JP4780668B2 - Traffic analysis model construction method, apparatus, construction program, and storage medium thereof - Google Patents

Description

  The present invention relates to a traffic analysis model construction method, apparatus and construction program for analyzing traffic characteristics of an arbitrary user based on the attribute information and attribute information of an existing user accommodated in a network, and its The present invention relates to a storage medium.

  In accommodation design for accommodating a new user in an existing network or facility design for constructing a new network, it is indispensable to accurately analyze the traffic characteristics of the user newly accommodated in the network.

  Non-Patent Document 1 discloses a technique for classifying (categorizing) daily traffic fluctuations (resolution: 30 minutes) of each existing user according to the characteristics of the fluctuations using a clustering analysis technique.

  Non-Patent Document 2 discloses a technique for predicting a category to which the traffic belongs from traffic fluctuation data using the correspondence relationship between the category obtained by the classification method of Document 1 and individual traffic fluctuation as teacher data. ing.

  In Non-Patent Document 3, the traffic characteristics of dial-up users and ADSL line users are analyzed focusing on the difference of access lines, and characteristics that are hardly affected by the access line speed (TCP session duration, TCP connection rate, etc.) And the characteristics (transfer traffic volume) that are affected.

In Patent Document 1, time series data of communication traffic statistics measured every day, and calendar information indicating calendar attributes indicating communication traffic measurement dates including month information and day information are input to a neural network, A technique for predicting communication traffic up to k days ahead of the measured communication traffic is disclosed.
MR de Oliveira et al, "Cluster Analysis of Internet Users Based on Hourly Traffic Utilization," In Proc. Of the First International Working Conference on Performance Modeling and Evaluation of Heterogenious Networks (HET-NETs'03), July 2003. A. Nogueira et al, "Using Neural Networks to Classify Internet Users," in Proc. Of Advanced Industrial Conference on Telecommunications / Service Assurance with Partial and Intermittent Resources Conference / E-Learning on Telecommunicatrions Workshop (AICT / SAPIR / ELETE 2005), 2005. N. Vicari et al, "The Dependence of Internet User Traffic Characteristics on Access Speed," in Proc. Of the 25th Local Computer Networks (LCN) Conference, pp.670-677, Tampa, FL, USA, Nov. 2000. JP 2004-23114 A

  Each of the technologies disclosed in the above-mentioned documents is a technology for classifying or predicting the user's traffic characteristics based on the actual data. However, as with new users newly accommodated in the network, the actual traffic is It was impossible to predict the traffic characteristics of completely unknown users.

  The object of the present invention is to solve the above-described problems of the prior art and analyze the traffic characteristics of an arbitrary user based on the user attributes based on the attribute information of the existing users accommodated in the network and the actual traffic. It is an object of the present invention to provide a traffic analysis model construction method, apparatus, construction program, and storage medium thereof.

  According to the investigation by the inventors of the present invention, the traffic characteristics of each user are attribute information specific to each user (hereinafter referred to as “contract speed, contract service type, contract area, etc.”), gender, age, etc. If the user attributes are different, the traffic characteristics are greatly different. On the other hand, if the user attributes are similar, the traffic characteristics are often similar.

  10, 11, and 12 are diagrams showing the relationship between user attributes and traffic loads. Here, the ratio of low-load users with a small amount of traffic and other non-low-load users is shown for each user attribute. Show.

  FIG. 10 is a diagram illustrating a result of obtaining a ratio between a low load user and a non-low load user for each contract speed. FIG. 11 is a diagram in which the ratio between the low-load user and the non-low-load user is obtained for each contract area of each user. FIG. 12 is a diagram in which the ratio between the low-load user and the non-low-load user is obtained for each contract service type.

  In any of the figures, it can be seen that there is a significant relationship between the characteristics of actual traffic (traffic load) and user attributes. Therefore, it is understood that the traffic characteristics of an arbitrary user can be analyzed with high accuracy by obtaining the correspondence between the user attribute of the existing user and the actual traffic and applying the user attribute of the arbitrary user to this correspondence.

