Communication Bandwidth Calculation Device, Communication Bandwidth Calculation Method, and Program

Abstract

This communication bandwidth calculation device, which calculates the required bandwidth of communication facilities, is provided with: an information acquisition unit that acquires traffic information for each communication facility, and attribute statistic traffic information and corresponding attribute statistic attribute information which are statistically processed on the basis of traffic information and contract user information for each communication terminal; a prediction calculation unit that calculates macro traffic growth rate prediction information by analysis using the attribute statistic traffic information and the attribute statistic attribute information; and a required bandwidth calculation unit that calculates the required bandwidth of each communication facility on the basis of the macro traffic growth rate prediction information, the traffic information for each communication facility, and a correction factor for each communication facility.

Description

Technical Field

[0001] The present invention relates to a technique for calculating the required bandwidth of communication facilities in a communication network.

Background Art

[0002] Conventionally, regardless of the form of communication, whether it is fixed-line (wired) communication or wireless communication by a mobile body, for the communication services provided via a communication network, as the quality of the communication services required by the users, QoS (Quality of Service) or QoE (Quality of Experience) can be defined. Communication service providers design, operate, and manage communication networks to achieve such service quality.

[0003] To achieve the above object, in a communication network, traffic volume is constantly measured, and at the same time, the traffic characteristics of the provided communication services are analyzed and evaluated to obtain knowledge of the traffic characteristics. Utilizing the obtained knowledge, the communication traffic volume at a future time is predicted, and the quality of the communication services required by the users is achieved under the condition that the predicted traffic volume becomes a load on the communication network. On the other hand, a technique for calculating the amount of communication resources facilities without excess or deficiency for the economic efficiency of communication services is required.

[0004] As conventional technologies, there are many techniques for predicting the communication traffic volume at a future time and calculating the amount of communication facilities for communication networks that provide fixed telephone services and multiplexed various communication services, and for communication networks that provide secure communication services connecting LANs at corporate bases. Patent Document 1 is one of these technologies.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

SUMMARY OF THE INVENTION

PROBLEMS TO BE SOLVED BY THE INVENTION

[0006] Regardless of the form of communication, whether it is communication by a fixed line (wired) or wireless communication by a mobile body, the diversification of communication services such as Internet connection services, video distribution services, VPN services, games, IP telephones, video telephones, and SNS has been rapidly progressing. In the use of these communication services, there is a large difference in the amount of transfer data required per unit time.

[0007] At the same time, there is also a large bias in the communication services used by individual users due to their preferences. Therefore, there are extremely large differences in the amount of transfer data consumed by individual users and the rate of increase thereof, making it increasingly difficult to predict future traffic volumes.

[0008] On the other hand, due to the progress of the digitalization of social life, strong demands for personal information protection have emerged, and the use of personal information managed by communication carriers for the design, operation, and management of communication traffic is severely restricted.

[0009] The present invention has been made in view of the above points, and an object thereof is to provide a technology for accurately calculating the communication bandwidth required to achieve the quality of communication services by predicting future traffic volumes while protecting personal information.

MEANS FOR SOLVING THE PROBLEMS

[0010] According to the disclosed technology, a communication bandwidth calculation device for calculating a required bandwidth in communication facilities of a communication network, at the future design target time an information acquisition unit that acquires attribute statistical traffic information and corresponding attribute statistical attribute information statistically processed based on traffic information for each communication terminal and contract user information, and traffic information for each communication facility; ​By analyzing using the attribute-statistical traffic information and the attribute-statistical attribute information, at the future design target time a prediction calculation unit that calculates macro traffic growth rate prediction information; a required bandwidth calculation unit that calculates the required bandwidth for each communication facility based on the macro traffic growth rate prediction information, the traffic information for each communication facility, and the correction coefficient for each communication facility; and The prediction calculation unit a traffic prediction calculation unit that performs a regression prediction calculation on the attribute-statistical traffic information to calculate attribute-statistical traffic prediction information; a share prediction calculation unit that performs a regression prediction calculation on the attribute-statistical attribute information to calculate attribute-statistical share prediction information; a share weighting unit that performs share weighted averaging on the attribute-statistical traffic prediction information and the attribute-statistical share prediction information to calculate user average traffic prediction information; a contract number prediction unit that performs a regression prediction calculation on the attribute-statistical attribute information to calculate contract number prediction information; a macro traffic growth rate prediction unit that calculates the macro traffic growth rate prediction information using the user average traffic prediction information and the contract number prediction information; comprises A communication bandwidth calculation device is provided.

Advantages of the Invention

[0011] According to the disclosed technology, it is possible to accurately calculate the communication bandwidth required to achieve the quality of communication services by predicting the future traffic volume while protecting personal information.

Brief Description of the Drawings

[0012]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Modes for Carrying Out the Invention

[0013] Hereinafter, embodiments of the present invention (hereinafter referred to as "the present embodiments") will be described with reference to the drawings. The embodiments described below are merely examples, and the embodiments to which the present invention is applied are not limited to the following embodiments.

[0014] (Overview of the Embodiment) In the present embodiment, for communication facilities in a communication network where the communication traffic volume increases (decreases) while fluctuating complexly, a technique for predicting and calculating the amount of bandwidth facilities required at a future design target time is described in order to economically provide the communication service quality required by users.

[0015] The above calculation process is executed by a communication bandwidth calculation device 10 described later. More specifically, the communication bandwidth calculation device 10 predicts the amount of transfer data for each attribute at a future design target time from only the information statistically classified by attributes, thereby predicting the load traffic volume for the entire communication network to be designed, and accurately calculating the communication bandwidth required to achieve the quality of the communication service.

