JPH04123647A - Service influence degree evaluating system at the time of abnormality of network - Google Patents

Abstract

PURPOSE:To automatize the fault influence degree evaluation work by executing the processing of a traffic prediction, a loss probability calculation. etc., by using a fault search result and understanding traffic information, network constituting information, and routing information. CONSTITUTION:An each ground traffic data input part 19 collects traffic data of every ground and every time zone through an external interface part 18, adds attribute information thereto, and stores it in an each ground traffic data base part 20. An on-line processing part 17 calculates a service influence degree at the time when the network is abnormal. A modified term derived by an error correcting method is added to a predicted value derived by an each ground traffic prediction type calculating part 21. Subsequently, from an each ground traffic predicted value derived by an each ground traffic predicted value correcting part 23 and each ground carried traffic derived by an each ground carried traffic calculating part 25, each ground loss probability is derived.

Description

[Detailed Description of the Invention] [Industrial Application Field] The present invention relates to a service impact evaluation method in the event of a network abnormality, and in particular, the present invention relates to a method for evaluating the degree of service impact in the event of a network abnormality. It is concerned with quantitatively understanding the impact of network abnormalities on service quality when a state in which equipment that makes up the network degrades or loses functionality due to abnormal congestion or abnormal congestion occurs, and the quality of service deteriorates. This system realizes mechanization or automation of network management operations in the event of network abnormalities, which have traditionally been performed manually.

[Conventional technology]

When providing telephone services through a communication network, it is desirable to connect all calls that request connection, but this requires excessive equipment. For this reason, in network operation, a position has been taken to set acceptable control limits for service quality and maintain service quality within an acceptable range.

Traditionally, service quality evaluation during network operation has been carried out by monitoring the processing status of each device that makes up the communication network, such as efficiency, using the operation system. There was no quantitative understanding of service quality itself. In addition, when a network abnormality occurs, information may not be obtained from the equipment at the abnormal location, making it impossible to understand the impact of the network abnormality on equipment processing capacity degradation, such as system failure, functional decline, or loss of functionality. .

For this reason, understanding of the network abnormality situation depends on the judgment of each operator, and problems such as regional differences in network abnormality measures arise due to inconsistency in the judgment results.

In recent years, with the aim of improving serviceability through effective use of low-load lines in communication networks, a method has been proposed in which the traffic detour route selection method is not fixed but is changed depending on the time of day and network conditions. This method is called a dynamic routing method. This method is effective when local line congestion occurs, but because the routing method is determined based on traffic data collected under normal conditions with no network abnormalities, it is effective when local line congestion occurs. If the network configuration or equipment processing capacity changes due to network abnormalities, it is difficult to implement appropriate network equipment.

[Problem to be solved by the invention]

In the future, it is expected that a variety of services will be provided as communication networks become 15DN, and it is desirable to appropriately operate communication networks so that each service satisfies a certain level of quality. When operating a communication network, accurately grasp the quality of the services being provided, and in particular, in the event of a network abnormality that significantly degrades service quality, promptly implement appropriate measures based on service impact assessment from the perspective of restoring quality. The technology to do this is important. SUMMARY OF THE INVENTION An object of the present invention is to provide a method for evaluating the degree of service impact during a network abnormality, which solves the problem of determining the degree of service quality deterioration when a network abnormality occurs.

[Means to solve the problem]

In order to achieve the above object, the present invention provides a communication network consisting of a plurality of switching centers and a plurality of line groups, in a situation where the functionality of the equipment constituting the network is degraded or lost due to equipment failure or abnormal congestion. When a network abnormality occurs and the service quality deteriorates, in the service impact evaluation method during network abnormality, which evaluates the influence of the network abnormality on the service quality, the daily exchange rate information obtained from the information collection device is traffic and
Means for determining traffic prediction values for each area from a traffic database for each area that accumulates attribute information regarding the traffic for each area, past traffic for each area obtained from the traffic database for each area, and attribute information regarding the past traffic for each area. , a means for correcting the traffic prediction value for each area based on the traffic for each area on the day of the abnormality obtained from the information collection device, and network configuration information that accumulates information representing the connection relationship and processing capacity of the devices that make up the network. a database, a routing information database storing information determining traffic route selection, the traffic prediction value for each area, and the network configuration information database;
a network component device call loss rate calculation means for calculating a call loss rate at each switching center and each line group from the routing information data base; a traffic prediction value for each area; a call loss rate at each switching center and each line group; information database;
The present invention is characterized in that it includes a call loss probability calculation means for each area for calculating the quality of service between each switching center from the routing information database.