  Similarly, FIG. 13 is a diagram showing a relationship between a contract service type, which is one of user attributes, and a multidimensional vector which is one of traffic characteristics and represents characteristics of traffic fluctuation. Multidimensional vectors are categorized into "day type" where traffic is concentrated in the daytime and "night type" where traffic is concentrated at night. According to the figure, it is shown that whether the traffic characteristic is “day type” or “night type” depends on the contract service type.

  Further, FIG. 14 is a diagram showing the relationship between a day of the week, which is one of the calendar attributes described later, and a multidimensional vector representing the characteristics of traffic fluctuations. The “day type” in which the traffic characteristics are concentrated in the daytime and the nighttime. It is shown that which of the “night type” that concentrates on is dependent on the calendar attribute.

  The present invention has been made on the basis of the above knowledge, and based on the user attributes and actual traffic of existing users already accommodated in the network, the traffic characteristics of an arbitrary user are analyzed based on the attribute information. In the analysis model construction apparatus, a plurality of means for storing the actual traffic of the existing user, a means for storing the user attributes of the existing user, and a plurality of unit periods specified by a predetermined calendar attribute from the actual traffic of the existing user Traffic analysis means for analyzing the traffic characteristics of the user, teacher data generation means for collecting user attributes, calendar attributes, and correspondence characteristics of each traffic characteristic for each unit period and generating teacher data for traffic analysis, and the traffic characteristics for the user Traffic analyzed per unit period based on attributes and calendar attributes The analysis model, characterized in that it comprises a traffic analysis model construction means for constructing on the basis of the training data for the traffic analysis.

According to the present invention, the following effects are achieved.
(1) Based on the user attributes and actual traffic of existing users, correspondence between user attributes, calendar attributes, and traffic characteristics is determined for each unit period specified by calendar attributes, and traffic analysis is performed using these correspondences as teacher data. A model is built. Therefore, for a given user, if a unit period for which the traffic characteristic is to be analyzed is specified as a calendar attribute together with the user attribute, the traffic characteristic corresponding to the desired calendar attribute of the user can be analyzed.
(2) Since the analysis model was constructed using the correspondence between user attributes, calendar attributes, and traffic characteristics as teacher data, traffic characteristics corresponding to combinations of user attributes and calendar attributes that do not have existing user information are also analyzed. become able to.

  DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the best embodiment of the present invention will be described in detail with reference to the drawings. FIG. 1 is a diagram showing an example of a network to which a prediction result predicted using a traffic analysis system according to the present invention is applied. A plurality of GC stations (city networks) are connected to a core network via an NC (network center). The internal exchanges are connected by a ring network (GC ring), and a number of user terminals are connected to each GC station via a switch / router (S / R).

  FIG. 2 is a block diagram showing the configuration of the main part of the traffic analysis system according to the present invention. Here, the configuration unnecessary for the description of the present invention is omitted.

  The traffic analysis model construction unit 1 associates the user attributes of the existing users accommodated in the network with the traffic characteristics obtained by analyzing the actual traffic of the existing users for each unit period specified by a predetermined calendar attribute. Based on the above, a traffic analysis model that predicts traffic characteristics corresponding to an arbitrary calendar attribute of an arbitrary user from only the user attribute and the calendar attribute is constructed.

  For example, if “all day of the month” is designated as the calendar attribute, the traffic characteristics of “1 day”, the traffic characteristics of “2 days”,..., The traffic characteristics of “31 days” are obtained for each existing user. Each traffic characteristic is associated with a user attribute of the existing user. As the traffic characteristics, a traffic average value for one day (24 hours), a traffic standard deviation, and the like are obtained.

  Similarly, if “all month within the year” is specified as the calendar attribute, “January” traffic characteristics, “February” traffic characteristics, and “December” traffic characteristics are obtained for each existing user. Each traffic characteristic is associated with a user attribute of the existing user. As the traffic characteristics, an average value of traffic for one month, a traffic standard deviation, and the like are obtained.

  Similarly, if “daytime” is designated as the calendar attribute, the traffic characteristics of “0 hour range”, the traffic characteristic of “1 hour level”,... Each is obtained, and each traffic characteristic is associated with the user attribute of the existing user. As the traffic characteristics, an hourly traffic average value, a traffic standard deviation, and the like are obtained.