[0016] In the present embodiment, it is assumed that a situation where the calculation and evaluation of the amount of bandwidth facilities required in the future are continuously carried out along with the management and operation of communication network facilities by a communication carrier. Taking a certain period as a cycle, the bandwidth required to ensure the quality of communication services at a desired future time point is calculated, the future bottleneck and congestion degree of the existing bandwidth facilities are estimated, and construction work such as additional facility installation required as the difference is planned and executed. Hereinafter, the present embodiment will be described in detail with reference to the drawings.

[0017] (Overall Configuration of the System) FIG. 1 is an overall block diagram showing a system configuration including a communication band calculation device 10 according to the present embodiment. In the present embodiment, as a communication network for which a communication band is to be calculated, a communication network 20 that provides a data communication service will be described as an example. In the present embodiment, it is assumed that data communication using the IP protocol is performed in the communication network 20. However, data communication using the IP protocol is merely an example, and the technology according to the present invention is applicable regardless of the type of protocol.

[0018] As shown in FIG. 1, the communication network 20 is a facility for the purpose of providing a data communication service to the communication terminals 41, 42, 43, 44. PC terminals 45, 46, 47, 48 are connected to the communication terminals 41, 42, 43, 44, respectively. The data information transmitted and received by the communication terminals is transferred to the node 21 via the access nodes 23, 24, the band facilities 31, 32, the node 22, and the band facility 30, and then sequentially transferred to the communication terminal of the desired transmission / reception destination within the communication network 20. Thereby, the data communication service is achieved.

[0019] Note that the node is, for example, a router or a switch. Also, the band facility may be referred to as a transmission line, a communication line, a circuit, or the like. Both the node and the band facility are examples of communication facilities.

[0020] The communication band calculation device 10 is configured by an information processing device using a computer. The communication band calculation device 10 periodically or timely acquires network facility configuration information 51 including information on communication facilities such as node information, line band information, and topology information regarding the communication network 20 from the operation system 61.

[0021] The facility unit traffic information 52 shown in FIG. 1 will be described. For example, the facility unit traffic information 52 related to the bandwidth facility 30 includes measurement data obtained by measuring the traffic volume flowing in and out of the bandwidth facility 30 at nodes 21 and 22 at regular time intervals. The operation system 61 holds the facility unit traffic information 52 for all communication facilities subject to operation, management, and design.

[0022] The terminal unit traffic information 53 shown in FIG. 1 will be described. For example, the terminal unit traffic information 53 related to the communication terminal 41 is information obtained by counting the amount of communication data transmitted and received by the communication terminal 41 within a specified period (such as a billing period) specified in the data communication service by identifying it with the communication terminal ID of the communication terminal 41. The operation system 61 holds the terminal unit traffic information 53 for all communication terminals providing the communication service.

[0023] The communication bandwidth calculation device 10 periodically or timely acquires the network facility configuration information 51 and the facility unit traffic information 52 from the operation system 61.

[0024] The telecommunications carrier manages information such as name, gender, date of birth (age), address, contract plan, communication terminal ID associated with the contract plan, and telephone number for users who contract for the data communication service. These management information are called user unit information 54. The items of these management information are called attribute items, and the specific description content is called attribute values. The user unit information 54 for all users who contract for the data communication service is held in the user contract management system 62.

[0025] Also, the user contract management system 62 periodically or timely obtains the terminal unit traffic information 53 from the operation system 61.

[0026] By matching the communication terminal IDs included in the per-terminal traffic information 53, for each communication terminal, it is possible to associate the user unit information 54 such as the contract user and contract plan with the per-terminal traffic information 53 such as the amount of communication data transmitted and received within a specified period (such as a billing period) using the communication terminal.

[0027] The user unit information 54 includes personal information, and its use is extremely strictly restricted by laws such as the Personal Information Protection Law. Therefore, it is a reasonable reason for the communication carrier to perform processing so that the information used for the design, management, and operation of the communication network facilities does not include personal information. Here, the process of processing information including personal information into information that does not include personal information (non-personal information) is called statisticalization, and such processing is called statistical processing.

[0028] In the present embodiment, as will be described in detail later, in order to clarify the state of being non-personal information by statistical processing related to the attributes included in the user unit information 54, the term "attribute statisticalization" is used.

[0029] The statistical processing device 63 shown in FIG. 1 can obtain the per-terminal traffic information 53 and the user unit information 54 from the user / contract management system 62, and by matching the two using the communication terminal ID, the user information and the traffic information can be related.

[0030] The statistical processing device 63 can perform statistical processing using attribute information such as name, gender, date of birth (age), address, and contract plan included in the user unit information 54. For example, the age can be calculated from the date of birth and statistically classified into user attributes of an age group such as the 30s (30 years old or more and 39 years old or less). The address can be statistically classified into user attributes at the prefecture level. In order to statistically classify user unit information such as being in the 30s and living in Tokyo, the information specifying the conditions for statisticalization by the attribute item and its attribute value is called statisticalization instruction information 47. The statisticalization instruction information 47 is obtained from the communication bandwidth calculation device 10.

[0031] Furthermore, the statistical processing device 63 extracts, for example, a set of communication terminals whose user attributes correspond to those in their 30s and living in Tokyo, from the traffic information associated by matching using the communication terminal ID, based on the statistical instruction information 57 which is information specifying the user attributes to be statistically processed. Then, using the history information of the transferred data volume in the per-terminal traffic information 53, it statistically processes them as the number of communication terminals included in an interval such as a transferred data volume of 5 GB or more and less than 10 GB, and can output, for example, a histogram.

[0032] In this way, the statistically processed traffic information (non-personal information) is called attribute-statistical traffic information 55 based on the statistical instruction information 57 which is information specifying the conditions for statistical processing by the attribute items included in the user attributes and their attribute values.

[0033] Also, information (non-personal information) statistically processed based on user attributes other than traffic information, such as the number or share of users for each user attribute and the number of communication terminals, is called attribute-statistical attribute information 56.

