RED-based network congestion control algorithm

A technology of network congestion and control algorithm, applied in the direction of data exchange network, digital transmission system, electrical components, etc., can solve the problems of reducing message transmission, network jitter, high burst traffic occupation, etc., to reduce configuration difficulty and improve control effective effect

Inactive Publication Date: 2011-01-26
云南省科学技术情报研究院
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Passive queue management is a type of queue truncation (Droptail) management, that is, the network data fills the queue buffer and then discards the data packets that arrive one after another. It has three main defects: (1) The queue is full, and the queue is forced to be dropped after the queue is full. packets, other data cannot enter the queue; (2) deadlock, high burst traffic occupies all queue buffers instantaneously, and other types of data cannot pass through; (3) TCP global synchronization, when the queue discards multiple TCP data packets at the same time, each TCP synchronization reduces message sending, and network throughput drops sharply
However, according to the research of American expert Feng W et al., network congestion control not only depends on the statistical characteristics of the average queue length, but also depends on the distribution of network data arrival time interval and data size distribution. Effectively solve the problem of network congestion control, but also easily cause network jitter, resulting in a decrease in network utilization
In addition, RED and related improved algorithms need to set more than 10 parameters such as average queue size, packet loss probability, upper and lower thresholds of queue packet loss, average data packet size, average network transmission rate, average network transmission delay, and burst volume of data transmission. , and there is no clear rule limit and theoretical basis for the parameter setting, it is mainly set by the experience of the network administrator, and the parameter value has a great influence on the operation effect of the congestion control algorithm

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] Explanation of terms:

[0047] S tiny , S small , S mid , S big , S giant Five numerical concentration measures representing the maximum size of packets in the network respectively.

[0048] P tiny , P small , P mid , P big , P giant Respectively represent the Pareto distribution probability of the data size in each interval.

[0049] P discard Indicates the packet discard probability in each interval of the data.

[0050] Queue_Buf_LEN indicates the length of the queue buffer.

[0051] Queue (utilizntion) Indicates the statistical average queue utilization.

[0052] ζ represents the concentration value of network traffic.

[0053] η represents the relative diffusion strength.

[0054] represents the diffusion rate.

[0055] CntByte (sum) Indicates the total capacity of data flowing through the queue in a period T.

[0056] CntPacket (sum) Indicates the total amount of data flowing through the queue in a cycle T.

[0057] CntByte (i) and CntPacke...

Embodiment 2

[0097] Repeat Example 1, with the following differences: By calculating the concentration, relative diffusion intensity and diffusion rate values ​​of the network flow in the interval, the calculated concentration value is used to partially differentiate the diffusion rate in the sampling period to obtain the change of the scale between the sampling periods size, and use the calculated results to automatically adjust the ruler. This method can be used to calculate the dynamic drop probability of each interval in different periods, so as to adapt to the changes of network congestion status in different periods.

[0098] Algorithm adaptation network congestion state is the optimization subroutine to the 6th step of the present invention, makes algorithm can effectively deal with the arbitrariness and suddenness of network data transmission, self-adaptive design principle is as follows image 3 As shown, the specific implementation description is as follows:

[0099] (1) Using F...

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Abstract

The invention discloses an RED-based network congestion control algorithm. The algorithm comprises the following steps of: segmentally processing different congestion states by introducing a fifth-order classification method in the statistics, dividing the network congestion states by using five indexes of weak, slight, medium, serious and severe, and dividing the average queue utilization rate into five regions, each of which corresponds to a network congestion state; calculating the discard probability of the average data size of the regions by utilizing a liner function of a data size distribution probability; and discarding data packets in corresponding regions in the probability according to the current congestion state to increase the available space of queues so as to avoid the network congestion. In the algorithm, original ten configuration parameter projects of the conventional algorithm are reduced to two, so that the configuration difficulty is obviously reduced. Under the same network scene, the average queue cache utilization rate is about 25 percent higher than that of an RED derivative algorithm. The algorithm improves the network congestion control effect, can flexibly adjust the packet loss number according to the congestion degree, and effectively inhibit network jitter.

Description

technical field [0001] The invention belongs to the technical field of computer network management, and relates to a network congestion control algorithm in network quality of service (QoS: Quality of Service) management, in particular to a RED-based network congestion control algorithm. Background technique [0002] In 1988, American scientist Van Jacobson and others discovered that network congestion is caused by links or network nodes carrying excessive data, and the queue buffer of network nodes is full, resulting in a large amount of data being discarded and service response delays increasing. As the scale of network applications continues to expand, the demand for network bandwidth resources continues to rise, and network congestion occurs frequently under limited bandwidth capacity. Network congestion control has become a key factor in improving network service quality. For the case of not changing the physical structure of the network, how to maximize the control of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/56H04L12/24H04L12/861
Inventor 邬平吴斌王立新马继涛诸开梅尚朝秋黄红伟谭鹏李俊杜军李鑫邓艺卢泓州刘薇
Owner 云南省科学技术情报研究院
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