  As with the user attribute, a plurality of calendar attributes can be specified at the same time. For example, in the case of a combination of “all day in the month” and “day of the week”, “Monday of 3rd”, “25th of day” A designation such as “Friday” is also possible. In addition, in the case of a combination of “all week in the month” and “all month in the year”, “second week in January” and “first week in March” can be designated.

  The traffic prediction unit 2 uses the traffic analysis model constructed by the traffic analysis model construction unit 1 to predict the traffic characteristics of the unit period specified by the calendar attribute of a new user from only the user attribute. For example, if “25 days” is input as the calendar attribute together with the user attributes, the traffic characteristics of “25 days” of the new user corresponding to the user attributes are predicted.

  Similarly, if “25th day” and “Monday” are input as the calendar attribute together with the user attribute, the traffic characteristics of “Monday on 25th” of the new user corresponding to this user attribute are predicted.

  At this time, in the present embodiment, even when analyzing the traffic characteristics of “Monday on 25th”, it is not necessary to register the information of existing users corresponding to the combination of “25th” and “Monday”. Even if the information of the existing user corresponding to the combination is not registered, it can be analyzed based on the information of other day and day of the week.

  In the traffic analysis model construction unit 1, the existing user database (DB) 101 includes a user attribute storage unit 101 a that stores user attributes of a number of existing users, and a track record that stores track traffic of each existing user that is separately measured. In the existing user DB 101, the user attribute and the actual traffic are associated with each other on the existing user DB 101. FIG. 3 is a diagram schematically showing the configuration of the existing user DB 1, and user attributes and actual traffic are associated with each user ID that uniquely identifies each user (port number).

  In the present embodiment, various information including contract conditions such as contract speed, contract service type, contract area, and user gender and age are registered as user attributes. The actual traffic is periodically (eg, every hour) over a predetermined period (eg, one month) for each uplink and downlink traffic on the port that accommodates the access line of each existing user in the switch / router, for example. It is time-series measurement data obtained by tapping.

  Returning to FIG. 2, the load analysis unit 102 analyzes the actual traffic of each existing user for each unit period specified by a predetermined calendar attribute, and identifies traffic having a specific load characteristic. In this embodiment, the traffic of each unit period includes “low load” in which traffic hardly flows, “temporary load” in which traffic is temporarily generated for the purpose of data backup, and “normal load” other than the above. Classify either.

  The traffic characteristic analysis unit 103 analyzes the traffic characteristic of a unit period classified as a normal load. In this embodiment, the traffic characteristic analysis unit 103 includes a category classification unit 103a, and the traffic characteristics of the normal load are classified into three categories of “small”, “medium”, and “large” from the viewpoint of the average value, From the viewpoint of standard deviation, it is classified into three categories of “small”, “medium”, and “large”.

  Furthermore, in this embodiment, for example, a multidimensional (24-dimensional in this embodiment) vector composed of traffic volume per hour is also adopted as one of the traffic characteristics, and this multidimensional vector is classified into a plurality of categories. Is done.

  For example, if this multi-dimensional vector shows 24-hour traffic characteristics, whether the traffic is concentrated in the daytime or at night, that is, the vector component corresponding to the daytime is compared to the other components. Is large (day type), or is classified into two categories, “day type” and “night type”, depending on whether the vector component corresponding to the night time is larger than the other components (night type) .

  For example, as shown in FIG. 4, normal load traffic in which the traffic volume is relatively small, the traffic volume fluctuation is small, and the nighttime traffic volume is larger than the daytime traffic volume has an average value of “small”. The standard deviation is classified into “small” and the shape is classified into “night”. In addition, as shown in FIG. 5, normal load traffic having a relatively large traffic volume, a large traffic volume fluctuation, and a daytime traffic volume higher than a nighttime traffic volume has an average value of “large”, The standard deviation is classified as “large” and the shape is classified as “daytime”.

  Returning to FIG. 2, the traffic analysis teacher data generation unit 104 determines the correspondence between the user attribute, the calendar attribute, and the category classification result of the traffic characteristic for each unit period in which the traffic volume is classified into the normal load. The data is registered in the teacher data table 104a as teacher data.

