Patent Publication Number: US-2015085651-A1

Title: Analysis server and mobile network system

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority from Japanese Patent Application No. 2013-199143, filed Sep. 26, 2013, which is incorporated herein by reference in its entirety. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to an analysis server and a mobile network system, and particularly to a technique to control a bandwidth of a network based on a congestion status of a base station. 
     2. Description of Related Art 
     A mobile operator to manage a base station or the like of a mobile network system struggles to process traffic which increases with the rapid increase of smartphones. In general, as the traffic increases, the mobile operator increases capacity of equipment. However, under circumstances in which profit per user does not increase, the increase of the capacity of the equipment is not appropriate in view of cost-effectiveness. Then, the mobile operator not only increases the capacity of the equipment, but also considers that effective use of existing equipment is important and configures a system in which the traffic amount to be processed in the existing equipment, particularly in a base station is improved, and quality of experience per end user, for example, user throughput is improved. The mobile operator visualizes the congestion state of the base station by using this system, and when the base station is in the congestion state, the mobile operators controls the traffic of a particular user occupying the bandwidth, for example, a user downloading moving picture data, or controls a specific application such as a moving picture service. 
     In the foregoing system, when the congestion state of the base station is determined, the maximum throughput in design, which is determined by the specifications of the base station, is made a theoretical throughput, a threshold is determined based on the value, and when exceeding the threshold, it is determined that the base station is in the congestion state. In a general method, the traffic amount is controlled to a user in the base station which is determined to be in the congestion state or to an application. 
     As another prior art technique, JP-A-2007-43311 discloses a method of performing a congestion state control by regulation in a mobile network system, and the focus is made on the control to be performed after the congestion state is determined. Besides, JP-A-2012-231335 discloses a congestion state control performed in advance for a case where a congestion state occurs, for example, for New Year or an event such as a concert. Neither of the prior arts disclose a way of determining the congestion. 
     However, there are various types of base stations, and according to the installation environment, density of population, influence of adjacent building, communication hours, and the like, the communication can not be necessarily performed with the maximum throughput in design, that is, the theoretical traffic amount. In general, the amount of traffic transmitted and received by the base station is lower than the theoretical traffic amount. Thus, in the system in which the congestion state is determined based on the theoretical traffic amount, there may be a user performing communication in the congestion state since the state is not determined to be congested although the state is actually congested. As a result, quality of experience of end user is reduced. 
     SUMMARY OF INVENTION 
     In order to solve the problem, according to an aspect of the invention, an analysis server in a mobile network system including a base station includes a calculation part which calculates statistics indicating a processing capacity of the base station based on a packet transmitted and received by the base station and calculates an effective statistics indicating an effective processing capacity of the base station based on the statistics, and a policy generation part which generates, based on the effective statistics, a policy for controlling traffic of the packet transmitted and received by the base station. 
     According to the aspect of the invention, congestion determination of a base station can be performed with high accuracy. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a figure showing a structural example of a mobile network system. 
         FIG. 2  is a functional block diagram of a DPI equipment. 
         FIG. 3  is a figure showing an example of a call processing signal packet. 
         FIG. 4  is a figure showing an example of a user data packet. 
         FIG. 5  is a figure showing an example of a packet for transmitting a terminal communication log. 
         FIG. 6  is a figure showing an example of a DPI log generated by the DPI equipment. 
         FIG. 7  is a figure showing an example of a communication terminal log notified by a communication terminal. 
         FIG. 8  is a functional block diagram of a communication log server. 
         FIG. 9  is a functional block diagram of an analysis server. 
         FIG. 10  shows an example of user data summarizing tables generated from the DPI logs of users in the same base station and from communication terminal logs. 
         FIG. 11  shows an example of a base station summarization table generated from the user data summarization tables in the same base station. 
         FIG. 12  shows an example of user data summarization tables generated from DPI logs of users moving between different base stations and from communication terminal logs. 
         FIG. 13  shows a first example of a base station summarization table generated from the DPI logs of the users moving between the different base stations and from the communication terminal logs. 
         FIG. 14  shows a second example of the base station summarization table generated from the DPI logs of the users moving between the different base stations and from the communication terminal logs. 
         FIG. 15  shows a third example of a base station summarizing table generated from the DPI logs of the users moving between the different base stations and from the communication terminal logs. 
