CN103632209A - Queuing theory-based data transmission bandwidth prediction method in intelligent power distribution and consumption business - Google Patents

Abstract

The invention discloses a queuing theory-based data transmission bandwidth prediction method in the intelligent power distribution and consumption business, aiming at the problem that a great deviation exists between a predicted value of a bandwidth and an actual demand value easily caused by the traditional bandwidth prediction method. The method comprises the following steps of firstly acquiring intelligent power distribution and consumption business QoS (quality of service) requirement parameters from an application layer as basic data for bandwidth prediction, and converting the QoS parameters of an upper layer into queuing theory model parameters through the queuing theory; secondly determining a queuing theory-based transmission bandwidth prediction model according to the QoS constraint condition of the electric power telecommunication business, and meanwhile converting the prediction model into a linear constraint nonlinear programming solving model to obtain the optimal solution; finally obtaining a smallest predicted transmission bandwidth capable of ensuring the QoS requirement of the system business. According to the method, the predicted band width is obtained by modeling and solving through intelligent power distribution and consumption business QoS parameter mapping and the queuing theory, so that the bandwidth prediction deviation is reduced, and the system bandwidth utilization rate is optimized.

Description

A kind of intelligent adapted electric industry business data transfer bandwidth Forecasting Methodology based on waiting line theory

Technical field

The invention belongs to intelligent adapted technical field of telecommunications, relate in particular to a kind of intelligent adapted electric industry business data transfer bandwidth Forecasting Methodology based on waiting line theory.

Background technology

Along with the continuous appearance with new traffic business that develops rapidly of intelligent grid, computer network is widely used in electric system, power communication rapidly and efficiently reliably information transmission mode become the basis of electric system production and administration.First electric system, when its communication network of design, must determine various applied business and service quality (QoS:Quality of Service) requirement thereof that it needs, then predicts the transmission bandwidth that communication system is essential.So the prediction of energy communication service transmission bandwidth is the basis of electric power communication network planning, design and optimization, because it is may be too leading and increase system drops into and waste Internet resources not meet actual transmission bandwidth, also likely too optimistic estimation and make the transmission capacity of network become the bottleneck of its communication.The communication service of intelligent grid generally can be divided into language business, data service, video traffic and multimedia service.Research to intelligent grid communication service at present, particularly the qos requirement of all kinds of business is as the research of the performances such as speed, time delay, delay variation, drift, error code and priority, especially the bandwidth prediction of communication service research in intelligent grid is not yet carried out comprehensively and systematically.Although standard < < DL/T5391-2007 communication of power system designing technique regulation > > has provided the bandwidth prediction method that simple intutive forecasting and elasticity coefficient combine, but its elasticity coefficient only can be got limited several values, easily there is the extreme case that transmission bandwidth and actual needs bandwidth deviation are larger, thereby occur that the low or system QoS requirement of system bandwidth utilization factor such as can not meet at the problem.

Summary of the invention

The invention provides a kind of intelligent adapted electric industry business data transfer bandwidth Forecasting Methodology based on waiting line theory, its object is, in order to overcome existing bandwidth prediction method in above-mentioned prior art, easily causes having the problem that deviation is large between the predicted value of bandwidth and actual demand value.

An intelligent adapted electric industry business data transfer bandwidth Forecasting Methodology based on waiting line theory, comprises the following steps:

Step 1: the service quality QoS requirements parameter of obtaining intelligent adapted electricity business datum;

The intelligent adapted electricity business datum of wave recording device collection is added up and obtained qos requirement parameter, and described qos requirement parameter comprises QoS arrival rate λ, QoS packet loss P _land QoS time delay W _s;

Step 2: the qos requirement parameter that step 1 is obtained is converted into queue theory model parameter;

According to broad sense H4/M/1/m queuing model, realize in the following manner the mapping of qos parameter and queue theory model parameter:

The Packet Service arrival rate that QoS bandwidth demand Parameter Mapping is queue theory model, the system business that QoS delay parameter is mapped as queue theory model arrived to the averaging time of leaving, the Packet Service that QoS packet loss Parameter Mapping is queue theory model leaves probability, and QoS effective transmission speed Parameter Mapping is the packeting average number of queue theory model reception service per second;