  FIG. 6 is a diagram schematically showing an example of the teacher data table 104a, in which correspondences between user attributes, calendar attributes, and category classification results of each traffic characteristic are registered.

  Based on the teacher data registered in the traffic analysis teacher data table 104a, the traffic analysis model construction unit 106 analyzes a plurality of traffic characteristics corresponding to an arbitrary calendar attribute of an arbitrary user from the user attribute and the calendar attribute. Build a traffic analysis model.

  In this embodiment, the category classification result of each item (average, standard deviation, multi-dimensional vector) of traffic characteristics is used as an objective variable, and user attributes (contract speed, contract area, contract service type, etc.) and calendar attributes of existing users are set. As an explanatory variable, consider the construction of a model that classifies the traffic characteristics of each user into one of the categories for each of the three items.

  In the present embodiment, the teacher data registered in the traffic analysis teacher data table 104a is used as an appropriate analysis method such as Neural network, Naive Bayes, Decision Tree, Support Vector Machine, and the like from user attributes and calendar attributes. Build a traffic analysis model to analyze traffic characteristics.

  When applying to the above-described analysis methods, the average value or standard deviation of the traffic that is quantitatively obtained may be the average value or standard deviation value itself instead of the category classification result.

  Here, if the Neural Network is applied to construct an analysis model, Network parameters (coupling weights) are optimized so as to minimize the error between the teacher data and the output. Similarly, when other analysis methods are applied, the parameters are optimized so that the error between the teacher data and the output is minimized.

  The load determination teacher data generation unit 105 determines a low load determination for the correspondence between user attributes, calendar attributes, and load characteristics (low load or normal load) for each unit period in which the traffic volume is classified as low load or normal load. Is registered in the teacher data table 105a as teacher data for use.

  FIG. 7 is a diagram schematically showing an example of the teacher data table 105a, in which correspondence relationships between user attributes, calendar attributes, and load characteristics (low load or normal load) are registered.

  The low load discrimination model construction unit 107 uses the teacher data registered in the low load discrimination teacher data table 105a for an appropriate analysis method such as Neural network, Naive Bayes, Decision Tree, Support Vector Machine, Based on the user attribute and calendar attribute of an arbitrary user, a low load determination model is determined for determining whether or not the unit period characterized by the calendar attribute of the user has a low load.

  Returning to FIG. 2, in the traffic prediction unit 2, the low load port prediction unit 201 applies the user attribute and calendar attribute of the new user to the low load determination model constructed by the low load determination model construction unit 107, It is determined whether or not the traffic of the unit period specified by the calendar attribute of the new user is light. The discrimination result is output after being temporarily stored in the storage unit 203.

  The traffic characteristic prediction unit 202 applies a new user to the traffic analysis model constructed by the traffic analysis model construction unit 106 for only a normal load unit period determined by the low load port prediction unit 201 as other than a low load. Are applied to predict the traffic characteristics of the unit period specified by the calendar attribute of the new user. The prediction result is output after being temporarily stored in the storage unit 203.

  Next, the operation of the traffic analysis model construction unit 1 will be described in detail with reference to the flowchart of FIG.

  In step S <b> 1, one of the existing users is selected as the current user of attention from the existing user DB 101, and the user attribute of the target user and the actual traffic thereof are taken into the load analysis unit 102. In step S2, the actual traffic is extracted by the load analysis unit 102 for each unit period specified by the calendar attribute. In the present embodiment, since the actual traffic is analyzed using the day and day of the week as a calendar attribute, the actual traffic is extracted every 24 hours.

  In step S3, the extracted actual traffic for 24 hours is analyzed, and an average value of the traffic volume is obtained. In step S4, the average value of the traffic volume is compared with a predetermined reference value, and based on the comparison result, it is determined whether or not the traffic of the current unit period is lightly loaded.

  In this embodiment, if the logic determination is set to “average traffic volume is 1 Mbps or less” and the average traffic volume for 24 hours is 1 Mbps or less, the current unit period is classified as low load, and the process proceeds to step S5. . In step S5, the load determination teacher data generation unit 105 stores the user attribute, calendar attribute, and load characteristic (low load) in the load determination teacher data table 105a as low load determination teacher data for the current unit period. be registered. As the calendar attribute, if the current unit period is [Monday of 1st], “1 day” as the whole day and “Monday” as the day of the week are registered as the calendar attributes.

