CN102025732A - Dynamic adaptive cognitive network quality of service (QoS) mapping method - Google Patents
Dynamic adaptive cognitive network quality of service (QoS) mapping method Download PDFInfo
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Abstract
The invention discloses a dynamic adaptive cognitive network quality of service (QoS) mapping method, comprising the following steps: grading a user on a user plane; comparing the service priority on a service plane; setting up an objective function on a strategy plane to adaptively adjust the weighting mode of the QoS of the strategy plane; and discussing the method for measuring obtained parameters on a control panel. By utilizing the method provided by the invention, the different requirements of different service-based cognitive network users for different QoS are met, the dynamic parameters among the user plane, the service plane, the strategy plane and the control plane are mapped, and the maximum utilization of network resources is realized.
Description
Technical field
The present invention is directed to that cognition network can be managed, can monitor, measurable characteristics, on the basis for the professional different QoS demand that satisfies the cognition network different user, foundation is from the parameter maps between user plane, service plane, tactful plane, the control plane, the realization network resource utilization maximizes, and belongs to the technical field of cognition network QoS mapping method.
Background technology
In the face of the changeable network environment that becomes increasingly complex, the Network of increase day by day, conventional network techniques has been difficult to satisfy people's demand.Cognition network is a kind of new-type network technology, and the monitoring network state is inferred the problem that future network may occur, and according to policy library network is adjusted then, realizes that network dynamically updates.Providing different QoS grades to satisfy different user's requests on IP network is a focus of studying at present.
At present provide the research of QoS model mechanism many on IP network, main research is Differentiated Services and integrated service model.Traditional differentiated service grade of service classification is limited, and limited grade of service classification can not be satisfied Protean user's request.Cognition network has increased user plane, service plane, tactful plane, control plane on traditional OSI seven layer network model based.For the terminal use (being user plane), determine the service quality that this user should enjoy according to user's paying situation often, therefore, it is simple and direct that the QoS that needs describes, and but can not be understood by service plane.The rest may be inferred, and needed service parameter also is different between tactful plane, control plane, the service plane.This patent proposes cognition network QoS mapping mechanism, can solve the parameter maps problem of user plane, service plane, tactful plane, control plane.
Summary of the invention
Technical problem: the objective of the invention is to propose many plane parameters mapping mechanism, safeguard that cognition network QoS guarantees, realize the resource utilization maximization between each business based on the cognition network of business.
Technical scheme: the present invention adopts following technical scheme for achieving the above object:
The cognition network QoS mapping method of dynamic self-adapting of the present invention is classified to user's QoS grade, based on service priority each plane qos parameter has been carried out adaptively dynamically mapping, and concrete grammar is as follows:
The parameter of service plane is to drive with the business, and for the different service types of different user, the priority comparison step is as follows:
1) extracts each professional information, respectively record traffic type and user gradation;
2) each is professional id, type of service, user gradation are stored in the three-dimensional array;
3) relatively give professional type of service, respectively business is resequenced according to type of service priority;
4) for the identical business of type of service, relatively user gradation sorts according to user gradation;
5) preserve array and the result being saved in the database of service plane;
The strategy plane is as follows according to the concrete steps of qos parameter weight system of selection:
I) traversal policy database, selection meets the strategy of QoS of survice requirement;
Ii) according to the target function of fine granularity service:
Calculate the target of alternate strategies
Functional value;
Iii) select the strategy protocol of minimum target functional value, judge feasibility;
The iv) feasible qos policy that then generates, the infeasible qos parameter weight of then adjusting is changeed step I again);
Wherein, δ
iExpression is at possible strategy in the concrete professional policy library of a certain class, α
iThe weight of representing the policing parameter that such is professional, time t1 is the time of the qos parameter preserved in policy database last time, the time when t2 is calculating target function, v
iBe this qos parameter that measures constantly, δ
iBe the value of i qos parameter stipulating in the present policy library, δ
MaxIt is the maximum of i QoS index.