         FIG. 16  is a flowchart showing a process of an analysis server. 
         FIG. 17  is a figure showing an example in which traffic is controlled. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
       FIG. 1  is a figure showing a structural example of a mobile network system of an embodiment. A wireless terminal  101  is served by a base station  102 . When starting communication, the wireless terminal  101  transmits a message signal for session connection to the base station  102 . The base station  102  receiving the message signal for session connect from the wireless terminal  101  transmits the message signal to a call processing control equipment  103 . The call processing control equipment  103  receiving the signal connects a session for transmitting and receiving user data. After the session for enabling the wireless terminal  101  to transmit and receive data for serving is connected, the wireless terminal  101  transmits user data to an application server  105  in order to be provided with the service. The user data transmitted from the wireless terminal  101  is transmitted to user data control equipment  104  via the base station  102 . The user data control equipment  104  receiving the user data from the base station  102  converts a header format of the user data, and transmits to the application server  105  via the Internet. 
     As described above, the data transmitted from the wireless terminal  101  and data transmitted to the wireless terminal  101  pass through an interface between the base station  102  and the call processing control equipment  103  or an interface between the base station  102  and the user data control equipment  104 . Thus, if all packets passing between the base station  102  and the call processing control equipment  103  and between the base station  102  and the user data control equipment  104  are captured, it is possible to visualize that “when” and “where” the wireless terminal  101  “uses what application” and “how is the feeling”. Then, tapping equipment  106  is installed between the base station  102  and the call processing control equipment  103 , and tapping equipment  107  is installed between the base station  102  and the user data control equipment  104 , and all packets passing through the routes are copied. 
     In order to determine the congestion in the system shown in  FIG. 1 , the tapping equipment  106  copies the packet flowing between the base station  102  and the call processing control equipment  103 , and the tapping equipment  107  copies the packet flowing between the base station  102  and the user data control equipment  104 , and the packets are transferred to a Deep Packet Inspection (DPI) equipment  108 . The packet transferred from the tapping equipment  106  to the DPI equipment  108  includes the information necessary for the wireless terminal  101  to connect the session. Besides, the packet transferred from the tapping equipment  107  to the DPI equipment  108  includes the information relating to the user data between the wireless terminal  101  and the application server  105 . The DPI equipment  108  extracts a terminal ID, a terminal machine type ID, a base station ID and the like from the packet transferred from the tapping equipment  106 , and extracts an application in use, GPS information such as latitude and longitude necessary for the application, and the like from the packet transferred from the tapping equipment  107 . The DPI equipment  108  combines the packets transferred from the tapping equipment  106  and the tapping equipment  107 , specifies the application or the service used by the wireless terminal  101 , and generates a DPI log as basic data of quality of experience. The generated DPI log is stored in a communication log DB  1091  in a communication log server  109 . 
     On the other hand, the DPI log is data generated from the packet flowing through the mobile network. Thus, the GPS information such as latitude and longitude and the terminal machine type ID are not necessarily included. Then, in order to raise the precision of the system, the wireless terminal  101  is enabled to periodically transmit a terminal communication log between the wireless terminal  101  and the base station  102  to the communication log server  109 . An application for the wireless terminal  101  to transmit the log is previously implemented, and the information collected by the application when the wireless terminal  101  communicates, for example, the GPS information such as latitude and longitude, terminal ID, terminal machine type ID, application in use, ID of the base station  102  in communication are transmitted to the communication log server  109 . The communication log server  109  receiving the log information stores the log in the communication log DB  1091 . 
     An analysis server  110  gets the DPI log and the terminal communication log stored in the communication log DB  1091 , and summarizes statistics for each user and each base station. The analysis server  110  calculates an effective traffic amount based on the statistics summarized for each user and each base station. Further, a congestion evaluation is made based on the effective traffic amount. The result of the congestion evaluation is transferred to all of or part of the base station  102 , the call processing control equipment  103 , the user data control equipment  104  and a traffic control equipment  111  in  FIG. 1 , and control is performed by all of or part of the base station  102 , the call processing control equipment  103 , the user data control equipment  104  and the traffic control equipment  111  in  FIG. 1 . 