[0034] The communication bandwidth calculation device 10 periodically or as needed acquires the network facility configuration information 51 and the per-facility traffic information 52 from the operation system 61. Also, the communication bandwidth calculation device 10 periodically or as needed acquires the attribute-statistical traffic information 55 and the attribute-statistical attribute information 56 from the statistical processing device 63. Furthermore, the communication bandwidth calculation device 10 creates the statistical instruction information 57 and periodically or as needed provides it to the statistical processing device 63 for use as information specifying the conditions for statistical processing.

[0035] Since both the attribute-statistical traffic information 55 and the attribute-statistical attribute information 56 are already statistical information (non-personal information) that does not include personal information, the communication bandwidth calculation device 10 only uses the statistical information (non-personal information) and does not use personal information.

[0036] The communication bandwidth calculation device 10 can accurately calculate the communication bandwidth required to achieve the quality of communication services by predicting the load traffic volume for the entire communication network to be designed by predicting the amount of transfer data for each attribute at the future design target time from only the information statistically processed based on the acquired attributes.

[0037] (Internal configuration of the communication bandwidth calculation device 10) Next, the internal configuration of the communication bandwidth calculation device 10 according to the present embodiment will be described in detail.

[0038] The configuration of the communication bandwidth calculation device 10 shown in FIG. 1 shows an example of its hardware configuration when the communication bandwidth calculation device 10 is implemented by a computer. However, the communication bandwidth calculation device 10 may be a physical machine or a virtual machine. When the communication bandwidth calculation device 10 is implemented by a virtual machine, the hardware configuration shown in FIG. 1 becomes a virtual hardware configuration.

[0039] As shown in FIG. 1, the communication bandwidth calculation device 10 is provided with main components including a communication interface unit 11 (hereinafter referred to as the communication I / F unit 11), an operation input unit 12, a screen display unit 13, an information database unit 14 (hereinafter referred to as the information DB unit 14), a storage unit 15, and an arithmetic processing unit 16, which are connected via an internal communication bus and can transmit and receive information to and from each other.

[0040] The communication I / F unit 11 consists of a dedicated data communication circuit and has a function of communicating with external devices such as an operation system 61.

[0041] The operation input unit 12 consists of an operation input device such as a keyboard or a mouse, and has a function of detecting an input operation from an operator and outputting it to the arithmetic processing unit 16.

[0042] The screen display unit 13 is a screen display device such as a display, and has a function of displaying various information such as an operation menu and a calculation result on the screen according to an instruction from the arithmetic processing unit 16.

[0043] The information DB unit 14 consists of a storage device such as a hard disk or a memory, and has a function of storing various data used in the necessary bandwidth calculation process in the arithmetic processing unit 16.

[0044] The storage unit 15 consists of a storage device such as a hard disk or a memory, and has a function of storing various programs and data used in the necessary bandwidth calculation process in the arithmetic processing unit 16.

[0045] The arithmetic processing unit 16 has a microprocessor such as a CPU (Central Processing Unit) and its peripheral circuits. By reading the program in the storage unit 15 and executing the program, the network facility configuration information 51, the facility unit traffic information 52, the attribute statistical traffic information 55, the attribute statistical attribute information 56, etc. required for arithmetic processing are obtained regularly or timely from the information DB 14 or the operation of the operation input unit 12. By predicting the amount of transfer data for each attribute at the future design target time from only the information statistically processed by the attribute, the load traffic amount for the entire communication network to be designed is predicted, the communication bandwidth required to achieve the quality of the communication service is accurately calculated, and the calculation result is externally output to the information DB unit 14, etc.

[0046] The program for realizing the processing in the communication bandwidth calculation device 10 is provided by a recording medium such as a CD-ROM or a memory card, for example. The program read from the recording medium is stored in the storage unit 15, for example, and read and executed by the arithmetic processing unit 16. Note that the program may be downloaded from a server or the like via a network.

[0047] (Regarding various information) The network equipment configuration information 51 shown in FIG. 1 includes the bandwidth information B_j of any interface j of the communication equipment being managed and operated within the communication network 20 including the bandwidth equipment 30, 31, 32, 33, 34, 35, 36, nodes 21, 22, and access nodes 23, 24. Further, the network equipment configuration information 51 includes information on the connection configuration relationship between any interfaces of the communication equipment being managed and operated within the communication network 20.

[0048] Furthermore, it is assumed that the network equipment configuration information 51 includes information on the connection configuration relationship between any interfaces of the communication equipment operated in the communication network 20 not only at present but also over the planned equipment construction schedule in the future from the past equipment construction history. For simplicity, the network equipment configuration information 51 is denoted as {BD}.

[0049] The equipment unit traffic information 52 includes time-series data of the measured traffic volume continuously measured over a long period by a predefined measurement period regarding the traffic volume flowing in / out of any interface j of the communication equipment operated in the communication network 20. Let the measured traffic volume in the measurement period t of the interface j be Y_j(t), and the set of its time-series data be defined as {Y_j(t), t ∈ T}. This is called the time-series data of the measured traffic volume, or simply the measured traffic volume. The measurement of the traffic volume is in principle continued for all communication equipment and their interfaces. For simplicity, the equipment unit traffic information 52 is denoted as {DT}.

[0050] The terminal unit traffic information 53 will be described. As described above, for any user who contracts for the data communication service of the communication carrier, the correspondence of the communication terminal bundled with the content of the contract is managed. The terminal unit traffic information 53 is the communication data volume data accumulated for each of the communication terminals in units of a certain period (for example, monthly unit) and includes past history information. For simplicity, the terminal unit traffic information 53 is denoted as {TT}.

[0051] The user unit information 54 will be described. As described above, for the purpose of charging and providing appropriate services, a communications carrier manages information such as name, gender, date of birth (age), address, contract plan, communication terminal ID associated with the contract plan, and telephone number for users who contract for data communication services, and these management information are called user unit information 54.

