Deployment method of virtualized network service function chain based on deep reinforcement learning
A virtualized network and service function chain technology, applied in the field of edge computing, can solve problems such as complex and changeable network environments
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[0074] In order to verify the beneficial effects of the present invention, the present embodiment is simulated and verified, and the experimental environment is a 7*3 undirected graph to represent the entire edge network. The whole network is divided into 3 columns, each column represents a class of nodes, and there are 7 nodes in each class. Each node serves as the physical infrastructure, and in the experiment, each node is regarded as an edge server. These three types of nodes can respectively provide a, b, and c three network services. After the simulation experiment, the results of Reward, Cost and Revenue and profit are obtained as follows: Figure 4 to Figure 6 shown.
[0075] Figure 4 It is shown that according to the DDPG algorithm of the present invention, in the 7*3 network topology, with the increase of the number of training sets, the average reward is basically stable after 400 times of training, and the value of Reward gradually converges. A notable finding...
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