       FIG. 2  is a functional block diagram of the DPI equipment  108 . The DPI equipment  108  receives the copied and transferred packets from the tapping equipment  106  and the tapping equipment  107 , and a packet analysis part  201  analyzes whether the received packet is the packet transferred from the tapping equipment  106  or the packet transferred from the tapping equipment  107 . The packet analysis part  201  can also determine whether the packet is transferred from the tapping equipment  106  or the tapping equipment  107  by dividing an input port of the DPI equipment  108 . After the packets are classified by the packet analysis part  201 , a user ID extraction part  2011  extracts the terminal ID (user identifier) and the like included in the packet transferred from the tapping equipment  106 , and a user data extraction part  2012  extracts the user data and the like included in the packet transferred from the tapping equipment  107 . 
       FIG. 3  is a figure showing an example of a packet format of the call processing signal packet transferred from the tapping equipment  106 . As shown in  FIG. 3 , the call processing signal packet includes a header  301 , a base station ID  302 , a terminal ID  303 , a machine type ID  304  indicating the machine type of the communication terminal, a common identifier  305  for linking the call processing signal packet and the user data packet, and other call processing information  306 . 
       FIG. 4  is a figure showing an example of a packet format of the user data packet transferred from the tapping equipment  107 . As shown in  FIG. 4 , the user data packet includes a header  401 , a base station ID  402 , a common identifier  403  for linking with the call processing signal packet, an information  404  relating to an application, and other user data  406 . According to the application, GPS information  405  such as latitude and longitude is included in the application field  404 . In order to connect these data, a common identifier, such as an IP address of the base station  102  included in any packet or a tunnel ID, is used. The identifier varies according to the communication system. Since the same value is included in the common identifier  305  shown in  FIG. 3  and the common identifier  403  shown in  FIG. 4 , the common identifiers are used as keys, and the call processing signal packet and the user data packet are linked. 
     The DPI equipment  108  adds times when the DPI equipment  108  detects the respective packets to the information included in the call processing signal packet and the user data packet linked by using the common identifier, so that the respective information of time, latitude, longitude, terminal ID, machine type ID, use application and base station ID shown in  FIG. 6  can be extracted as the DPI log. Besides, statistics such as a throughput of the user or a response time shown in  FIG. 6  can be calculated from the length of packets received per unit time by the DPI equipment  108  and the sequence number specified by the protocol such as HTTP. Incidentally, the statistics may be any information as long as the processing capacity of the base station  102  is indicated, and may be information other than the throughput or the response time. 
     The DPI equipment  108  uses the common identifier, and a data connecting part  202  connects the terminal ID (user identifier) and the like with the user data and the like, so that it becomes possible to visualize that “when” the terminal “uses what application” and “how is the feeling”. The quality of experience of user (how the user feels) can be visualized by replacing the throughput of the user or the response speed of the application by response time. 
     Although the place is not necessarily notified according to the use application, if the application notifying the GPS information is used, the position information such as latitude and longitude can be captured from the packet. The DPI equipment  108  uses the data connected by the data connecting part  202 , and a statistic data generation part  203  generates the statistics such as user throughput in the application actually used by the terminal or the response time to the application server  105 . A DPI log  81  generated by the DPI equipment  108  is transferred from an output part  204  to the communication log server  109 . 
     As described above, the position information such as latitude and longitude is not necessarily included in the DPI log  81  generated by the DPI equipment  108 . Then, the DPI log  81  can be complemented by implementing an application into the wireless terminal  101 , which transfers, as a terminal communication log  82 , a log in communicated between the wireless terminal  101  and the base station  102  to the communication log server  109  when the wireless terminal  101  communicates. The transfer of the terminal communication log  82  is enabled by implementing the specific application into the wireless terminal  101  in advance. 
       FIG. 5  is a figure showing an example of a packet transmitted from the application which is implemented in the wireless terminal  101  in order to generate the communication terminal log  82 . The packet for generating the communication terminal log  82  includes a header  501 , a time  502  when the packet is transmitted, a base station ID  503 , a terminal ID  504 , a terminal GPS information  505 , and an application  506  which is not the application used for generating the communication terminal log but is the application used in the service received by the user at the time. The communication log server  109  receiving the packet generates the communication terminal log  82  shown in  FIG. 7  from the packet. 