Beneficial effect: the proposition of cognition network QoS mapping mechanism, we can realize:
1) different QoS requirements of different user is classified, make cognition network can satisfy different levels user's different requirements;
2) proposed the priority comparative approach of different brackets client's different service types QoS quality, realized that user plane is to the mapping between the service plane parameter;
3) the qos parameter weight to tactful aspect has proposed the assessment objective function that self adaptation is adjusted, and has realized the awareness of strategy in the cognition network;
4) collection to the control plane parameter has proposed concrete measurement way, has realized the mapping of strategy to Control Parameter.
Description of drawings
Fig. 1 is cognitive agency and legacy network seven layer model architectural framework.Provided the relation between cognitive agency and the traditional seven layer model among the figure.
Fig. 2 is that the service plane service priority compares.Provided priority manner of comparison among the figure for the different business of different user grade.
Fig. 3 is the appraisal procedure that the QoS weight is dynamically adjusted on tactful plane.Provided step among the figure for the different service selection specific strategy of priority.
Embodiment
Describe in detail below in conjunction with the technical scheme of accompanying drawing to invention.
The present invention proposes a kind of cognition network QoS mapping mechanism of dynamic self-adapting, QoS guarantees in order to provide end to end, has finished the integral body mapping of user's request to mensurable technology, thereby has realized the total cost minimum of network, the network resource utilization maximization.The present invention will propose the unified cognition network QoS mapping ruler of a cover on to the basis of the research of cognition network and guarantee service quality.
As shown in Figure 1, the cognition network basic framework is on the basis of legacy network seven layer model, and cognitive agency is provided, and cognitive agency is divided into four planes, is control plane, tactful plane, service plane and user plane from the bottom up successively.
1. user plane qos parameter
User profile is mainly deposited in client layer panel data storehouse, mainly is made up of IP address and user gradation.
When a professional A entered terminal, at first user plane was judged this user's promoter according to business information, and promptly the grade of user a supposes that here this user is from grades of silver.The definition of user plane inquiry SLA service clause, i.e. table 1.
Table 1 user plane SLA terms of service parameter list
2. service plane qos parameter
As shown in Figure 2, the parameter of service plane is to drive with the business, and each business all has different QOS demands.The typical services of conversation type mainly contains voice, video telephone etc., and data flow quasi-representative business mainly contains video flowing, audio stream etc., and interactive class mainly contains web page browsing, and the backstage type mainly contains email class, ftp download etc.Unless the user is the priority of specified services initiatively, otherwise their priority is conversation type＞data stream type＞type of interaction＞backstage type successively.For type of service of the same race, the priority height that user promoter rank is high; For same user, professional preferential its QOS that ensures that type of service priority is high.For the different service types of different user, the priority comparison step is as follows:
1) extracts each professional information, respectively record traffic type and user gradation;
2) each is professional id, type of service, user gradation are stored in the three-dimensional array;
3) relatively give professional type of service, respectively business is resequenced according to type of service priority;
4) for the identical business of type of service, relatively user gradation sorts according to user gradation;
5) preserve array and the result being saved in the database of service plane.
3. tactful plane qos parameter
The parameter on strategy plane mainly is meant the bottom-layer network performance parameter that supports cognition network running environment.These performance parameters concentrate in the network of particular technology realization, are gathered termly by network management system to report.The most frequently used index mainly comprises parameters such as bandwidth, throughput, packet loss, time delay and time delay variation, shake in IP network, and we study as major parameter with bandwidth, packet loss, time delay and shake here.Because different business, be different to the degree of concern of these four parameters, such as session service, strict to time delay and shake, and lower to packet loss and bandwidth requirement.Therefore, we distribute different initial weights to four parameters respectively for different business, adjust weighted value adaptively according to network state then.