  In step S11, it is determined whether or not the analysis of all unit periods has been completed for the current user of interest, and if not completed yet, the process returns to step S2, and the actual traffic for 24 hours of the next day is set as the next unit period. Are extracted and the analysis is performed in the same manner as described above.

  If it is not determined as “low load” in step S4 for the current unit period, the process proceeds to step S6, and the load analysis unit 102 determines whether the current unit period is a “temporary load” in which the traffic is bursty. It is determined whether or not. If it is determined as “temporary load”, the process proceeds to step S10, and information on the current unit period is discarded.

  On the other hand, if it is not “temporary load”, it is classified as “normal load” and the process proceeds to step S7, and the user attribute, calendar attribute, and load characteristic (normal load) for the current unit period are determined for load determination. It is registered in the load determination teacher data table 105a as teacher data. In other words, in the present embodiment, both the correspondence between unit periods with a low load characteristic and the correspondence with unit periods with a normal load characteristic are registered as teacher data for load determination.

  In step S8, the traffic characteristic analysis unit 103 analyzes the traffic characteristic of the current unit period, and the category classification unit 103a categorizes the traffic “average value”, “standard deviation”, and “multidimensional vector”. Is done.

  In step S9, the traffic analysis teacher data generation unit 104 registers the user attribute, calendar attribute, and traffic characteristic category classification result in the traffic analysis teacher data table 104a for the current unit period. That is, in the present embodiment, only the correspondence between unit periods whose load characteristics are normal loads is registered in the traffic analysis teacher data table 104a as the teacher data for traffic analysis.

  As described above, the actual traffic analysis of the current user of interest proceeds, and if it is determined in step S11 that the analysis and teacher data registration have been completed for all unit periods, the process proceeds to step S12. In step S12, it is determined whether or not the actual traffic of the existing user remains. If the actual traffic remains, the process returns to step S1, the target user is switched to the next existing user, and the above-described processes are repeated.

  When all of the existing users or the planned number of existing users have been analyzed for their actual traffic and registered for teacher data, the process proceeds to step S13. In step S13, in the traffic analysis model construction unit 106, the teacher data registered in the traffic analysis teacher data table 104a is applied to an algorithm such as Neural network, and the error between the teacher data and the algorithm output is minimized. Thus, a traffic analysis model is constructed by optimizing various parameters.

  In step S14, in the low load discrimination model construction unit 107, the teacher data registered in the load discrimination teacher data table 105a is applied to an algorithm such as Neural network, and the error between the teacher data and the algorithm output is minimized. A load discrimination model is constructed by optimizing various parameters so that

  FIG. 9 is a flowchart showing a prediction operation by the traffic prediction unit 2, in which the low load analysis model is registered in the low load port prediction unit 201, and the traffic analysis is performed in the traffic characteristic prediction unit 202. The model is registered. Here, a description will be given focusing on the operation of the traffic characteristic prediction unit 202.

  In step S21, contract conditions (contract speed and contract service type), gender, age, and the like are taken in as user attributes of the new user, and further, in this embodiment, as calendar attributes that specify the prediction period of traffic characteristics, in this embodiment. The day and day of the week are captured. That is, if it is desired to predict the traffic characteristics of “Monday of 3rd”, “3rd day” is designated as the day of the month and “Monday” is designated as the day of the week. Similarly, if the traffic characteristics of “Sunday on the 20th” are to be predicted, “20th day” is designated as the day of the month and “Sunday” is designated as the day of the week.

  The calendar attribute that can be specified here is not limited to the combination of the calendar attributes registered as the teacher data (that is, the correspondence relationship of the existing user). Even if the correspondence relationship relating to the combination of “20th day” and “Sunday” of the day of the week is unregistered, the traffic characteristics of “Sunday on 20th” can be predicted based on another correspondence relationship.