       FIG. 8  is a block diagram showing a structure of the communication log server  109 . The communication log server  109  includes the communication log DB  1091 , and stores the DPI log  81  and the terminal communication log  82 . 
       FIG. 6  is a figure showing an example of the DPI log  81  stored in the communication log server  109 . The example of the DPI log  81  shown in  FIG. 6  includes time, terminal ID (user identifier), machine type ID, use application, base station ID, and statistics (throughput in this example). GPS information such as latitude and longitude is collected and stored by the application if possible. In the table of  FIGS. 6 ,  601  and  604 ,  602  and  605 , and  603  and  606  respectively form pairs. For convenience, the two tables are shown. The time, the terminal ID (user identifier), the machine type ID, the use application, the base station ID, and the statistics are stored at the rows  601  and  604 . Since the latitude and longitude information can not be collected from the packet, it is treated as missing data. The same user generates the log at the rows  603  and  606 , and at this time, since the GPS information can be collected from the packet, the GPS information is shown in the table. 
       FIG. 7  is a figure showing an example of the terminal communication log  82  stored in the communication log server  109 . The example of the terminal communication log shown in  FIG. 7  includes time, latitude, longitude, terminal ID, machine type ID, use application, and base station ID. The log is the log collected from the wireless terminal  101  by using the specific application, and communication data of the wireless terminal  101  is periodically transmitted to the communication log server  109  at the time of use of the application. In the table of  FIGS. 7 ,  701  and  704 ,  702  and  705 , and  703  and  706  respectively form pairs. For convenience, the two tables are shown. 
       FIG. 9  is a block diagram showing a structure of the analysis server  110 . The analysis server  110  includes a log connection part  901 , a user data summarization part  902 , abase station data summarization part  903 , an effective traffic amount calculation part  904 , a control policy generation part  905  and a control part  906 . The analysis server periodically collects the DPI log  81  and the terminal communication log  82  stored in the communication log server  109  and calculates the effective traffic amount. 
     Incidentally, any of the DPI equipment  108 , the communication log server  109  and the analysis server  110  described in  FIG. 2 ,  FIG. 8  and  FIG. 9  are realized by general server equipments, and include, although not shown, a CPU, a memory, a hard disk, and a communication interface for communicating with another equipment. The respective function parts such as the packet analysis part  201  and the log connection part  901  are realized by, for example, the CPU which executes a program stored in the memory. Besides, the communication log DB  1091  storing the DPI log  81  and the terminal communication log  82  is realized by, for example, the hard disk. 
     The analysis server  110  connects the collected DPI log  81  and the terminal communication log  82  by the log connection part  901 , and the logs are summarized by the user data summarization part  902  for the same terminal ID (user identifier) and are summarized by the base station data summarization part  903  for the same base station. When the DPI log  81  and the communication terminal log are connected by the user data summarization part  902  and the base station data summarization part  903 , the logs are connected based on data as a common item in any data such as the terminal ID, the machine type ID and the base station ID in communication. In addition, information existing in only one of the logs, for example, the statistics and the position information are added. Based on the summarized data, the effective traffic amount calculation part  904  calculates the effective statistics when the user communicates or communicates via the base station. Based on the effective traffic amount calculated by the effective traffic amount calculation part  904 , the control policy generation part  905  generates a control policy for the traffic control equipment  111  and the like to actually control. The policy generated by the control policy generation part  905  is notified to the traffic control equipment  111  and the like via the control part  906 . 
       FIG. 10  is a view showing examples of user data summarization tables  1001 ,  1002  and  1003  generated by the user data summarization part  902  from the DPI log  81  and the terminal communication log  82 . Time, use application, statistics (user throughput in this example), and information of base station which wireless terminals connect are stored in respective columns of the user data summarization tables  1001 ,  1002  and  1003 . Summarization results in a specified unit time (one second in this example) are stored in respective rows. The information is summarized for each of the user A1001, the user B1002 and the user C1003. The user here corresponds to the terminal ID of the DPI log  81  or the communication terminal log  82 , and the respective data are summarized for each terminal ID. 