The tactful plane parameter weight table of table 2
As shown in table 2, business 1, professional 2, professional 3 priority are successively decreased the state the when weight that is write down in the table is initial successively.For business 1,2,3, all have
For the strategy that business will adopt through a territory, setting up provides the target function of setting up the fine granularity service:
Here, δ i represents at possible strategy in the concrete professional policy library of a certain class.α
iThe weight of representing the policing parameter that such is professional is if professional 1 time delay, then α
i=B1, other by that analogy.Time t1 is the time of the qos parameter preserved in policy database last time, the time when t2 is calculating target function, v
iBe this qos parameter that measures constantly, δ
iIt is the value of i qos parameter stipulating in the present policy library.δ
MaxIt is the maximum of i QoS index.The target function value of more various strategies, get satisfy condition and the functional value minimum be strategy to be selected.If there is not strategy to satisfy condition, then need dynamically to adjust the network weight according to the network actual conditions.Such as this moment time delay bigger, then reduce the weight of time delay, thereby reduce the influence degree of time delay, thereby obtain feasible scheme the final goal functional value.
As shown in Figure 3, tactful plane is as follows according to the concrete steps of qos parameter weight selection strategy:
I) traversal policy database, selection meets the strategy of QoS of survice requirement;
Ii) according to the target function of fine granularity service:
Calculate the order of alternate strategies
Offer of tender numerical value;
Iii) select the strategy protocol of minimum target functional value, judge feasibility;
The iv) feasible qos policy that then generates, the infeasible qos parameter weight of then adjusting is changeed step I again)
4. control plane qos parameter
For the feasible scheme of having obtained, finally want control plane to implement.Therefore, the mapping one by one of implementation strategy plane parameter and control plane parameter.Selected network performance parameter all is the most basic parameter, and all the other parameters all are to obtain one by one by these selected performance parameters.Because cognition network is based on QoS target end to end, method of measurement all is based on and initiatively measures end to end at present.Initiatively measure and to detect network availability, time delay and throughput.For the parameter on tactful plane, control plane adopts and initiatively measures the mapping of finishing between parameter (supposition is measured and measured frequency n within a certain period of time):
A) time delay: with the mean value of all samples of collecting in the measuring period average delay as this cycle.
B) shake: choose the maximum delay IPTD in each measuring period
MaxWith minimal time delay IPTD
Min, the time delay rate of change in this measuring period is as shake.
C) packet loss: in the process that packet is transmitted in network, the quantity of being lost accounts for the ratio that sends total amount.
D) bandwidth: network under the situation that does not reduce other Business Stream transmission rates, the peak transfer rate of a Business Stream that can provide.Here use the method for measurement of basic SLOPS (sezf-Loading Peridic streajn).
Claims (1)
1. the cognition network QoS mapping method of a dynamic self-adapting is characterized in that user's QoS grade is classified, and based on service priority each plane qos parameter has been carried out adaptively dynamically mapping, and concrete grammar is as follows:
The parameter of service plane is to drive with the business, and for the different service types of different user, the priority comparison step is as follows:
1) extracts each professional information, respectively record traffic type and user gradation;
2) each is professional id, type of service, user gradation are stored in the three-dimensional array;
3) relatively give professional type of service, respectively business is resequenced according to type of service priority;
4) for the identical business of type of service, relatively user gradation sorts according to user gradation;
5) preserve array and the result being saved in the database of service plane;
