a highly efficient coding scheme, crossover, and mutation operators are designed. Based on these, an improved genetic algorithm in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework (GA-MOEA/D) is proposed to solve this multi-objective problem. Finally, simulation experiments are conducted using two widely used network topologies in order to demonstrate the performance of the proposed algorithm. The simulation results demonstrate that the proposed algorithm can obtain the smaller deployment overhead, transmission delay, load degree and energy consumption 5.2%-15.61%, 11.2%-26.4%, 8.3%-17.6% and 7.8%-14.4% than that of the compared algorithm, respectively. [ABSTRACT FROM AUTHOR]

As a new cloud service mode, mobile edge computing distributes the traditional centralized deployment and management of cloud resources to the wireless access network. Mobile services can be processed for the sake of obtaining a good service experience and reduce the network load of the backtrip network. In order to effectively improve the end-to-end service delay of the flow in multi-clusters coexisting mobile edge computing (MEC) network, an improved virtual network function (VNF) service chain deployment algorithm based on Q-learning was proposed. Based on the planning model, a spatial-temporal optimization model of VNF service chain deployment was established by markov decision process, and an improved BPQ-learning algorithm was designed to solve the model. The method considered both the virtual deployment of MEC service chain in space dimension and the life cycle management of VNF in time dimension. The optimization of VNF deployment is realized. The possible network congestion is avoided by deployment service nodes in advance. Experimental results show that the proposed strategy can provide end-to-end services with lower latency and better experience for delaysensitive mobile services under different VNF service chain scale, service node scale, cluster number and logical connection relationship among the virtual network functions. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of next generation communication. Resource scheduling for virtual network function service chain (VNF-SC) mapping is the key issue of the NFV. A virtual network function service chain placement multi-objective optimization model and algorithm based on improved genetic algorithm is proposed. Firstly, a multi-objective optimization model, which minimizes deployment cost, transmission delay and maximizes the load balance, is established. On this basis, an improved genetic algorithm based on MOEA/D is proposed to solve the established multiobjective model. In this algorithm, the combination scheme and mapping scheme of the service request are coded by the two-level coding method in the mapping process, and then the improved sparrow search algorithm is used to obtain the service function chain deployment scheme of the request and calculate the mapping weight. In addition, an efficient individual generation strategy is proposed to generate some superior individual. Finally, some simulation experiments are conducted and the experimental results show that the algorithm can effectively reduce deployment cost and transmission delay than the compared algorithm. [ABSTRACT FROM AUTHOR]