       FIG. 11  is a figure showing an example of a base station data summarization table  1101  generated such that the base station data summarization part  903  summarized data for the same base station based on the user data summarization table  1001 . The respective items enumerated in the base station data summarization table  1101  shown in  FIG. 11  are merely examples. The items shown in respective columns of the base station data summarization table  1101  include statistics of each of the user A, the user B, and the user C, in this example, user throughputs and the total user throughputs of the three users of the user A, the user B and the user C are listed. The summarization result in a specified unit time (one second in this example) is stored in each row. 
     Subsequently, the effective traffic amount calculation part  904  obtains a time average user throughput  11011  in the base station and a standard deviation  11012  of the user throughput based on the data of the base station data summarization table  1101  shown in  FIG. 11 . Besides, an effective user throughput  11013  is obtained by using the time average user throughput  11011  and the user throughput standard deviation  11012 . In the example shown in  FIG. 11 , the effective user throughput  11013  is a value obtained by adding a value three times larger than the user throughput standard deviation to the time average user throughput  11011 . The effective user throughput  11013  can also be calculated by using another function by a method other than the above. 
     Here, the effective user throughput  11013  means a maximum throughput at which the user communicating through the base station can communicate. In general, connection speed is lower than the theoretical specification of the base station because of the deployment environment of the base station, time zone, weather and the like. The effective user throughput is obtained after considering the condition and is the maximum throughput at which the user can communicate. The effective statistics such as the effective user throughput is a value lower than the design specifications, and the value means a realistic value which can be actually felt by the user. In the effective user throughput used in this example, the throughput felt by the user is obtained based on the packet flowing through the actual network and the log from the user terminal, and the throughput in view of variation of the throughput (value three times larger than the standard deviation is added) is defined as the effective throughput. 
       FIG. 12  is a figure showing another example of the user data summarization table.  FIG. 12  assumes a case in which the user communicates with different base stations in respective times or a case including a time when the user does not communicate. A user D and a user E of  FIG. 12  are examples of users moving between two base stations, and a user F is an example including a communication time in addition to the case of moving between base stations. In the user D, a base station which user D connects is changed from β to δ at time 00:00:05 in the user data summarization table  1201 . In the user E, a base station which user E connects is changed from β to γ at time 00:00:05 in the user data summarization table  1202 . In the user F, who does not communicate until time 00:00:02 in the user data summarization table  1203 , communication starts at a base station γ from time 00:00:03, and the base station is changed from γ to δ at time 00:00:05. 
     Base station summarization tables for three users, in which summarization is performed for each base station, are denoted by  1301  of  FIG. 13 ,  1401  of  FIGS. 14 and 1501  of  FIG. 15 . Similarly to  FIG. 11 , data of the users in the base station β are summarized in  FIG. 13 , data of the users in the base station γ are summarized in  FIG. 14 , and data of the users in the base station δ are summarized in  FIG. 15 . 
     When the time average user throughput is obtained in each of the base stations, the division is made using a time when the users actually communicate. That is, in the base station β, a total user throughput  13011  at time 00:00:01, a total user throughput  13012  at time 00:00:02, a total user throughput  13013  at time 00:00:03, and a total user throughput  13014  at time 00:00:04 are added to each other and are divided by 4 of the communication time. That is, (11+8+10+7)/4=9 is obtained, and 9 Mbps is a time average user throughput  13015 . Similarly, variance is calculated from the second central moment, and a user throughput standard deviation  13016  is obtained as the square root of the variance. Similarly to  FIG. 11 , an effective user throughput  13017  is calculated by adding a value three times larger than the user throughput standard deviation  13016  to the time average user throughput  13015 . 
     Similarly, with respect to the base station γ, a time average user throughput  14011 , a user throughput standard deviation  14012  and an effective user throughput  14013  are calculated in the base station summarization table  1401  of  FIG. 14 . With respect to the base station δ, a time average user throughput  15011 , a user throughput standard deviation  15012  and an effective user throughput  15013  are calculated in the base station summarization table  1501  of  FIG. 15 . 
       FIG. 16  is a flowchart showing a series of processes in which the analysis server calculates an effective user throughput for each base station, and generates a control policy.  FIG. 16  is also the flowchart in which the series of processes explained in  FIG. 10  to  FIG. 15  are generalized. 
