CN101958843A - Intelligent routing selection method based on flow analysis and node trust degree - Google Patents
Intelligent routing selection method based on flow analysis and node trust degree Download PDFInfo
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- CN101958843A CN101958843A CN2010105262623A CN201010526262A CN101958843A CN 101958843 A CN101958843 A CN 101958843A CN 2010105262623 A CN2010105262623 A CN 2010105262623A CN 201010526262 A CN201010526262 A CN 201010526262A CN 101958843 A CN101958843 A CN 101958843A
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Abstract
The invention discloses an intelligent routing selection method based on flow analysis and node trust degree, which belongs to the technical field of networks and is suitable for the situation that a plurality of routings exist between an inlet router node and a target node and each routing only passes one intermediate node. The method comprises the steps of: implementing differentiation service among different businesses through business identification and marking, and then selecting a link with smaller load and higher trust degree through link flow prediction and the trust degree of an evaluation node. The method guarantees the safety at the same time of realizing load balance, thereby realizing the real-time, intelligent and dynamic routing.
Description
Technical field
The present invention relates to route selection method, relate in particular to a kind of intelligent routing method, belong to networking technology area based on flow analysis and node confidence.
Background technology
In recent years, along with the development of technique of internet, network access technique is variation day by day, complicated day by day network has been managed the focus of paying close attention to into people.Simultaneously, the new business that continues to bring out has brought new problem also for the monitoring of network and management, and the technology of traffic identification is arisen at the historic moment.Identifying miscellaneous service from the other class of complicated service stream center opening door, also is the basis that ensures network service quality (QoS).
On the other hand, abnormal flows such as D0S attacks, a large amount of spam, downloads of P2P are wantonly taking huge bandwidth, cause network congestion, bring tremendous influence to the QoS of network.How correctly to discern and isolate this type of abnormal flow, and predict link flow exactly, so that take measures before network goes wrong, it is particularly important also to seem in trustable network.In addition, trustable network require the expecting of network behavior, can monitoring, may command, can manage.Therefore, the degree of belief of node is evaluated at critical role in the trustable network.
Present route technology is based upon on the basis by traffic identification such as port detection, deep-packet detection mostly, utilize intelligent Agent, ant group algorithm to wait and realize route technology, but seldom consider the real-time monitoring of data flow and link flow and the balance of load.Route selection method based on ant group algorithm or traffic identification or degree of belief also has certain limitation, the rare method for routing that several different methods is combined merely.
Summary of the invention
The object of the present invention is to provide a kind of intelligent routing method, many routes and every route being arranged only under the situation through an intermediate node between ingress router node and the destination node, can when realizing load balance, guarantee the fail safe of network.
The thinking of the inventive method is at first by the Differentiated Services between traffic identification and the mark realization different business, choose the link that load is less and degree of belief is higher by the degree of belief of link flow prediction and assessment node again, when realizing load balance, guaranteed fail safe.Technical scheme of the present invention is specially:
A kind of intelligent routing method based on flow analysis and node confidence is used at ingress router node and order
Node between many routes and every route are arranged only through the situation of an intermediate node, it is characterized in that, comprise following each step:
Steps A, utilize the packet-capturing instrument to catch packet in real time at ingress router node place and put into buffer queue; Gather the flow of each the bar link that links to each other with this router simultaneously;
The class of service of step B, recognition data bag also is sent to the corresponding business flow queue according to its class of service with this packet;
The current flow of each bar link that step C, collection link to each other with ingress router, and predict next flow constantly of described each bar link; Utilize the degree of belief of described each the bar link intermediate node of degree of belief administrative model calculating of Ersin Uzun and Mark Resat Pariente;
Step D, be followed successively by each Business Stream is selected predicted flow rate and degree of belief ratio minimum from idle link link according to priority order from high to low; When priority is identical, then according to first come first served basis.
Compare existing route selection method, the inventive method has the following advantages:
One, combines with traffic identification, realized professional differentiation;
The situation that meets of the current link of two, flow analysis module real-time estimate for Route Selection provides information, has been avoided net
Network congested;
Three, link flow and node confidence are combined as a parameter selecting route, balanced loaded
By also having guaranteed simultaneously the confidence level of network;
Four, guaranteed the service quality of network, realized network measurable, can manage, may command.
Description of drawings
Fig. 1 is the flow chart of the intelligent routing method based on flow analysis and node confidence of the present invention.
Embodiment
Below in conjunction with accompanying drawing technical scheme of the present invention is elaborated:
As shown in Figure 1, the inventive method is carried out Route Selection according to following steps:
Steps A, utilize the packet-capturing instrument to catch packet in real time at ingress router node place and put into buffer queue; Gather the flow of each the bar link that links to each other with this router simultaneously;
In this step, carry out the real-time collection of data, utilize the packet-capturing instrument to catch packet in real time and put into the buffer queue of ingress router, so that carry out professional identification and flow analysis at ingress router node place; Gather the flow of each the bar link that links to each other with this router simultaneously, prepare for predicting next flow constantly of this link.