[For production]

In the present invention, as an evaluation measure of service quality, a call loss rate by region is used, which indicates the rate at which calls occurring between exchanges (by region) are lost due to requests from communication service users.

Since the present invention includes the following means, it operates as follows.

■Method for predicting the traffic to each area that enters the network In order to calculate the call loss rate by area, it is necessary to understand the traffic to each area that applies to the network, but when there is a network abnormality, this is not possible because information cannot be obtained from the abnormal location. There is. Therefore, the traffic to each area that will be added to the network is predicted, and the degree of service impact is determined based on the prediction results. This prediction is based on prediction processing based on traffic data for each area during normal times before the day of the abnormality (see ■-1 below), and forecasting that aims to improve prediction accuracy by reflecting trends in traffic for each area on the day of the abnormality in the prediction results. This is carried out in two steps: result modification processing (see ①-2 below).

■-1 Predicting traffic by area based on traffic data by area before the day of abnormality Using a traffic database for each area that stores attribute information, we predict traffic for each area and time zone.

■-2 Correcting the result of traffic prediction by region using traffic data by region on the day of abnormality In order to improve prediction accuracy by incorporating traffic trends on the day of abnormality into prediction,
The error correction method is applied to the traffic data by area obtained in real time, and the prediction result obtained in the above ①-1 is corrected.

■Method for determining the line group call loss rate/exchange station call loss rate Using the traffic prediction value for each destination obtained by the above method (■), a network configuration information database that corresponds to changes in the network due to network abnormalities, and a routing information database, each exchange station and each exchange Find the traffic added to the line group as a load. The call loss rate of the switching center and line group is determined from the determined traffic and the processing capacity of the equipment.

■Means for calculating the call loss rate for each area Using the results obtained in (2) and (3) above, the network configuration information database, and the routing information database, calculate the traffic carried for each area, and from that and the traffic prediction results for each area, calculate the call loss rate for each area. seek.

Examples will be described below based on the drawings.

[Example] A configuration example and a processing flowchart example of the present invention are shown in FIGS. 1, 2, and 3.

FIG. 1 shows an example of the configuration of an operation system that implements the present invention. In FIG. 1, a switching center 2 and a line group 3 in a communication network 1 are connected to an information collection device 4 via an information transfer path 14, and the information collection device 4 performs service impact evaluation via an information transfer path 11. It is connected to an operating system 5 that performs operations. The operation system 5 is composed of a processing device 6 that performs various processes, an output device 7 that displays the service impact degree determined by internal processing, and a database section 8 that stores information used in the processing. information and failure search operation system 9
The degree of service impact is calculated using the failure information sent from the service. The determined service impact degree is transferred to the network configuration determination operation system 10 and used to determine the network configuration for restoring service quality.

Figure 2 shows service impact evaluation operation system 5.
An example of the configuration is shown below. Information external to the operation system 5 can be used through the information transfer path 11 and the external input interface section 18 . Each part will be explained below.

Each of the units 18 to 30 of the operating system 5 is connected via an information transfer path 15.

The offline processing unit 16 is a part that performs traffic prediction for each area using traffic data for each area before the abnormality occurrence date, and is composed of a traffic data input unit 19 for each area and a traffic prediction formula calculation unit 21 for each area. The traffic data input unit 19 for each destination is connected to the external interface unit 1.
8, traffic data is collected for each location and each time period, and daily attribute information is added thereto, and the data is recorded in the location-specific traffic database section 20. The region-by-region traffic prediction formula calculation unit 21 analyzes the information in the region-specific traffic database unit 20, determines a region-specific traffic prediction formula, and obtains a region-specific predicted value.

The obtained prediction formula parameters and destination-specific traffic prediction values are recorded in the destination-specific traffic prediction formula database section 22.

The above processing is performed during normal times when no network abnormality occurs.