[0052] The attribute-statistical traffic information 55 and the attribute-statistical attribute information 56 will be described. As described above, by matching the terminal unit traffic information 53 and the user unit information 54 using the communication terminal ID commonly included in both, after relating the user information and the traffic information, based on the above-described statistical instruction information 57, the statistically processed traffic information is called the attribute-statistical traffic information 55. Statistically processed information other than traffic information, such as the number of users for each user attribute, share, and the number of communication terminals, is called the attribute-statistical attribute information 56.

[0053] The statistical instruction information 57 will be described. As described above, the statistical instruction information 57 is information including attribute items, attribute values, and specific set operations and statistical processes specified as conditions for statistical processing in order to perform statistical processing for the purpose of processing the user unit information 54 including personal information and the terminal unit traffic information 53 into non-personal information. The statistical instruction information 57 is set (created) in the communication bandwidth calculation device 10 and is acquired by the statistical processing device 63 regularly or as needed and applied to the statistical processing.

[0054] (Configuration of the arithmetic processing unit 16) Next, with reference to FIG. 2, the internal configuration of the arithmetic processing unit 16 according to the present embodiment will be described in detail. FIG. 2 is a block diagram showing each processing unit for communication bandwidth calculation in the arithmetic processing unit 16.

[0055] The internal configuration of the arithmetic processing unit 16 shown in FIG. 2 corresponds to a functional configuration realized by the arithmetic processing unit 16 by executing a program. The internal configuration of the arithmetic processing unit 16 shown in FIG. 2 may be interpreted as the functional configuration of the communication bandwidth calculation device 10.

[0056] As shown in FIG. 2, the arithmetic processing unit 16 includes, as main processing units, an information acquisition unit 16A, a prediction design unit 16B, a prediction calculation unit 16C, and a required bandwidth calculation unit 16D.

[0057] The information acquisition unit 16A periodically or as needed acquires, from the operation system 61, network facility configuration information 51 and facility unit traffic information 52, which are information necessary for communication bandwidth calculation, regarding the communication facilities of the communication network 20 to be designed. Further, the information acquisition unit 16A periodically or as needed acquires, from the statistical processing device 63, attribute statistical traffic information 55 and attribute statistical attribute information 56.

[0058] As described above, in the statistical processing device 63, attribute traffic information 55 and attribute statistical attribute information 56 are created by statistical processing using the terminal unit traffic information 53 and user unit information 54 as inputs. The prediction design unit 16B creates statistical instruction information 57, which is the condition or procedure for the statistical processing for the attribute items and attribute values necessary for the statistical processing.

[0059] Since the statistical instruction information 57 needs to be the attribute items of the users described and managed in the user unit information 54 managed in the user - contract management system 62, the prediction design unit 16B acquires the attribute items from the information DB unit 14. The accommodation of the attribute items in the information DB unit 14 may be information obtained using the operation input unit 12 or information obtained by communication with the user - contract management system 62.

[0060] The prediction design unit 16B creates statistical instruction information 57, which is the condition or procedure for the statistical processing, from the attribute items.

[0061] As described above, for each contract user, the age can be calculated from the date of birth, integrated in units of 10 years, and statistically processed into the user attributes of the age group. The addresses can be integrated by prefecture and statistically processed into the user attributes of the prefecture. Furthermore, a histogram of the transferred data volume can also be generated for users who meet the AND condition (intersection set) of the user attributes of the age group and the prefecture. Since individuals cannot be identified from the data statistically processed in this way, it is regarded as non-personal information.

[0062] Such statistical processing can generally be described by set operations on the attribute items and their attribute values. The created statistical instruction information 57 is acquired by the statistical processing device 63 periodically or as needed.

[0063] Since the results of traffic prediction and share prediction can vary greatly depending on the instruction content of the statistical instruction information 57, the attribute items and their attribute values for which high prediction accuracy is expected are adjusted by reconsidering the results of various predictions as described later, and the optimal statistical instruction information is designed. The statistical instruction information 57 created by the prediction design unit 16B is stored in the information DB unit 14.

[0064] The prediction calculation unit 16C acquires the attribute-statistical traffic information 55, the attribute-statistical attribute information 56, the network facility configuration information 41, and the facility-unit traffic information 52 from the information acquisition unit 16A.

[0065] Using these pieces of information as input, the prediction calculation unit 16C calculates, by regression prediction and the like, which will be described in more detail later, the attribute-statistical traffic prediction information 71, the attribute-statistical share prediction information 72, the user average traffic prediction information 73, the contract number prediction information 74, and the macro traffic growth rate prediction information 75 related to future traffic prediction.

[0066] The attribute-statistical traffic prediction information 71, the attribute-statistical share prediction information 72, the user average traffic prediction information 73, the contract number prediction information 74, and the macro traffic growth rate prediction information 75 are stored in the information DB unit 14.

[0067] The necessary bandwidth calculation unit 16D acquires the network facility configuration information 51 and the facility unit traffic information 52 from the information acquisition unit 16, acquires the macro traffic growth rate prediction information 75 from the prediction calculation unit 16C, and acquires the correction coefficient information 77 from the information DB unit 14. Using these pieces of information, the necessary bandwidth calculation unit 16D calculates necessary bandwidth information 76, which is information on the necessary bandwidth for the target communication facilities at the design target time.

[0068] (Regarding the necessary bandwidth) Here, the necessary bandwidth will be explained. The traffic data included in the facility unit traffic information 52 is a numerical value of the average traffic flow bit / sec at a measurement time granularity such as one hour or five minutes for each communication facility. At a time granularity shorter than the measurement time granularity, there must always be a moment when the actual traffic flow exceeds the measured traffic volume within the same measurement time.

[0069] The transmission of IP packets flowing through a communication flow by the IP protocol has a large bias rather than a uniform speed. This instantaneous bias property of IP packets is called burstiness. Strongly influenced by the burstiness of the IP packet flow, the property that the actual traffic volume instantaneously exceeds the measured traffic volume in the same measurement period is called short-term traffic variation.