The class of service of step B, recognition data bag also is sent to the corresponding business flow queue according to its class of service with this packet;
In this embodiment, the packet that captures is carried out the identification of class of service and adopt following method:, distinguish different business with the corresponding relation of different business according to predefined DSCP value by the DSCP value among the DS field that analyzes packet.Certainly also can adopt separately or the existing business recognition method of integrated use, for example:
(1) port match method: the port mapping table according to setting up in advance, identify the used port of different business;
(2) deep-packet detection: the content to data packet header or payload encapsulation is analysed in depth the different business of discerning;
(3) payload characteristic coupling: the payload character string of the packet of different business has certain feature, can identify the business that some uses dynamic port or pseudorandom port in view of the above.
The current flow of each bar link that step C, collection link to each other with ingress router, and predict next flow constantly of described each bar link; Utilize the degree of belief of described each the bar link intermediate node of degree of belief administrative model calculating of Ersin Uzun and Mark Resat Pariente;
This embodiment adopts FARIMA model and small echo to change the method that combines and predicts next flow constantly of described each bar link, wherein the FARIMA model is a prior art, particular content can be referring to document [Hu Yuqin, Tan Xianhai, Song Zhengyang. based on network modelling and the performance evaluation of FARIMA. computer engineering and design, 2008,29 (18): 4666-4668.], concrete volume forecasting is according to following each step:
A. with the t that obtains constantly the
The flow of bar link
Carry out 3 layers of Mallat wavelet decomposition with the db3 small echo, obtain approximate part
And detail section
B. will be similar to part and detail section and carry out single reconstruct with the Mallat algorithm respectively, obtain the approximate portion after the reconstruct
Divide
And detail section
C. will
Carry out volume forecasting with the FARIMA model respectively, the component after obtaining predicting
With
Coefficient vector
Can draw by the following formula iteration:
Wherein,
For
Flow constantly,
Be coefficient vector,
With
For the coefficient vector parameter and have
+
=1,
D. obtain the predicted flow rate of each link
This embodiment is when the degree of belief of computing node, (particular content can be referring to document [Su Han to have adopted the degree of belief administrative model of Ersin Uzun and Mark Resat Pariente, Wang Yun, in the P2P environment based on the research [J] of the service route system of degree of belief. computer application research, 2006(9), 230-233]), and it is revised, specifically carry out degree of belief and calculate according to following each step:
(1) obtains the trust value Tv of each node; Adopt 5 grades of grade scales, the initial nodal value that adds is 0,
Complete believable node trust value is 3, and insincere node trust value is-1;
(2) revise the degree of belief vector with the degree of belief administrative model of Ersin Uzun and Mark Resat Pariente
, adopt the eight-digit binary number vector.For example node A is 11101000 to the degree of belief vector of Node B, and this value was revised as 11110100 after A and B success was mutual, otherwise was 01110100, and the success or not result (1 be successfully, and 0 is to fail) after promptly mutual places the vector first place, and all the other move to right;
(3) confidence rate
Be calculated as
// 2
8In order to reduce the complexity of calculating, we simplify this node of calculating
Degree of belief TR be TR=Tv*
Step D, be followed successively by each Business Stream is selected predicted flow rate and degree of belief ratio minimum from idle link link according to priority order from high to low; When priority is identical, then according to first come first served basis.This step specifically comprises following each step:
(1) upgrade routing table, list item is followed successively by SAdd, DAdd, and DSCPvalue, TR, PL, expression respectively:
The degree of belief of source IP address, purpose IP address, DSCP value, next-hop node, the predicted flow rate of next-hop node respective links;
(2) calculate the value of each node PL/TR, be followed successively by each Business Stream from the free time according to priority order from high to low
Select the link of the value minimum of PL/TR in the link; When priority is identical, then according to first come first served basis.The following method of concrete employing:
A. putting the busy not busy value of all links is 0, and expression is idle;
B. be followed successively by each Business Stream is selected PL/TR from idle link value minimum according to priority order from high to low
Link, and to put the busy not busy value of this link be 1; When priority is identical, then according to first come first served basis;
C. behind the Business Stream end of transmission, putting the busy not busy value of this link is 0;
D. be followed successively by each Business Stream according to the method described above and select link, until all Business Streams all till the end of transmission;
When Business Stream quantity during, wait for idle link greater than number of links.
Claims (4)
1. the intelligent routing method based on flow analysis and node confidence is used for having between ingress router node and destination node many routes and every route only through the situation of an intermediate node, it is characterized in that, comprises following each step:
Steps A, utilize the packet-capturing instrument to catch packet in real time at ingress router node place and put into buffer queue; Gather the flow of each the bar link that links to each other with this router simultaneously;
The class of service of step B, recognition data bag also is sent to the corresponding business flow queue according to its class of service with this packet;
The current flow of each bar link that step C, collection link to each other with ingress router, and predict next flow constantly of described each bar link; Utilize the degree of belief of described each the bar link intermediate node of degree of belief administrative model calculating of Ersin Uzun and Mark Resat Pariente;
Step D, be followed successively by each Business Stream is selected predicted flow rate and degree of belief ratio minimum from idle link link according to priority order from high to low; When priority is identical, then according to first come first served basis.
