MEC中基于改进遗传模拟退火算法的虚拟网络功能部署策略

Virtual network function deployment strategy based on improved genetic simulated annealing algorithm in MEC

为了有效改善多集群共存的移动边缘网络中业务流端到端服务时延,提出了一种基于改进遗传模拟退火算法的虚拟网络功能部署策略。通过开放Jackson排队网络对移动业务流的时延进行最优化建模,在证明其NP性的基础上提出了将遗传算法与模拟退火算法相结合的求解策略,该策略通过对服务节点的提前映射机制避免了可能带来的网络拥塞,并通过个体的约束性判断和纠正遗传的方法避免了局部最优的出现。在不同的服务请求量、服务节点规模、集群数量及虚拟网络功能之间的逻辑连接关系等参数下的对比实验表明,该策略能提供更低时延的端到端服务,使时延敏感类移动业务获得更好体验。

In order to effectively improve the end-to-end service delay of the flow in multi-clusters coexisting mobile edge computing(MEC) network, a virtual network function deployment strategy based on improved genetic simulated annealing algorithm was proposed. The delay of mobile service flow was mathematically modeled through the open Jackson queuing network. After proving the NP attribute of this problem, a solution combining genetic algorithm and simulated annealing algorithm was proposed. In this strategy, the advance mapping mechanism avoids the possibility of network congestion, and the occurrence of local optima was avoided through using the methods of individual judgment and corrective genetic. Extensive simulation was set up to evaluate the effectiveness of the proposed strategy under different parameter settings, such as different volume of requests, different scale of service nodes, different number of MEC clusters, and logical link relationships between virtual network functions. Results show that this strategy can provide lower end-to-end services delay and better service experience for latency-sensitive mobile application.