[0070] That is, at the future design target time, just having a bandwidth facility with the same facility capacity as the measured traffic volume cannot absorb the short-term traffic variation.

[0071] Therefore, for the purpose of ensuring the communication quality expected by users in a communication service, a bandwidth that can surely absorb the short-term traffic variation is required. Thus, it is necessary to calculate an optimal bandwidth that is larger than the measured traffic volume, can absorb the short-term traffic variation, and does not become uneconomical. The optimal bandwidth considering the short-term variation in this way will be called the "necessary bandwidth".

[0072] The coefficient for converting the measured traffic volume described above into the required bandwidth shall be referred to as the correction coefficient. The correction coefficient is a correction coefficient for filling the following gaps and can be set for each communication facility unit.

[0073] First, the traffic volume measured by the communication facility is a value averaged over the measurement time interval. On the other hand, in actual communication, there is burstiness at the IP packet level, and there are moments when the traffic becomes larger than the measured value. Due to the scale gap between the measurement time scale of the traffic volume of the communication facility and the time scale at which the burstiness of the IP packets occurs, depending on the burstiness of the IP packets, there is a possibility that IP packets may be lost within a bandwidth equivalent to the measured traffic volume. Therefore, an excess bandwidth is required to absorb the burstiness of the IP packets and prevent the loss of IP packets. The required bandwidth is the measured traffic volume plus the excess bandwidth, and in this sense, correction is required between the measured traffic volume and the required bandwidth.

[0074] In a state where an actual (including emulated) communication traffic load is flowing through a standard communication facility, it becomes possible to calculate correction values for calculating the required bandwidth for the measured traffic volume from device verification such as measuring the short-term variation amount of IP packets by packet capture, etc., and the performance specifications of the communication device.

[0075] Second, there is a large scale gap between the measurement time interval of the original data used to calculate the traffic volume as a macro (for example, the amount of transferred data per individual is accumulated on a monthly basis) and the time interval for measuring the traffic volume by the communication facility (for example, 5 minutes). Furthermore, since there are large time variations in communication service demand or traffic demand, such as peak hours and off-peak hours, it is necessary to correct between the traffic volume as a macro (and its predicted value) and the maximum traffic volume as the load measured by the communication facility.

[0076] However, since the demand for communication services and the peak hours are relatively stable because they are strongly linked to social life, it is possible to calculate the correction value by verifying past data.

[0077] Thirdly, since the traffic growth rate as a macro and the traffic growth rate of each communication facility generally do not match, it is also necessary to correct the gap between them. However, this correction can also calculate the correction value by verifying past data.

[0078] The combined correction values for correcting the above three gaps related to each communication facility are again called correction values. The correction values for each communication facility aggregated for the entire communication facility are called correction coefficient information 77. The correction coefficient information 77 is generated and managed by the information DB unit 14.

[0079] Note that the correction values constituting the correction coefficient information 77 are not limited to those obtained by combining the correction values for correcting the above three gaps. Correction values for correcting gaps other than the above three gaps may be used, or correction values for correcting any one or any two of the above three gaps may be used.

[0080] The required bandwidth calculation unit 16D acquires the macro traffic growth rate information 75, the traffic information 52 per facility, and the correction coefficient information 77.

[0081] Then, for the communication facility_j for which the required bandwidth is to be calculated, the required bandwidth calculation unit 16D uses the macro traffic growth rate r(m) at the design target time m included in the macro traffic growth rate information 75, the traffic volume Y_j(0) (for simplicity, the measurement time point is set to 0) measured most recently for the communication facility_j included in the traffic information 52 per facility, and the correction coefficient K_j for the communication facility_j included in the correction coefficient information 77, and calculates the required bandwidth B_j(m) at the design target time m by the following formula.

[0082] B_j(m)= r(m) * Y_j(0) * K_j The necessary bandwidth calculation unit 16D stores the necessary bandwidth information 77 in the information DB unit 14.

[0083] (Processing of the prediction calculation unit) Next, with reference to FIGS. 3 to 5, the processing of the prediction calculation unit 16C according to the present embodiment will be described in detail. FIGS. 3 to 5 are flowcharts showing the processing of the prediction calculation unit 16C. In these flowcharts, it is also described which functional unit in the prediction calculation unit 16C executes the processing of each step.

[0084] First, with reference to FIG. 3, the creation process of the attribute statistical traffic prediction information and the attribute statistical share prediction information will be described. In S110, the prediction calculation control unit 16C1 acquires the attribute statistical traffic information 55 and the attribute statistical attribute information 56 from the information acquisition unit 16A. The attribute statistical traffic information 55 and the attribute statistical attribute information 56 are described as {ST} and {AT}, respectively.

[0085] Also, in S120, the prediction calculation control unit 16C1 acquires regression prediction calculation parameters including the time period to be predicted, the period used for regression, etc., which are necessary for executing the regression prediction calculation described later, from the prediction design unit 16B. The regression prediction calculation parameters are described as {R}.

[0086] Next, the calculation algorithm of the regression prediction used in the present embodiment will be described. The regression prediction used here is regression analysis in statistics, and various methods such as linear regression, non-linear regression, and logistic regression can be applied. Here, the simplest case will be described using a linear simple regression model.

[0087] ·In the case of traffic prediction The traffic volume measured for each communication facility unit is defined as Y_k(X_k) with the measurement time point X_k, and they are used as time series data {(X_k, Y_k), (k = 1,..., p)}. In regression analysis, constants a and b that define a straight line Y = aX + b with the smallest error from the time series data are obtained. The error is generally defined by the sum of squares (least squares method).

[0088] The value obtained by extrapolating this straight line to the future time point m is set as the traffic prediction value Y(m) = am + b of the communication facility. Data lengths (p) to be used, etc. are included in the regression prediction calculation parameters.

