Intelligent cloud computing network flow adjusting and optimizing system and method

Abstract

The invention particularly relates to an intelligent cloud computing network flow adjusting and optimizing system and method. The intelligent cloud computing network flow adjusting and optimizing system is composed of a data acquisition layer, a data analysis and modeling layer and a strategy service layer. According to the intelligent cloud computing network flow adjusting and optimizing system and method, artificial intelligence is used for providing high-quality services, passive defense is switched to active optimization, static configuration is switched to dynamical adjustment, flow monitoring can be better conducted, the resource bottleneck of the network can be excavated through a machine learning algorithm, the optimal bandwidth configuration scheme is obtained, the bandwidth utilization rate is improved, so that the user experience is improved, and the operation cost and investment are reduced; through strategy configuration, early warning and automatic expansion of bandwidthresources are realized, so that automatic adjustment of bandwidth is realized.

Description

technical field [0001] The invention relates to the technical field of cloud computing and machine learning, in particular to an intelligent cloud computing network traffic optimization system and method. Background technique [0002] With the rapid increase of network traffic and the development of new technologies such as NFV / SDN / 5G, the network scale is rapidly expanding, and the network topology and service types are extremely complex, which puts forward higher requirements for network bandwidth planning and maintenance. Especially in the cloud center multi-tenant scenario, Internet traffic changes complexly, requiring professional knowledge and experience to manually optimize bandwidth. Currently commonly used network bandwidth adjustment methods have the following defects: [0003] 1. The operation and maintenance workload is heavy, relying on manual configuration is cumbersome and error-prone, and it is difficult to meet flexible bandwidth requirements with fixed con...

AI Technical Summary

Problems solved by technology

[0003] 1. The operation and maintenance workload is heavy, relying on manual configuration is cumbersome and error-prone, and it is difficult to meet flexible bandwidth requirements with fixed configuration;

[0004] In order to ensure important services, network administrators need to manually schedule traffic, which is difficult to achieve real-time traffic optimization

Manual operation and maintenance costs are high, requiring high experience for administrators, and there is a risk of network traffic being blocked due to improper operation

[0005] Two: The flexibility of manual configuration is poor. Since there is no real-time prediction mechanism, it cannot be adjusted in real time according to the time characteristics, space characteristics, and business characteristics of the traffic itself.

In order to ensure service quality, operators often configure according to the experience peak plus redundancy. When the service is low, bandwidth resources are wasted, and when hot events such as concerts or other emergencies occur, resources may be lost due to insufficient resources. causing network congestion

[0006] 3. It is impossible to perceive the change trend of traffic in advance, and it is impossible to predict the change of traffic in real time and give early warning of faults. It is often a time mechanism, that is, to respond to emergency after a fault occurs, and to expand capacity passively.

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