Step 3: build the waiting line theory transmission bandwidth prediction optimization model based on qos requirement restriction on the parameters condition;

Set μ and represent QoS effective transmission speed, L _qwith η be Packet Service average waiting queue length and system strength in buffer memory in queue theory model, waiting line theory transmission bandwidth prediction optimization model is: $\begin{array}{c}\underset{\mathrm{\&mu;}}{\max }\mathrm{\&eta;}=\frac{\mathrm{\&lambda;}(1-P_L)}{\mathrm{\&mu;}}\\ s.t.P_L\&le;C_L\\ W_s\&le;C_T\\ L_q\&le;m\end{array}$

In formula, C _land C _tbe respectively maximum packet loss and the minimum transfer time delay in intelligent adapted electricity business datum, set; M is the length of buffer queue of system, for being not more than C _t* the positive integer of μ

Described system strength be business effectively arrive system and serviced after leave the ratio of the speed of system;

Step 4: optimum solution set { μ while adopting in linear restriction nonlinear planning solution step 3 model m=k _k;

Solving of following optimal model when solving of optimization model in step 3 is converted into given m=k value: $\begin{array}{c}\underset{\mathrm{\&mu;}}{\min }\frac{\mathrm{\&mu;}}{\mathrm{\&lambda;}(1-P_L\left(\mathrm{\&mu;}\right))}\\ s.t.P_L\left(\mathrm{\&mu;}\right)\&le;C_L\\ W_s\left(\mathrm{\&mu;}\right)\&le;C_T\\ L_q\left(\mathrm{\&mu;}\right)\&le;k\end{array},$ Utilize linear restriction nonlinear programming to try to achieve optimum solution μ _k;

Adopt enumerative technique to obtain optimum solution set { μ _k, k is since 1 value successively, until

Step 5: determine optimum solution;

Choose set { μ _kminimum value μ _*optimum solution as transmission bandwidth prediction optimal model;

Step 6: the predicted value that obtains the intelligent adapted electric industry business data transfer bandwidth based on waiting line theory is 8 * μ _** l, unit is kbps, each integrated data block length is l byte.

In described step 1 by utilize wave recording device according to cycle length T gather intelligent adapted electricity business datum, 10s<T<20s.

Described system strength be business effectively arrive system and serviced after leave the ratio of the speed of system; System strength is larger, illustrates when arrival rate one timing effective transmission speed is or/and packet loss is less.

Beneficial effect

The invention provides a kind of intelligent adapted electric industry business data transfer bandwidth Forecasting Methodology based on waiting line theory, for existing bandwidth prediction method, easily cause having the problem that deviation is large between the predicted value of bandwidth and actual demand value, first the method is obtained from the intelligent adapted electric industry of application layer and is engaged in qos requirement parameter as bandwidth prediction basic data, by waiting line theory, high-rise qos parameter index is converted into queue theory model parameter; Secondly according to the QoS constraint condition of energy communication service, determine the transmission bandwidth forecast model based on waiting line theory, forecast model is converted to linear restriction nonlinear planning solution model to obtain optimum solution simultaneously; Final acquisition can guarantee the minimum prediction transmission bandwidth of system business qos requirement.The method to obtain prediction bandwidth, can reduce bandwidth prediction deviation and optimization system bandwidth availability ratio by intelligent adapted electric industry business qos parameter mapping and waiting line theory model solution.

Accompanying drawing explanation

Fig. 1 is the inventive method overall flow figure;

Fig. 2 is intelligent adapted electric industry business multi-service list service queue theory model figure of the present invention;

Fig. 3 is power telecom network aggregation node communication traffic streams application drawing of the present invention.

Embodiment

Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that, following explanation is only exemplary, rather than in order to limit the scope of the invention and to apply.