[0089] · In the case of share prediction By using the share {S_k} instead of the above traffic volume {Y_k}, share prediction by linear simple regression can be performed.

[0090] · In the case of contract number prediction By using the contract number {Z_k} instead of the above traffic volume {Y_k}, contract number prediction by linear simple regression can be performed. Contract number prediction for each attribute is also possible in the same way.

[0091] In S210, the traffic prediction calculation unit 16C2 acquires the attribute - statistical traffic information 55 ({ST}) and the regression prediction calculation parameters ({R}) from the prediction calculation control unit 16D.

[0092] Next, in S220, the traffic prediction calculation unit 16C2 performs the above - mentioned regression prediction calculation on the traffic using the attribute - statistical traffic information 55 and the regression prediction calculation parameters. The result of the regression prediction calculation is called the attribute - statistical traffic prediction information 71. In S230, the attribute - statistical traffic prediction information 71 is stored in the information DB unit 14.

[0093] In S310, the share prediction calculation unit 16C3 acquires the attribute - statistical attribute information 56 ({AT}) and the regression prediction calculation parameters ({R}) from the prediction calculation control unit 16D.

[0094] Next, in S320, the share prediction calculation unit 16C3 performs the above-described regression prediction calculation on the attribute share included in the attribute statistical attribute information 56 and using the regression prediction calculation parameters. The attribute share is the share (occupancy rate) of users for each attribute. The result of the regression prediction calculation is called the attribute statistical share prediction information 72. In S330, the attribute statistical share prediction information 72 is stored in the information DB unit 14.

[0095] Next, with reference to FIG. 4, a method for creating the user average traffic prediction information 73 and the contract number prediction information 74 will be described. In S410, the share weighting unit 16C4 acquires the attribute statistical traffic prediction information 71 ({PT}) from the traffic prediction calculation unit 16C2 and acquires the attribute statistical share prediction information 72 ({PS}) from the share prediction calculation unit 16C3.

[0096] In S420, the share weighting unit 16C4 performs a share weighting process in order to convert the traffic prediction for each attribute into a traffic prediction at the macro level using the attribute statistical traffic prediction information 71 ({PT}) and the attribute statistical share prediction information 72 ({PS}). The content of the share weighting process is as follows.

[0097] When the share prediction value at an arbitrary future time point m is S_i(m) and the traffic prediction value for the attribute is TD_i(m) for the attribute value_i (i = 1,..., n) of a certain attribute item, the average traffic prediction value TD(m) for the entire target users is

[0098]

Equation

[0099] In S510, the contract number prediction unit 16C5 acquires the attribute statistical attribute information 56 ({AT}) and the regression prediction calculation parameter ({R}) from the prediction calculation control unit 16C1.

[0100] Next, in S520, the contract number prediction unit 16C5 performs the above-described regression prediction calculation on the contract number by using the contract number included in the attribute statistical attribute information 56 and the regression prediction calculation parameter. The result of the regression prediction calculation is referred to as contract number prediction information 74 ({PU}). In S530, the contract number prediction information 74 is stored in the information DB unit 14.

[0101] Subsequently, with reference to FIG. 5, the processing of the macro traffic growth rate prediction unit 16C6 will be described.

[0102] In S610, the macro traffic growth rate prediction unit 16C6 acquires the user average traffic prediction information 73 ({PM}) from the share weighting unit 16C4, and acquires the contract number prediction information 74 ({PU}) from the contract number prediction unit 16C5. Further, the regression prediction calculation parameter ({R}) is read.

[0103] In S620, the macro traffic growth rate prediction unit 16C6 calculates Y(m)*Z(m) / Y(0) from the predicted user average traffic value Y(m) included in the user average traffic prediction information 73 and the predicted contract number Z(m) included in the contract number prediction information 74, and sets this as the macro traffic growth rate prediction information 75 ({PR}) at the future facility design time point m.

[0104] In S630, the macro traffic growth rate prediction unit 16C6 stores the macro traffic growth rate prediction information 75 in the information DB unit 14.

[0105] (Another configuration and processing of the traffic prediction calculation unit) Next, with reference to FIG. 6, another configuration and processing of the traffic prediction calculation unit 16C2 according to the present embodiment will be described in detail. In this example, the traffic prediction calculation unit 16C2 includes a heavy / light separation unit and a regression prediction / share weighting unit.

[0106] Even among users belonging to the same attribute, there are significant differences in the amount of transferred data used and consumed for communication services. Here, users with a small amount of transferred data are defined as light users, and users with a large amount of transferred data are defined as heavy users. After further separating users within the same attribute into light users and heavy users, the prediction accuracy is improved by predicting the traffic volume and share of each group.

[0107] To separate light users and heavy users, the characteristics of the power-law distribution are used. The power-law distribution refers to a distribution where the probability density function p(x|x>x min ) = Cx -α (α, C, x min are positive constants). By using logarithmic scales on both axes, it becomes a straight line with a slope of (-α). It has the characteristic that the probability of the tail is heavier compared to the normal distribution.

[0108] This distribution appears in natural phenomena, and it is known that the distributions of the size of urban population, earthquake magnitude, computer files, wealth owned, etc. follow the power-law. Regarding communication data volume, there is also a report in reference [1] (Paxson V. et al. (1995) Wide Area Traffic: The Failure of Poisson Modeling.) that it follows the power-law.

[0109] An algorithm for fitting the actual distribution such as the amount of transferred data of users with the power-law and separating the tail part (heavy users) of the distribution that follows the power-law from the part that does not follow the power-law (light users) is described in reference [2] (Clauset A. et al. (2009) Power-law Distributions in Empirical Data.).

[0110] In SS210 of FIG. 6, the traffic prediction calculation unit 16C2 acquires the attribute-statistical traffic information 55 ({ST}), the attribute-statistical attribute information 56 ({AT}), and the regression prediction calculation parameter ({R}) from the prediction calculation control unit 16C1.

