Content delivery network server optimization and deployment method applied in network function virtualization environment

Abstract

The invention provides a content delivery network server optimization and deployment method applied in a network function virtualization environment in order to overcome the defects of an existing CDN replica placement scheme. According to the method, a spectral clustering model is utilized, and data and topological features of a physical network are both taken into account. An adjacenty matrix is built by means of the topological relation of an underlying network and the traffic demande of nodes, and nodes of the physical network are partitioned into several sets through a spectral clustering algorithm. The nodes in all the sets are traversed, and the cost needed when each node provides a content service for the other nodes in the corresponding set by serving as a replica center node is calculated. The node needing the lowest cost is selected and taken as the placement node of a replica server, and therefore the cost is reduced.

Description

technical field [0001] The invention belongs to the field of network technology, and in particular relates to a content delivery network server optimization deployment method under a network function virtualization environment. Background technique [0002] With the development of network technology, especially the rapid development of mobile Internet technology, the dissemination of network content has gradually changed from text and image content to video dissemination. This results in explosive growth of network traffic and increases the possibility of network congestion. Network congestion leads to a decline in user experience, which causes content providers to lose a large number of customers and reduce profits. [0003] With the development of CDN (Content Delivery Network), the content replica server can be deployed on the edge network closer to the user, so that the user can obtain the network content in a relatively short period of time, which increases the user ex...

AI Technical Summary

Problems solved by technology

However, there are still the following deficiencies: (1) the random algorithm has a large volatility, and the resulting solutions have great limitations

(2) The random algorithm is not sensitive to the topological structure of the physical network, and cannot be adjusted according to different network topological structures, and the topology sensitivity of the algorithm is poor

(3) The random algorithm is not sensitive to the node traffic requirements of the physical network and bandwidth resources, and cannot be adjusted according to the actual underlying network requirements, and the data sensitivity of the algorithm is poor

The disadvantages of this placement scheme are as follows: (1) The greedy algorithm is easy to fall into the local optimal solution

Since the replica center placed first has an impact on the replica center placed later, it is difficult to obtain the global optimal

(2) The greedy algorithm is sensitive to the data of the underlying network, but is less sensitive to the topology of the underlying network

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