An intelligent adapted electric industry business data transfer bandwidth Forecasting Methodology based on waiting line theory, as shown in Figure 1, comprises the following steps:

Step 1: the service quality QoS requirements parameter of obtaining intelligent adapted electricity business datum;

The intelligent adapted electricity business datum of wave recording device collection is added up and obtained qos requirement parameter, and described qos requirement parameter comprises QoS arrival rate λ, QoS packet loss P _land QoS time delay W _s;

Step 2: the qos requirement parameter that step 1 is obtained is converted into queue theory model parameter;

As shown in Figure 2, the communication service data of intelligence adapted electricity comprises language business datum, data service data, video traffic data and multimedia service data totally 4 classes, if it is infinitely many that all kinds of business datums arrive the number of packet of system, all kinds of groupings arrive the process of system and obey Poisson distribution, and time of arrival, interval obeys index distribution and parameter were respectively λ ₁, λ ₂, λ ₃and λ ₄, arrive probability and be respectively p ₁, p ₂, p ₃and p ₄thereby the average time interval T that in system, grouping arrives just obeys 4 rank hyperexponential distribution H ₄.The service regulation of initialization system is First Come First Served, obeys the exponential distribution that parameter is μ the service time that waiter provides for each grouping, and then the model of above-mentioned queuing service system is broad sense H ₄/ M/1/m queuing model.

According to broad sense H4/M/1/m queuing model, realize in the following manner the mapping of qos parameter and queue theory model parameter:

The Packet Service arrival rate that QoS bandwidth demand Parameter Mapping is queue theory model, the system business that QoS delay parameter is mapped as queue theory model arrived to the averaging time of leaving, the Packet Service that QoS packet loss Parameter Mapping is queue theory model leaves probability, and QoS effective transmission speed Parameter Mapping is the packeting average number of queue theory model reception service per second;

Step 3: build the waiting line theory transmission bandwidth prediction optimization model based on qos requirement restriction on the parameters condition;

Set μ and represent QoS effective transmission speed, L _qwith η be Packet Service average waiting queue length and system strength in buffer memory in queue theory model, waiting line theory transmission bandwidth prediction optimization model is: $\begin{array}{c}\underset{\mathrm{\&mu;}}{\max }\mathrm{\&eta;}=\frac{\mathrm{\&lambda;}(1-P_L)}{\mathrm{\&mu;}}\\ s.t.P_L\&le;C_L\\ W_s\&le;C_T\\ L_q\&le;m\end{array}$

In formula, C _land C _tbe respectively maximum packet loss and the minimum transfer time delay in intelligent adapted electricity business datum, set; M is the length of buffer queue of system, for being not more than C _t* the positive integer of μ

Described system strength be business effectively arrive system and serviced after leave the ratio of the speed of system;

Step 4: optimum solution set { μ while adopting in linear restriction nonlinear planning solution step 3 model m=k _k;

Solving of following optimal model when solving of optimization model in step 3 is converted into given m=k value: $\begin{array}{c}\underset{\mathrm{\&mu;}}{\min }\frac{\mathrm{\&mu;}}{\mathrm{\&lambda;}(1-P_L\left(\mathrm{\&mu;}\right))}\\ s.t.P_L\left(\mathrm{\&mu;}\right)\&le;C_L\\ W_s\left(\mathrm{\&mu;}\right)\&le;C_T\\ L_q\left(\mathrm{\&mu;}\right)\&le;k\end{array},$ Utilize linear restriction nonlinear programming to try to achieve optimum solution μ _k;

Adopt enumerative technique to obtain optimum solution set { μ _k, k is since 1 value successively, until

Step 5: determine optimum solution;

Choose set { μ _kminimum value μ _*as the optimum solution of transmission bandwidth prediction optimal model, and optimum solution is designated as

Step 6: the predicted value that obtains the intelligent adapted electric industry business data transfer bandwidth based on waiting line theory is 8 * μ _** l, unit is kbps, each integrated data block length is l byte.

In described step 1 by utilize wave recording device according to cycle length T gather intelligent adapted electricity business datum, 10s<T<20s.