The strategy plane is as follows according to the concrete steps of qos parameter weight system of selection:
I) traversal policy database, selection meets the strategy of QoS of survice requirement;
Ii) according to the target function of fine granularity service:
Calculate the target function value of alternate strategies;
Iii) select the strategy protocol of minimum target functional value, judge feasibility;
The iv) feasible qos policy that then generates, the infeasible qos parameter weight of then adjusting is changeed step I again);
Wherein, δ
iExpression is at possible strategy in the concrete professional policy library of a certain class, α
iThe weight of representing the policing parameter that such is professional, time t1 is the time of the qos parameter preserved in policy database last time, the time when t2 is calculating target function, v
iBe this qos parameter that measures constantly, δ
iBe the value of i qos parameter stipulating in the present policy library, δ
MaxIt is the maximum of i QoS index.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102394812A (en) * | 2011-10-21 | 2012-03-28 | 南京邮电大学 | Self-feedback dynamic self-adaption resource distribution method of cognitive network |
| CN102970170A (en) * | 2012-12-04 | 2013-03-13 | 南京富士通南大软件技术有限公司 | Setting method of integer weight of network transmission quantity |
| CN103209186A (en) * | 2013-04-08 | 2013-07-17 | 浪潮电子信息产业股份有限公司 | Design method for ensuring quality of service of businesses in heterogeneous network |
| CN103259719A (en) * | 2013-05-27 | 2013-08-21 | 重庆邮电大学 | Service awareness route protective method by means of Bayesian classification |
| WO2018028061A1 (en) * | 2016-08-12 | 2018-02-15 | 中兴通讯股份有限公司 | User plane data mapping method, device and system, and storage medium |
| CN108040121A (en) * | 2017-12-26 | 2018-05-15 | 广东电网有限责任公司电力调度控制中心 | A kind of multimedia service QoE resource allocation methods based on SDN |
| CN108737167A (en) * | 2018-05-04 | 2018-11-02 | 安徽师范大学 | A kind of network multimedia business span-domain QoE ensuring methods based on isomorphism stream |
| CN109684429A (en) * | 2018-12-18 | 2019-04-26 | 南京云灿信息科技有限公司 | A kind of low flyer identifying system and algorithm based on three-dimensional digital earth |
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| WO2009150042A1 (en) * | 2008-06-10 | 2009-12-17 | Telefonaktiebolaget L M Ericsson (Publ) | Policy control with predefined rules |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102394812A (en) * | 2011-10-21 | 2012-03-28 | 南京邮电大学 | Self-feedback dynamic self-adaption resource distribution method of cognitive network |
| CN102394812B (en) * | 2011-10-21 | 2014-01-22 | 南京邮电大学 | Self-feedback dynamic self-adaption resource distribution method of cognitive network |
| CN102970170A (en) * | 2012-12-04 | 2013-03-13 | 南京富士通南大软件技术有限公司 | Setting method of integer weight of network transmission quantity |
| CN102970170B (en) * | 2012-12-04 | 2015-11-18 | 南京富士通南大软件技术有限公司 | A kind of establishing method of integer weight of network transmission quantity |
| CN103209186A (en) * | 2013-04-08 | 2013-07-17 | 浪潮电子信息产业股份有限公司 | Design method for ensuring quality of service of businesses in heterogeneous network |
| CN103209186B (en) * | 2013-04-08 | 2017-05-03 | 浪潮电子信息产业股份有限公司 | Design method for ensuring quality of service of businesses in heterogeneous network |
| CN103259719B (en) * | 2013-05-27 | 2016-06-01 | 重庆邮电大学 | The service-aware route protection method of a kind of Bayes classification |
| CN103259719A (en) * | 2013-05-27 | 2013-08-21 | 重庆邮电大学 | Service awareness route protective method by means of Bayesian classification |
| WO2018028061A1 (en) * | 2016-08-12 | 2018-02-15 | 中兴通讯股份有限公司 | User plane data mapping method, device and system, and storage medium |
| CN108040121A (en) * | 2017-12-26 | 2018-05-15 | 广东电网有限责任公司电力调度控制中心 | A kind of multimedia service QoE resource allocation methods based on SDN |
| CN108737167A (en) * | 2018-05-04 | 2018-11-02 | 安徽师范大学 | A kind of network multimedia business span-domain QoE ensuring methods based on isomorphism stream |
| CN109684429A (en) * | 2018-12-18 | 2019-04-26 | 南京云灿信息科技有限公司 | A kind of low flyer identifying system and algorithm based on three-dimensional digital earth |
| CN109684429B (en) * | 2018-12-18 | 2022-06-21 | 南京云灿信息科技有限公司 | Low-altitude flight target identification system and algorithm based on three-dimensional digital earth |
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