Statistical-prediction-based automatic cloud CDN (Content Delivery Network) resource automatic deployment method

An automatic deployment and resource technology, applied in the field of communication, can solve the problems of low resource utilization rate and poor elastic expansion capacity, and achieve the effect of reducing energy consumption and operation and maintenance costs, reducing waste of resources, and improving utilization rate

Inactive Publication Date: 2012-11-28
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the shortcomings of low utilization rate of existing CDN resources and poor elastic capacity expansion, the present invention provides an automatic deployment method of clou

Method used

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  • Statistical-prediction-based automatic cloud CDN (Content Delivery Network) resource automatic deployment method
  • Statistical-prediction-based automatic cloud CDN (Content Delivery Network) resource automatic deployment method
  • Statistical-prediction-based automatic cloud CDN (Content Delivery Network) resource automatic deployment method

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[0044] Example

[0045] Such as figure 1 As shown, the cloud CDN architecture is mainly divided into two parts: central node and edge node. The central node is mainly responsible for global content management, resource management, and service scheduling, and distributes content to each edge node through PULL or PUSH; while the edge node is the carrier of cloud CDN content distribution, and the edge node closest to the user will Internet content is directly transmitted to the user terminal. Using cloud computing virtualization technology, the resources of each edge node are concentrated in the resource pool for unified management, and the corresponding cache server is automatically deployed according to the load situation of the edge node.

[0046] The principle of automatic deployment of cloud CDN resources of the present invention is as follows: at a certain time of the day, each edge node of the cloud CDN predicts the load forecast value of the day according to the forecast mode...

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Abstract

The invention discloses a statistical-prediction-based automatic cloud CDN (Content Delivery Network) resource deployment method which comprises the steps of: predicting a load prediction value in a next 24h according to history data of a load of each edge node of a CDN, formulating a virtual server resource deployment plan of a corresponding edge node, carrying out virtual server resource deployment on each edge node of a cloud CDN; monitoring a load condition of each edge node of the cloud CDN in real time, when the cloud CDN is in a user visit rush time, mounting mirror images configured with relevant businesses to virtual servers by a cloud platform, and starting so as to be added in each edge node of the cloud CDN for sharing burst visit pressure; and when the cloud CDN is in a non-visit rush time, only preserving the virtual server which can keep business operate at a normal level, and recovering idle virtual server resources to a resource pool. According to the statistical-prediction-based cloud CDN resource automatic deployment method, the burst business rush time can be effectively dealt, the resource utilization rate of the CDN is increased, and the energy consumption and the maintenance and operation cost of the CDN are lowered.

Description

technical field [0001] The invention relates to the communication field, in particular to a method for automatically deploying cloud CDN resources based on statistical prediction. Background technique [0002] CDN (Content Delivery Network) is a content distribution network, which sends content (video, audio, web pages, etc.) to the "edge" of the network closest to the user by adding a new network architecture to the existing IP bearer network. It allows users to obtain the content they need nearby, thereby optimizing network traffic conditions and improving the response speed of users visiting websites. CDN can solve the problems of slow content transmission and stability caused by the small bandwidth of the backbone network, the large number of user visits, and the uneven distribution of network points. [0003] However, the access volume of the CDN service is not balanced, and has strong periodicity and burstiness. For example, according to the data analysis of China Te...

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Application Information

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IPC IPC(8): H04L29/08
CPCY02B60/31Y02D30/50
Inventor 吴宗泽梁啟成冯梁谋张勰向友君傅予力
Owner SOUTH CHINA UNIV OF TECH
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