  Similarly, the user attribute of the new user need not match the combination of user attributes registered as teacher data. For example, the user attribute of the new user is “contract speed is 10 Mbps”, “Service type is A”, “contract area is 2”, etc., and even if such correspondence is not registered as teacher data, traffic characteristics can be predicted based on other correspondence.

  Furthermore, even if a combination of each user attribute and each calendar attribute of a new user is not registered as teacher data, traffic characteristics can be predicted based on other combinations.

  In step S22, the user attribute and calendar attribute are applied to the traffic characteristic prediction model, and the average value, standard deviation, and multidimensional vector of the traffic for the unit period specified by the calendar attribute are used as the traffic characteristic of the new user for the period. is expected. In step S23, the prediction result is output.

  Since the average value, the standard deviation, and the prediction result of the multidimensional vector depend on the traffic characteristic classification method used for the construction of the analysis model, the average value is “small”, “medium”, “ If the standard deviation is categorized as “small”, “medium”, “large”, and the multidimensional vector is categorized as “day” or “night”, the new user ’s In the traffic characteristics, the average value of the unit period (24 hours in this embodiment) belongs to any category of “small”, “medium”, and “large”, and the standard deviation is “small”, “medium”, “large” ”To which category the multi-dimensional vector belongs to“ day type ”or“ night type ”is given as a prediction result.

  The output form of the prediction result may be provided as text for each item of the traffic characteristics, or as a model-shaped pattern reflecting the category of each item of the traffic characteristics. You may make it.

  In the above-described embodiment, the traffic characteristics of the normal load have been described as being categorized by paying attention to the three items of the average value, the standard deviation, and the multidimensional vector. However, the present invention is not limited to this. Instead, it may be categorized by paying attention to appropriate two to three items including other items such as maximum frequency, maximum value, minimum value, and the like. Alternatively, the average value, standard deviation, maximum value, minimum value, and the like that easily quantify the traffic characteristics may be obtained without categorization.

  Further, in the above-described embodiment, it has been described that “calendar day of the month” and “day of the week” are designated as calendar attributes. However, the present invention is not limited to this, and the distinction between holidays and weekdays is made. It may be specified or “all week of the month (the week number in the month)” may be specified.

  At this time, if the new user's traffic is analyzed separately for holidays and weekdays, the actual traffic is analyzed for each day in the same manner as described above, and the “calendar attribute” of the teacher data is set to the holidays / weekdays. Another is registered. If the traffic of a new user is analyzed by “all week”, the performance of the actual traffic is analyzed every week, and “all week” is registered in the “calendar attribute” of the teacher data.

It is a figure of the network to which the prediction result of the traffic characteristic using the traffic analysis model constructed | assembled with the traffic analysis model construction | assembly apparatus of this invention is applied. It is the block diagram which showed the structure of the traffic analysis system to which this invention is applied. It is the figure which showed the structure of the existing user database typically. It is the figure which showed an example of the analysis result of the traffic characteristic. It is the figure which showed another example of the analysis result of the traffic characteristic. It is the figure which showed an example of the traffic analysis teacher data table. It is the figure which showed an example of the load discrimination teacher data table. It is the flowchart which showed operation | movement of the traffic analysis model construction part. It is the flowchart which showed operation | movement of the traffic estimation part. It is the figure which showed the relationship between the ratio of a low load / non-low load user, and contract speed. It is the figure which showed the relationship between the ratio of a low load / non-low load user, and a contract area. It is the figure which showed the relationship between the ratio of a low load / non-low load user, and contract service classification. It is the figure which showed the relationship between a contract service classification and the multidimensional vector of a traffic characteristic. It is the figure which showed the relationship between the day of the week of a calendar characteristic and the multidimensional vector of a traffic characteristic.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Traffic analysis model construction part, 2 ... Traffic prediction part, 101 ... Existing user DB, 101a ... User attribute storage part, 101b ... Performance traffic storage part, 102 ... Load analysis part, 103 ... Traffic characteristic analysis part, 103a ... Category Classifying unit 104 ... Traffic analysis teacher data generation unit 104a ... Teacher data table 105 ... Load discrimination teacher data generation unit 105a ... Teacher data table 106 ... Traffic analysis model construction unit 107 ... Low load discrimination model construction unit