[0111] Next, in SS220, the heavy / light separation unit separates heavy users and light users from the attribute-statistical traffic information 55 by the algorithm described in Reference [2].

[0112] However, as a separation method, a simple method may be used in which a user who consumes u times or more (u is a constant greater than 1) of the average transfer data volume of the attribute obtained from the attribute-statistical traffic information 55 is determined as a heavy user, and those less than that are determined as light users.

[0113] Furthermore, the regression prediction / share weighting unit performs the following processing.

[0114] In SS230, the regression prediction / share weighting unit performs the aforementioned regression prediction calculation on the traffic (transfer data volume) of heavy users using the attribute-statistical traffic information 55 and the regression prediction calculation parameter for heavy users.

[0115] In SS240, the regression prediction / share weighting unit calculates the share within the attribute of heavy users, and performs the aforementioned regression prediction calculation on the share time series of the heavy users using the regression prediction calculation parameter.

[0116] In SS250, the regression prediction / share weighting unit performs the aforementioned regression prediction calculation on the traffic (transfer data volume) of light users using the attribute-statistical traffic information 55 and the regression prediction calculation parameter for light users.

[0117] In SS260, the regression prediction / share weighting unit calculates the share within the attribute of light users, and performs the aforementioned regression prediction calculation on the share time series of the light users using the regression prediction calculation parameter.

[0118] In SS270, the regression prediction and share weighting unit performs share weighting processing for heavy users and light users. The obtained result is used as the attribute statistical traffic prediction information 71.

[0119] Thereby, traffic prediction for attributes can be performed, taking into account the consumption transfer data volume and share trends of heavy users and light users. In SS280, the attribute statistical traffic prediction information 71 ({PT}) is stored in the information DB unit 14.

[0120] (Supplementary Explanation) For the sake of convenience of explanation, the communication bandwidth calculation device according to the present embodiment is described using a functional block diagram. However, the communication bandwidth calculation device according to the present embodiment may be implemented by hardware, software, or a combination thereof. Also, each functional unit may be used in combination as needed. Further, the method according to the present embodiment may be implemented in an order different from the order shown in the embodiment.

[0121] (Effects of the Embodiment) By predicting the transfer data volume for each attribute at the future design target time from only the information statistically classified by attributes using the technology described above, it is possible to predict the load traffic volume for the entire communication network to be designed, and accurately calculate the communication bandwidth required to achieve the quality of the communication service.

[0122] (Supplementary Note) Regarding the above embodiment, the following supplementary claims are further disclosed. (Supplementary Claim 1) A communication bandwidth calculation device for calculating the required bandwidth of communication facilities in a communication network, a memory, at least one processor connected to the memory, and including the processor Attribute statistical traffic information and corresponding attribute statistical attribute information obtained by performing statistical processing based on traffic information and contract user information for each communication terminal, and obtain traffic information for each communication facility. Calculate macro traffic growth rate prediction information through analysis using the attribute statistical traffic information and the attribute statistical attribute information. Calculate the required bandwidth for each communication facility based on the macro traffic growth rate prediction information, the traffic information for each communication facility, and the correction coefficient for each communication facility. Communication bandwidth calculation device. (Supplementary item 2) The processor creates statistical instruction information, which is instruction information for statistical processing according to attributes, from attribute items, and transmits the statistical instruction information to a statistical processing device that executes the statistical processing. The communication bandwidth calculation device according to claim 1. (Supplementary item 3) The processor Performs a regression prediction operation on the attribute statistical traffic information to calculate attribute statistical traffic prediction information. Performs a regression prediction operation on the attribute statistical attribute information to calculate attribute statistical share prediction information. Performs share weighted averaging on the attribute statistical traffic prediction information and the attribute statistical share prediction information to calculate user average traffic prediction information. Performs a regression prediction operation on the attribute statistical attribute information to calculate contract number prediction information. Calculate the macro traffic growth rate prediction information using the user average traffic prediction information and the contract number prediction information. The communication bandwidth calculation device according to supplementary item 1. (Supplementary item 4) The processor Separate users into heavy users and light users from the attribute statistical traffic information. Perform regression prediction operations on the traffic and share for heavy users and light users respectively, and further perform share weighting to calculate the attribute statistical traffic prediction information considering the usage trends of heavy users and light users. The communication bandwidth calculation device described in Supplementary Note 3. (Supplementary Note 5) A communication bandwidth calculation method executed by a computer used as a communication bandwidth calculation device for calculating the required bandwidth of communication facilities in a communication network, An information acquisition step of acquiring attribute statistical traffic information and corresponding attribute statistical attribute information statistically processed based on traffic information and contract user information for each communication terminal, and traffic information for each communication facility; A prediction calculation step of calculating macro traffic growth rate prediction information by analysis using the attribute statistical traffic information and the attribute statistical attribute information; A required bandwidth calculation step of calculating the required bandwidth for each communication facility based on the macro traffic growth rate prediction information, the traffic information for each communication facility, and the correction coefficient for each communication facility; A communication bandwidth calculation method comprising: (Supplementary Note 6) The prediction calculation step includes: A traffic prediction calculation step of performing a regression prediction calculation on the attribute statistical traffic information to calculate attribute statistical traffic prediction information; A share prediction calculation step of performing a regression prediction calculation on the attribute statistical attribute information to calculate attribute statistical share prediction information; A share weighting step of performing share weighted averaging on the attribute statistical traffic prediction information and the attribute statistical share prediction information to calculate user average traffic prediction information; A contract number prediction step of performing a regression prediction calculation on the attribute statistical attribute information to calculate contract number prediction information; A macro growth rate prediction step of calculating the macro traffic growth rate prediction information using the user average traffic prediction information and the contract number prediction information; The communication bandwidth calculation method according to Supplementary Note 5, comprising: (Supplementary Note 7) The traffic prediction calculation step includes: A heavy / light separation step of separating users into heavy users and light users from the attribute statistical traffic information; Performing regression prediction calculations of traffic and share for heavy users and light users respectively, and further performing share weighting to calculate the attribute statistical traffic prediction information considering the usage trends of heavy users and light users, a regression prediction and share weighting step; The communication bandwidth calculation method according to appended claim 6 comprising the above. (Appended claim 8) A non-transitory storage medium storing a program for causing a computer to execute each process in the communication bandwidth calculation apparatus according to any one of appended claims 1 to 4.