The online processing unit 17 is a part that calculates the degree of service impact in the event of a network abnormality, and is a part that calculates the degree of service impact in the event of a network abnormality.
3, a network component call loss probability calculation unit 24, a traffic carried by area calculation unit 25, a failure search result input unit 26,
It is composed of a call loss rate calculating section 29 for each region. When an abnormality in the communication network is detected, the traffic prediction value correction unit 23 for each destination
calculates the traffic prediction formula for each region using the traffic prediction value for each region accumulated in the traffic prediction formula database unit 22 for each region and the traffic data for each region on the day of abnormality that was collected in advance through the external input interface unit 18. The predicted value obtained in section 21 is corrected. There are various methods for correction, but in this embodiment, the correction term obtained by the error correction method is used in the traffic prediction formula calculation unit 2 for each destination.
Add to the predicted value obtained in step 1. On the other hand, the fault search result input unit 26 collects the fault search results such as the location of the abnormality and the scale of the abnormality from the external input interface unit 18, and records the information in the network configuration information database unit 28 as changes in the network configuration information. The network configuration device call loss probability calculation unit 24 uses the traffic prediction value for each area obtained by the area-specific traffic prediction value correction unit 23, the routing information database unit 27, and the network configuration information database unit 28 that reflects the failure search results. , calculate the traffic added to each switching center and each line group as a load, and calculate each switching center and each line group from the processing capacity information of each switching center and the number of lines of each line group collected from the network configuration information database unit 28. Find the call loss rate at . Next, the traffic to be carried by area calculation unit 25 calculates the traffic to be carried by area using the results from the network component device call loss probability calculation unit 24 and the databases of the routing information database unit 27 and the network configuration information database unit 28. The call loss rate calculation unit 29 calculates the call loss rate for each area from the traffic prediction value for each area obtained by the predicted traffic value correction unit 23 for each area and the traffic carried for each area calculated by the traffic carried for each area calculation unit 25. The service impact degree display section 30 displays the results of the call loss rate calculation section 29 for each destination as output information.

FIG. 3 shows an example of a processing flowchart for the call loss rate by region.

In offline processing 31 to 34 in FIG. 3, traffic prediction for each area is performed using traffic data for each area during normal times before the abnormality occurrence date. This process is carried out in the region-specific traffic data input section 19 and region-specific traffic prediction formula calculation section 21 shown in FIG. Known traffic prediction methods include a method using a time-series prediction model, a method using a prediction model based on a quantification type I method, and the like. Here, a case is shown in which a prediction model based on a quantification type I method is used as a traffic prediction method. In step 31, a traffic prediction model (linear linear equation) corresponding to the quantification type I method is set, and in step 32, traffic data for each area and each time period used to determine the prediction model is input. As this traffic data, data from normal times before the day when the network abnormality occurred is used. At step 33, the least squares method is applied to the traffic data input at step 32 to determine the parameters of the prediction equation. This determines the traffic prediction method for each destination. In step 34, a predicted traffic value for each destination is determined.

In online processing 35 to 39 in FIG. 3, the predicted traffic value obtained in step 34 is corrected using the traffic data for each destination on the day the network abnormality occurred. This modification process is performed in order to improve the prediction accuracy by reflecting the traffic trends of the day in the prediction, and is performed by the region-by-destination traffic predicted value modification unit 23 in FIG. The deviation between the current day's traffic data input in step 35 and the predicted value obtained in step 34 is determined in step 36, and the exponential smoothing method is applied to the deviation to obtain a predicted value of the deviation. In addition, if the traffic data input in 35 is applied directly to the deviation prediction, the fluctuation rate of the deviation numerator will increase, and the purpose of expressing the trend of the day will not be achieved. Therefore, in 37, a well-known AR model is used. 3 to smooth out the deviation.
In step 8, the deviation is predicted. It has been confirmed through simulation that the value of the parameter a of the exponential smoothing method used in Section 38 is approximately 0.3. In step 39, the traffic prediction value for each area is corrected by adding the deviation numerator measurement value obtained in step 38 above to the traffic prediction value obtained in step 34 above. With this method, when the error in the predicted value based on past data increases due to some reason, it is possible to improve the accuracy by correcting the predicted value.

Next, we will proceed to step 40 of the line group/switching center call loss rate calculation process in Figure 3.
In steps 42 to 42, the predicted traffic for each destination obtained in step 39 is used to calculate the call loss rate of the line group and the switching center. This process is performed in the network component call loss probability calculation unit 24 shown in FIG. In step 40, the traffic added as a load to each switching center and each line group is determined from the destination-specific traffic prediction value obtained in step 39, the network configuration information, and the routing information. From this result, network configuration information, and routing information, 4
In step 1, the call loss rate of each line group is determined using a known equivalent random method, and in step 42, the call loss rate of each switching center is determined based on the processing capacity information of each switching center in the network configuration information database unit 28.