2. according to claim 1 based on the intelligent routing method of flow analysis and node confidence, it is characterized in that, next flow constantly of described each the bar link of prediction among the described step C, specifically according to following steps:
With the t that obtains constantly the
The flow of bar link
Carry out 3 layers of Mallat wavelet decomposition with the db3 small echo,
Obtain approximate part
And detail section
To be similar to part and detail section respectively the Mallat algorithm carry out single reconstruct, obtain the approximate part after the reconstruct
Will
Carry out volume forecasting with the FARIMA model respectively, the component after obtaining predicting
With
Coefficient vector
Can draw by the following formula iteration:
Wherein,
For
Flow constantly,
Be coefficient vector,
With
For the coefficient vector parameter and have
+
=1,
D. obtain the predicted flow rate of each link
3. according to claim 1 based on the intelligent routing method of flow analysis and node confidence, it is characterized in that, the described degree of belief administrative model that utilizes Ersin Uzun and Mark Resat Pariente calculates the degree of belief of all intermediate nodes in described each bar link, specifically according to following steps:
Obtain the trust value Tv of each node; Adopt 5 grades of grade scales, the initial nodal value that adds is 0, and complete believable node trust value is 3, and insincere node trust value is-1;
Degree of belief administrative model with Ersin Uzun and Mark Resat Pariente is revised the degree of belief vector
, adopt the eight-digit binary number vector;
Confidence rate
Be calculated as
/ 2
8, the degree of belief TR of this node is TR=Tv*
4. according to claim 1 based on the intelligent routing method of flow analysis and node confidence, it is characterized in that described step D specifically comprises following each step:
Upgrade routing table, list item is followed successively by SAdd, DAdd, DSCPvalue, TR, PL, expression respectively: the degree of belief of source IP address, purpose IP address, DSCP value, next-hop node, the predicted flow rate of next-hop node respective links;
Calculate the value of each node PL/TR, be followed successively by each Business Stream is selected the value minimum of PL/TR from idle link link according to priority order from high to low; When priority is identical, then according to first come first served basis.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103780501A (en) * | 2014-01-03 | 2014-05-07 | 濮阳职业技术学院 | Peer-to-peer network traffic identification method of inseparable-wavelet support vector machine |
| CN109729017A (en) * | 2019-03-14 | 2019-05-07 | 哈尔滨工程大学 | A kind of load-balancing method based on DPI prediction |
| CN112398911A (en) * | 2020-10-22 | 2021-02-23 | 成都中讯创新科技股份有限公司 | Multi-channel network scheduling method based on FC network |
| CN113395170A (en) * | 2021-04-29 | 2021-09-14 | 国网浙江省电力有限公司嘉兴供电公司 | Intelligent robot data transmission method based on linear topology transmission |
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| CN1412999A (en) * | 2002-05-22 | 2003-04-23 | 华为技术有限公司 | Network frame quick distribution method with flow rate control in network equipment |
| US20060233175A1 (en) * | 2005-03-30 | 2006-10-19 | Yi Ge | Traffic balancing apparatus and method, and network forwarding apparatus and method using the same |
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| 《计算机工程与设计》 20080930 胡玉清等 基于FARIMA的网络建模与性能分析 第4666页右栏 1，3，4 第29卷, 第18期 2 * |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103780501A (en) * | 2014-01-03 | 2014-05-07 | 濮阳职业技术学院 | Peer-to-peer network traffic identification method of inseparable-wavelet support vector machine |
| CN103780501B (en) * | 2014-01-03 | 2017-02-15 | 濮阳职业技术学院 | Peer-to-peer network traffic identification method of inseparable-wavelet support vector machine |
| CN109729017A (en) * | 2019-03-14 | 2019-05-07 | 哈尔滨工程大学 | A kind of load-balancing method based on DPI prediction |
| CN109729017B (en) * | 2019-03-14 | 2023-02-14 | 哈尔滨工程大学 | Load balancing method based on DPI prediction |
| CN112398911A (en) * | 2020-10-22 | 2021-02-23 | 成都中讯创新科技股份有限公司 | Multi-channel network scheduling method based on FC network |
| CN112398911B (en) * | 2020-10-22 | 2022-07-15 | 成都中讯创新科技股份有限公司 | Multichannel network scheduling method based on FC network |
| CN113395170A (en) * | 2021-04-29 | 2021-09-14 | 国网浙江省电力有限公司嘉兴供电公司 | Intelligent robot data transmission method based on linear topology transmission |
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