[0123] As described above, by predicting the amount of transfer data for each attribute at the future design target time from only the information statistically processed by the attribute, the load traffic amount for the entire communication network to be designed is predicted, and the method for accurately calculating the communication bandwidth required to achieve the quality of the communication service has been described. However, the present invention is not limited to the above embodiments, and various modifications and applications are possible within the scope of the claims.

Explanation of Signs

[0124] 10 Communication bandwidth calculation apparatus 11 Communication I / F unit 12 Operation input unit 13 Screen display unit 14 Information DB unit 15 Storage unit 16 Arithmetic processing unit 16A Information acquisition unit 16B Prediction design unit 16C Prediction arithmetic unit 16C1 Prediction arithmetic control unit 16C2 Traffic prediction arithmetic unit 16C3 Share prediction arithmetic unit 16C4 Share weighting unit 16C5 Contract number prediction unit 16D Required bandwidth calculation unit 20 Communication network 21, 22 Nodes 23, 24 Access nodes Band devices 30, 31, 32, 33, 34, 35, 36 Communication terminals 41, 42, 43, 44 PC terminals 45, 46, 47, 48 Network equipment configuration information 51 Equipment unit traffic information 52 Terminal unit traffic information 53 User unit information 54 Attribute statistical traffic information 55 Attribute statistical attribute information 56 Statistical instruction information 57 Operating system 61 User contract management system 62 Statistical processing device 63 Attribute statistical traffic prediction information 71 Attribute statistical share prediction information 72 User average traffic prediction information 73 Contract number prediction information 74 Macro traffic growth rate prediction information 75 Required bandwidth information 76 Correction coefficient information 77

Claims

1. A communication bandwidth calculation device that calculates the required bandwidth for a future design target period in communication facilities of a communication network, comprising: an information acquisition unit that acquires attribute statistical traffic information and corresponding attribute statistical attribute information that have been statistically processed based on traffic information and contract user information for each communication terminal, and traffic information for each communication facility; a prediction calculation unit that calculates macro traffic growth rate prediction information for the future design target period by analyzing using the attribute statistical traffic information and the attribute statistical attribute information; a required bandwidth calculation unit that calculates the required bandwidth for each communication facility based on the macro traffic growth rate prediction information, the traffic information for each communication facility, and a correction coefficient for each communication facility; wherein the prediction calculation unit includes a traffic prediction calculation unit that performs a regression prediction calculation on the attribute statistical traffic information to calculate attribute statistical traffic prediction information; a share prediction calculation unit that performs a regression prediction calculation on the attribute statistical attribute information to calculate attribute statistical share prediction information; a share weighting unit that performs share weighted averaging on the attribute statistical traffic prediction information and the attribute statistical share prediction information to calculate user average traffic prediction information; a contract number prediction unit that performs a regression prediction calculation on the attribute statistical attribute information to calculate contract number prediction information; a macro traffic growth rate prediction unit that calculates the macro traffic growth rate prediction information using the user average traffic prediction information and the contract number prediction information; and is provided with a communication bandwidth calculation device.

2. A prediction design unit that creates statistical instruction information, which is instruction information for statistical processing according to attributes, from attribute items and transmits the statistical instruction information to a statistical processing device that executes the statistical processing The communication bandwidth calculation device according to claim 1, further comprising.

3. wherein the traffic prediction calculation unit includes a heavy / light separation unit that separates users into heavy users and light users from the attribute statistical traffic information; a regression prediction / share weighting unit that performs regression prediction calculations on traffic and share for heavy users and light users respectively, and further performs share weighting to calculate the attribute statistical traffic prediction information considering the usage trends of heavy users and light users; The communication bandwidth calculation device according to claim 1, further comprising.

4. A communication bandwidth calculation method executed by a computer used as a communication bandwidth calculation device for calculating the required bandwidth in the future design target period in communication facilities of a communication network, an information acquisition step of acquiring attribute statistical traffic information and corresponding attribute statistical attribute information statistically processed based on traffic information and contract user information for each communication terminal, and traffic information for each communication facility; a prediction calculation step of calculating macro traffic growth rate prediction information in the future design target period by analysis using the attribute statistical traffic information and the attribute statistical attribute information; a required bandwidth calculation step of calculating the required bandwidth for each communication facility based on the macro traffic growth rate prediction information, the traffic information for each communication facility, and a correction coefficient for each communication facility, comprising: In the prediction calculation step, the computer performs a regression prediction calculation on the attribute statistical traffic information to calculate attribute statistical traffic prediction information, a traffic prediction calculation step; performs a regression prediction calculation on the attribute statistical attribute information to calculate attribute statistical share prediction information, a share prediction calculation step; performs share weighted averaging on the attribute statistical traffic prediction information and the attribute statistical share prediction information to calculate user average traffic prediction information, a share weighting step; performs a regression prediction calculation on the attribute statistical attribute information to calculate contract number prediction information, a contract number prediction step; a macro traffic growth rate prediction step of calculating the macro traffic growth rate prediction information using the user average traffic prediction information and the contract number prediction information; A communication bandwidth calculation method for executing the above.

5. A program for causing a computer to function as each part of the communication bandwidth calculation device according to any one of Claims 1 to 3.

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