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...

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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.

Application Domain

Energy efficient ICTTransmission +1

Technology Topic

Data predictionProgram planning +9

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Experimental program(1)

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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 model, and deploys the resources of each edge node of the CDN according to the forecast load forecast value; when the CDN is in During the peak load period, the cloud platform automatically mounts the image of the configured related business to the virtual server and adds it to the CDN edge node as the cache server of the node to increase service capacity; after the peak period, the cloud platform reclaims idle servers to resources In the pool, only the number of virtual servers preset by the original CDN is retained, thereby realizing flexible resource allocation. The available resources in the resource pool can be allocated to other services.

[0047] Such as figure 2 As shown, the present invention predicts the load forecast value of the next 24 hours based on the historical data of the load of each edge node of the cloud CDN, formulates the resource deployment plan of the corresponding node, and deploys the resources of each edge node of the cloud CDN, including the following steps:

[0048] S11. At a specific time of the day, such as 3 o'clock in the morning every day, count the load of each edge node of the cloud CDN in the previous 24 hours to obtain the actual load average value L.

[0049] S12. Search for statistical records, and obtain the load forecast value P of each edge node of the cloud CDN in the previous 24 hours.

[0050] S13. Compare the load forecast P and the actual load average L in the previous 24 hours, and judge If yes, if yes, the cloud CDN maintains the current deployment scale; if it does not hold and P is greater than L, the edge node will increase deployment of CDN virtual servers; if it is not true and P is less than L, the edge node will reclaim idle virtual server resources; among them, The threshold α is 0.1.

[0051] In addition, the load prediction model is used to obtain the predicted load value P of each edge node of the cloud CDN in the next 24 hours. The process of establishing the load forecasting model will be described in detail below:

[0052] Cloud CDN counts the load situation of each edge node of the cloud CDN in the previous n days every day, and calculates the daily average load of the previous n days {l n }, establish a random time series Auto Regressive model to predict the load forecast value P in the next 24 hours.

[0053] The steps for establishing a stochastic time series autorecursive model as a linear signal model are as follows: establish a stochastic time series autorecursive model as a linear signal model L(z), the form is shown in formula (1).

[0054] L ( z ) = d 0 A ( z ) = d 0 1 + X k = 1 p a k z - k - - - ( 1 )

[0055] D in formula (1) 0 Is the system gain, p is the order of the model, and the formula (1) is transformed to obtain:

[0056] L ( z ) + X k = 1 p a k L ( z ) z - k = d 0 - - - ( 2 )

[0057] Since the system is causal, take the inverse z transformation to equation (2), and the impulse response l(n) of the linear signal model L(z) is taken as the predicted load value P in the next 24 hours, which can be expressed as

[0058] l ( n ) = - X k = 1 P a k l ( n - k ) + d 0 δ ( n ) - - - ( 3 )

[0059] If all the poles of the linear signal model L(z) are in the unit circle, then the impulse response l(n) is a causal and stable sequence, and the system is a minimum phase system. From formula (3):

[0060] l(0)=d 0 (4)

[0061] l ( n ) = - X k = 1 p a k l ( n - k ) n> 0 (5)

[0062] From causality:

[0063] l(n)=0 n <0 (6)

[0064] Therefore, except for the value at n=0, l(n) can be obtained as a linear weighted recursive of the previous values l(n-1),...,l(n-p). In other words, the predicted load value l(n) for the next 24 hours can be predicted from p load values in the past. Therefore the coefficient {a k } Can be called predictor coefficients. From equations (4) and (5), the inverse relationship can be written to obtain the predictor coefficient:

[0065] a n = - l ( n ) l ( 0 ) - X k = 1 n - 1 a k l ( n - k ) l ( 0 ) n> 0 (7)

[0066] In summary, assuming that the order of the load forecasting model is p=6, using the daily average load value l(n) for each day within a week, 0≤n≤6, we can calculate the load forecast value l( 7), which is the predicted load value P in the next 24 hours.

[0067] In the process of real-time monitoring of the load situation of each edge node of the cloud CDN, the invention flexibly schedules virtual server resources to deal with sudden access pressure. When the cloud CDN is in the peak period of user access, according to the expansion strategy, the cloud platform automatically configures the relevant The image of the business is mounted on the virtual server and started to join the edge nodes of the cloud CDN to share the sudden access pressure; when the cloud CDN is in the non-access peak period, only the virtual server that maintains the normal level of business operation will be kept idle. Resources are recycled into the resource pool, and the cloud platform then uniformly allocates them according to the needs of other businesses, thereby improving the effective utilization of resources.

[0068] Such as image 3 As shown, the process of cloud CDN resource elastic scheduling is as follows:

[0069] S21: Set a maximum load value M for each edge node of the cloud CDN, and the initial value of M is the predicted load value P in the next 24 hours.

[0070] S22. Monitor the current load value N of each edge node of the cloud CDN in real time.

[0071] S23. If the current load value N is greater than the maximum load value M, the waiting time t1 again determines whether N is greater than M; if it is, it proves that it is the current peak of cloud CDN user access, increases the virtual server of the edge node, and updates the maximum load value M at the same time; Otherwise, ignore and do nothing, and return to step S22 to monitor the current load value N in real time.

[0072] S24. If the current load value N is less than the maximum load value M, wait time t2 to judge again whether N is less than or equal to the predicted load value P in the next 24 hours; if so, shut down redundant virtual servers and reclaim resources, and restore to the cloud CDN edge node The scale of the planned deployment on the day; otherwise, ignore it without doing any processing, and return to step S22 to monitor the current load value N in real time.

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Improve resource utilization

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Increase profit Reduce waste of resources Resource pool Resource utilization Mirror image Energy consumption Distributed computing High level techniques

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

AI-Extracted Technical Summary

Problems solved by technology

Abstract

Image

Examples

Example Embodiment

PUM

Description & Claims & Application Information

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