Claims (15)

In a traffic analysis model construction apparatus that analyzes traffic characteristics of a new user based on attribute information and actual traffic of existing users already accommodated in the network,
Means for storing the actual traffic of existing users;
Means for storing user attributes of existing users;
Traffic analysis means for analyzing a plurality of existing traffic characteristics for each unit period specified by a predetermined calendar attribute from the actual traffic of existing users;
Teacher data generating means for collecting correspondences of user attributes, calendar attributes and existing traffic characteristics of existing users in each unit period to generate teacher data for traffic analysis,
And a traffic analysis model construction means for constructing a traffic analysis model for analyzing the traffic characteristics of the new user for each unit period based on the user attributes and calendar attributes of the new user based on the teacher data for the traffic analysis. A device for building a traffic analysis model characterized by The traffic analysis means includes categorization means for classifying each existing traffic characteristic into a plurality of categories,
The apparatus for constructing a traffic analysis model according to claim 1, wherein the teacher data generation means collects the category classification results as existing traffic characteristics. Load analysis means for identifying load characteristics of the unit period,
The apparatus for constructing a traffic analysis model according to claim 1, wherein the teacher data generation unit does not use a correspondence relationship between unit periods with a specific traffic load as teacher data.   4. The traffic analysis model construction apparatus according to claim 3, wherein the load analysis unit identifies a low-load unit period in which traffic usage is low. 4. The traffic analysis model construction apparatus according to claim 3, wherein the load analysis unit identifies a unit period in which traffic usage is bursty. The traffic analysis unit, the average value of the existing traffic, standard deviation, and any of claims 1 to 5, characterized in that for obtaining a plurality of existing traffic characteristics including at least one multi-dimensional vector representing the characteristics of the traffic change An apparatus for constructing a traffic analysis model according to claim 1. Second teacher data generation means for collecting the correspondence between user attributes, calendar attributes and load characteristics of each existing user and generating teacher data for load determination;
The load characteristic discrimination model construction means for constructing a load characteristic discrimination model for discriminating a load characteristic for each unit period based on a user attribute and a calendar attribute on the basis of the teacher data for the load discrimination. Item 7. An apparatus for constructing a traffic analysis model according to any one of Items 3 to 6. In the construction method of the traffic analysis model for analyzing the traffic characteristics of the new user based on the attribute information and the attribute information of the existing user already accommodated in the network, based on the attribute information,
A procedure for analyzing a plurality of existing traffic characteristics for each unit period specified by a predetermined calendar attribute from the actual traffic of existing users,
A procedure for collecting correspondence between user attributes, calendar attributes and existing traffic characteristics of existing users in each unit period to generate teacher data for traffic analysis;
And a procedure for constructing a traffic analysis model for analyzing a traffic characteristic of a new user for each unit period based on a user attribute and a calendar attribute of the new user based on the teacher data for the traffic analysis. How to build a traffic analysis model. Procedure for analyzing the existing traffic characteristic comprises the steps of classifying the existing traffic characteristics into a plurality of categories,
The traffic analysis model construction method according to claim 8, wherein the category classification results are collected as the existing traffic characteristics. Including a procedure for identifying a load characteristic of the unit period;
10. The method for constructing a traffic analysis model according to claim 8 or 9, wherein a correspondence relationship between unit periods having a specific traffic load is not used as teacher data.   11. The method for constructing a traffic analysis model according to claim 10, wherein a correspondence relationship between unit periods of low load with little traffic use is not used.   11. The method for constructing a traffic analysis model according to claim 10, wherein the traffic use does not use a correspondence relationship of bursty unit periods. Procedure for analyzing the existing traffic characteristics, the average value of the traffic, the standard deviation, and to claims 8 and obtains a plurality of existing traffic characteristics including at least one multi-dimensional vector representing the characteristics of the traffic change 13. A method for constructing a traffic analysis model according to any one of 12 above.   A traffic analysis model construction program for causing a computer to execute the traffic analysis model construction method according to any one of claims 8 to 13.   15. A storage medium storing the traffic analysis model construction program according to claim 14 in a computer-readable manner.

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