In Figure 3, in step 43 of the calculation process of the traffic carried by each area and in step 44 of the calculation process of the call loss rate by area, the traffic carried by each area is calculated, and from this and the predicted traffic by area obtained in step 39 above, the call loss rate by area is calculated. demand. The processes 43 and 44 are respectively carried out in the destination traffic calculation section 25 and the destination call loss probability calculation section 29 in FIG. 4
In 3, the traffic prediction value for each destination, the line group call loss rate, and the switching center call loss rate obtained in 39.41.42 are used as input information, and while referring to the network configuration information database section 28 and the routing information database section 27, the traffic for each destination is calculated. Find the traffic that will be generated. 43 shows an example of a method for determining the traffic carried by destination. This method assumes the following prerequisites for the communication network configuration and route selection method. The target communication network is a network in which line groups are set up between all exchanges, and the route selection method (routing method) for traffic occurring between exchanges uses direct line groups as the first selection route. However, as the second selection route (detour route), a route passing through an arbitrary switching center is specified. If this assumption changes, the calculation formula 43 will be changed in various ways accordingly. In step 44, the call loss probability for each region is determined using the results of steps 39 and 43 above.

(Examples of references related to known calculation methods such as quantification type methods and AR models: ・Quantification theory and data processing, Chosen Shoten Publishing, 1982.6.20, ・Basics of data analysis, Morikita Publishing Co., Ltd., 1977, 4.20) ,・Statistical Dictionary, Toyo Keizai Inc., December 4, 1989) [Effects of the Invention] As described above, according to the present invention, traffic prediction can be performed using failure search results, communication traffic information, network configuration information, and routing information. , An operation system that performs processes such as call loss rate calculations can realize quantitative evaluation of service impact, and automate failure impact evaluation work, which is a part of network failure management work. Furthermore, by comparing and examining the obtained service impact degree and the network configuration, it becomes possible to accurately determine the network configuration for restoring service quality.

[Brief explanation of the drawing]

FIG. 1 shows an example of the configuration of the present invention, FIG. 2 shows an example of the configuration of a service impact evaluation operation system, and FIG. 3 shows an example of a processing flowchart for a call loss rate by area. l...Communication network 2...Switching center 3...Line group 4...Information collection device 5...Service impact evaluation operation system 6
... Processing device 7 ... Output device 8 ... Database unit 9 ... Failure search operation system IO ... Network configuration determination operation system 11, 12.13.1
4... Information transfer path 15... Information transfer path 16... Offline processing unit 17... Online processing unit 18... External input interface unit 19... Traffic data input unit for each destination 20... Area-specific traffic database unit 21... Area-specific traffic prediction formula calculation unit 22... Area-specific traffic prediction formula database unit 23... Area-specific traffic prediction value correction unit 24... Network component call loss rate calculation unit 25. ...Traffic calculation unit 26 carried by ground...
Failure search result input unit 27... Routing information database unit 28... Network configuration information database unit 29... Call loss rate calculation unit for each area 30... Service impact level (call loss rate for each area) display unit 31
...Setting the traffic prediction formula for each region and time zone 32.
... Input of past traffic data by region 33 ... Calculation of coefficient bll of prediction formula 34 ... Calculation of offline prediction y8 35 ... Input of traffic data by region for the day 36.
... Calculation of deviation Δ 37 ... Smoothing of deviation Δ using AR model 38 ...
・Calculation of deviation numerator measurement value Δ l+1 by exponential smoothing method 39
... Calculation of online predicted markdown 5 40 ... Calculation of traffic added to each exchange and each line group 41 ... Calculation of line group call loss rate 42 ... Calculation of exchange center call loss rate 43 ..., Toward Calculation of separately carried traffic 44...
・Calculation of call loss rate by region Patent applicant: Nippon Telegraph and Telephone Corporation

Claims (1)

[Claims] In a communication network consisting of a plurality of switching centers and a plurality of line groups,
When a network abnormality occurs where equipment that makes up the network degrades or loses functionality due to equipment failure or abnormal congestion, and the quality of service deteriorates, evaluate the impact of the network abnormality on the quality of service. In the service impact evaluation method, the traffic by area between exchanges obtained every day from an information collection device, the traffic database by area which accumulates the attribute information regarding the traffic by area, and the past traffic obtained from the traffic database by area. means for calculating a traffic prediction value for each area from the traffic for each area and attribute information regarding the past traffic for each area; a network configuration information database that stores information representing the connection relationships and processing capabilities of devices that make up the network; a routing information database that stores information that determines traffic route selection; and the traffic prediction value for each destination. , a network component call loss rate calculating means for calculating a call loss rate at each switching center and each line group from the network configuration information database and the routing information database; A method for evaluating the degree of service impact in the event of a network abnormality, characterized by comprising a call loss rate calculation means for each area, which calculates the quality of service between each exchange from the call loss rate at the network, the network configuration information database, and the routing information database. .