System strength is that business arrives system and the ratio that leaves the speed of system, and system strength is larger, illustrates when arrival rate one timing effective transmission speed is or/and packet loss is less.

As shown in Figure 3, multiple business cut-in convergent node, analyzes by upper strata qos parameter, calculates and realizes the intelligent adapted electric industry business bandwidth prediction that QoS of survice requirement guarantees, then carry out system business minimum and require transmission bandwidth to distribute, finally access power communication backbone network.

The above; be only the present invention's embodiment preferably, but protection scope of the present invention is not limited to this, is anyly familiar with in technical scope that those skilled in the art disclose in the present invention; the variation that can expect easily or replacement, within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.

Claims (2)

1. the intelligent adapted electric industry business data transfer bandwidth Forecasting Methodology based on waiting line theory, is characterized in that, comprises the following steps:

Step 1: the service quality QoS requirements parameter of obtaining intelligent adapted electricity business datum;

The intelligent adapted electricity business datum of wave recording device collection is added up and obtained qos requirement parameter, and described qos requirement parameter comprises QoS arrival rate λ, QoS packet loss P _land QoS time delay W _s;

Step 2: the qos requirement parameter that step 1 is obtained is converted into queue theory model parameter;

According to broad sense H4/M/1/m queuing model, realize in the following manner the mapping of qos parameter and queue theory model parameter:

The Packet Service arrival rate that QoS bandwidth demand Parameter Mapping is queue theory model, the system business that QoS delay parameter is mapped as queue theory model arrived to the averaging time of leaving, the Packet Service that QoS packet loss Parameter Mapping is queue theory model leaves probability, and QoS effective transmission speed Parameter Mapping is the packeting average number of queue theory model reception service per second;

Step 3: build the waiting line theory transmission bandwidth prediction optimization model based on qos requirement restriction on the parameters condition;

Set μ and represent QoS effective transmission speed, L _qwith η be Packet Service average waiting queue length and system strength in buffer memory in queue theory model, waiting line theory transmission bandwidth prediction optimization model is: $\begin{array}{c}\underset{\mathrm{\&mu;}}{\max }\mathrm{\&eta;}=\frac{\mathrm{\&lambda;}(1-P_L)}{\mathrm{\&mu;}}\\ s.t.P_L\&le;C_L\\ W_s\&le;C_T\\ L_q\&le;m\end{array}$

In formula, C _land C _tbe respectively maximum packet loss and the minimum transfer time delay in intelligent adapted electricity business datum, set; M is the length of buffer queue of system, for being not more than C _t* the positive integer of μ

Described system strength be business effectively arrive system and serviced after leave the ratio of the speed of system;

Step 4: optimum solution set { μ while adopting in linear restriction nonlinear planning solution step 3 model m=k _k;

Solving of following optimal model when solving of optimization model in step 3 is converted into given m=k value: $\begin{array}{c}\underset{\mathrm{\&mu;}}{\min }\frac{\mathrm{\&mu;}}{\mathrm{\&lambda;}(1-P_L\left(\mathrm{\&mu;}\right))}\\ s.t.P_L\left(\mathrm{\&mu;}\right)\&le;C_L\\ W_s\left(\mathrm{\&mu;}\right)\&le;C_T\\ L_q\left(\mathrm{\&mu;}\right)\&le;k\end{array},$ Utilize linear restriction nonlinear programming to try to achieve optimum solution μ _k;

Adopt enumerative technique to obtain optimum solution set { μ _k, k is since 1 value successively, until

Step 5: determine optimum solution;

Choose set { μ _kminimum value μ _*optimum solution as transmission bandwidth prediction optimal model;

Step 6: the predicted value that obtains the intelligent adapted electric industry business data transfer bandwidth based on waiting line theory is 8 * μ _** l, unit is kbps, each integrated data block length is l byte.

2. the intelligent adapted electric industry business data transfer bandwidth Forecasting Methodology based on waiting line theory according to claim 1, it is characterized in that, in described step 1 by utilize wave recording device according to cycle length T gather intelligent adapted electricity business datum, 10s<T<20s.

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