Optimal Deployment Method of Content Delivery Network Server in Network Function Virtualization Environment

Abstract

Aiming at the defects of the existing CDN copy placement scheme, the present invention proposes a content delivery network server optimization deployment method under the network function virtualization environment. The invention utilizes the spectral clustering model, taking into account the data and topological characteristics of the physical network. The adjacency matrix is ​​established by using the topological relationship of the underlying network and the traffic demand of the nodes, and the physical network nodes are divided into several sets by using the spectral clustering algorithm. Then traverse the nodes in each set, and calculate the cost required for it to serve as the replica center node to provide content services for the rest of the set. Select the node with the lowest cost and use it as the placement node of the replica server to reduce the cost.

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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