     In the data summarization, reference time T1 and T2 are determined in order to determine a measurement time, and the number of base stations in which summarization is performed during the time is counted (step  1601 ). Next, the measurement time is divided into n parts, and the unit time Δt is calculated (step  1602 ). A procedure from step  1603  to step  1609  is a loop for obtaining the effective user statistics in each base station. In the loop, first, statistic data St for respective users are summarized in each Δt (step  1604 ). This step corresponds to, for example, the process of summarizing the throughputs of the users A, B and C and the total user throughputs at time 00:00:01 to 00:00:06 in  FIG. 11 . Next, an average E (St) of the statistic data and a standard deviation σ(St) for the respective users between the measurement time T1 and T2 are calculated (step  1605 ). This step corresponds to, for example, the process of calculating the time average user throughput  11011  and the user throughput standard deviation  11012  in  FIG. 11 . The effective user statistics is calculated based on E(St) and σ(St) (step  1606 ). This step corresponds to, for example, the process of calculating the effective user throughput  11013  in  FIG. 11 . Here, in this embodiment, the effective user statistics is calculated by E (St)+3σ(St). However, the effective user statistics calculated at step  1606  is an example, and can also be treated as a general function. If the effective user statistics calculated at step  1606  is larger than the theoretical statistics, the effective user statistics is replaced by the theoretical user statistics (step  1607 , step  1608 ). The theoretical user statistics are a theoretically achievable numerical value in design, which is determined according to the equipment, for example, the specifications of the base station and is, for example, a throughput or a response time. This process is performed for all base stations (step  1609 ). 
     In this embodiment, although the user throughput is used as an example of the statistics, a statistics other than the user throughput, for example, a response time between the communication terminal  101  and the application server  105  or a communication time can also be selected as the statistics. When the calculation is ended for all the base stations, a control policy used for control by the call processing control equipment  103 , the user data control equipment  104  and the traffic control equipment  111  is generated based on the effective user statistics for the respective base stations ( 1610 ). When the control policy is notified from the analysis server  110 , the call processing control equipment  103 , the user data control equipment  104  and the traffic control equipment  111  control the traffic for the base station based on the control policy, or the base station itself controls the traffic from the wireless terminal  101 . 
       FIG. 17  is a view showing an example in which the traffic is controlled by the call processing control equipment  103 , the user data control equipment  104  and the traffic control equipment  111 . In  FIG. 17 , based on an actually measured throughput  1701 , an average throughput  1703  in the time and an effective throughput  1704  are calculated. Besides, a theoretical throughput  1702  is a known value and is a higher value than the effective throughput  1704  in this example. At this time, it is assumed that the maximum throughput achieved in the base station is the effective throughput  1704 , and the congestion degree of the base station is determined based on the effective throughput  1704 . 
     It is conceivable that for example, a value of 90% of the effective throughput is determined to be a regulation throughput. Alternatively, since the effective throughput  1704  is the throughput obtained by adding a value three times larger than the standard deviation to the average throughput  1703 , it is conceivable that the throughput obtained by adding a value two times larger than the standard deviation to the average throughput  1703  is determined to be the regulation throughput  1705 . When the throughput exceeds the regulation throughput, the control policy is notified to the base station  102  or the adjacent traffic control equipment  111  and regulation is applied. 
     For example, many users exist in the base station  102 , and there is a case where apart of user communicates of a large amount of data. If the user can be identified, the control policy of allocating narrower bandwidth of only the user can be notified to the base station  102 . When all users uniformly communicate, the control policy of allocating to uniformly regulate transmission can also be notified to the base station  102 . 
     Besides, when the throughout exceeds the regulation throughput, the control policy of instructing to prevent the traffic larger than the regulation throughput from being transmitted to the base station  102  can also be notified to the traffic control equipment  111 . 
     As described above, according to the embodiment, the congestion determination of the base station is not performed based on the theoretical traffic amount, but the accurate congestion determination closer to the user experiencing can be communicated based on the effective traffic amount which can be transmitted and received by the base station under the environment in which the base station is deployed. 
     Besides, according to the embodiment, the accuracy of congestion determination can be improved and the equipment use efficiency of the base station or the like can be raised. Since the equipment use efficiency is improved, the operator can suppress equipment investment, while the user can receive the best service which can be received at each time irrespective of the area and time. Since quality of experience of user is visualized and controlled, the operator further classifies customers and can realize a premium service and the like.