CONTROL SYSTEM AND CONTROL METHOD

An operation control module executes, for each of a plurality of base station system groups, operation control corresponding to a degree of operation of a leader base station system included in the base station system group, with respect to a follower base station system included in the base station system group. A removal module removes, in accordance with a fact that a magnitude of a deviation of a ratio of the degree of operation of a follower base station system included in one of the base station system groups with respect to the degree of operation of a leader base station system included in the base station system group from a given reference ratio has satisfied a predetermined condition, the follower base station system from the base station system group.

TECHNICAL FIELD

The present invention relates to a control system and a control method.

BACKGROUND ART

There is known a technology of executing, in accordance with a degree of operation of a certain base station system (hereinafter referred to as “leader base station system”) included in a base station system group, operation control of a different base station system (hereinafter referred to as “follower base station system”) included in this base station system group. As an example of such a technology, in Patent Literature 1, there is described a configuration in which, when a determination value calculated based on information on a radio base station which has the same group ID as that of a selected radio base station and is in an operation ON state is smaller than a threshold value, the selected radio base station is changed to an operation OFF state.

CITATION LIST

Patent Literature

SUMMARY OF INVENTION

Technical Problem

In a situation in which the operation control of the follower base station system is performed in accordance with the degree of operation of the leader base station system, in some cases, a tendency of a transition of the degree of operation of a specific follower base station system changes for some reasons such as an increase of the number of subscribers in a specific area, for example.

Such a follower base station system is brought into a state in which the operation control corresponding to the degree of operation of the leader base station system cannot be accurately performed. Thus, this follower base station system should no longer be included in this base station system group including this follower base station system.

The present invention has been made in view of the above-mentioned circumstances, and has an object to provide a control system and a control method which are capable of accurately removing a follower base station system which should not be included in a base station system group from this base station system group.

Solution to Problem

In order to solve the above-mentioned problem, according to one embodiment of the present invention, there is provided a control system including: degree-of-correlation identification means for identifying, for each pair of base station systems included in a plurality of base station systems, a degree of correlation of a transition of a degree of operation between the each pair of base station systems; classification means for classifying, based on the degree of correlation identified for the each pair of base station systems, the plurality of base station systems into a plurality of base station system groups; operation control means for executing, for each of the plurality of base station system groups, operation control corresponding to the degree of operation of a leader base station system included in the each of the plurality of base station system groups, with respect to a follower base station system included in the each of the plurality of base station system groups; and removal means for removing, in accordance with a fact that a magnitude of a deviation of a ratio of the degree of operation of a follower base station system included in one of the plurality of base station system groups with respect to the degree of operation of a leader base station system included in the one of the plurality of base station system groups from a given reference ratio has satisfied a predetermined condition, the follower base station system from the one of the plurality of base station system groups.

In one aspect of the present invention, the degree-of-correlation identification means is configured to identify the degree of correlation of the transition of the degree of operation for the each pair of base station systems based on transition data indicating the transition of the degree of operation of each of the plurality of base station systems, the control system further includes ratio identification means for identifying, based on the transition data, the given reference ratio being the ratio of the degree of operation of the follower base station system included in the one of the plurality of base station system groups with respect to the degree of operation of the leader base station system included in the one of the plurality of base station system groups, and the removal means is configured to remove the follower base station system from the one of the plurality of base station system groups in accordance with a fact that a magnitude of a difference between a value indicating the degree of operation of the leader base station system included in the one of the plurality of base station system groups and a value obtained by dividing a value indicating the degree of operation of the follower base station system included in the one of the plurality of base station system groups by the given reference ratio has satisfied a predetermined condition.

As another aspect, the degree-of-correlation identification means is configured to identify the degree of correlation of the transition of the degree of operation for the each pair of base station systems based on transition data indicating the transition of the degree of operation of each of the plurality of base station systems, the control system further includes ratio identification means for identifying, based on the transition data, the given reference ratio being the ratio of the degree of operation of the follower base station system included in the one of the plurality of base station system groups with respect to the degree of operation of the leader base station system included in the one of the plurality of base station system groups, and the removal means is configured to remove the follower base station system from the one of the plurality of base station system groups in accordance with a fact that a magnitude of a difference between a value obtained by multiplying a value indicating the degree of operation of the leader base station system included in the one of the plurality of base station system groups by the given reference ratio and a value indicating the degree of operation of the follower base station system included in the one of the plurality of base station system groups has satisfied a predetermined condition.

In those aspects, the removal means may be configured to remove the follower base station system satisfying a condition that a distribution of the differences identified a plurality of times is different from the distribution of another follower base station system, from the one of the plurality of base station system groups.

Further, the removal means may be configured to remove the follower base station system in which a magnitude of a variance or a standard deviation of the differences identified a plurality of times satisfies a predetermined condition, from the one of the plurality of base station system groups.

As another aspect, the removal means may be configured to remove the follower base station system in which an average of the differences identified a plurality of times satisfies a predetermined condition, from the one of the plurality of base station system groups.

Further, in one aspect of the present invention, the control system further includes prediction means for predicting the degree of operation of the leader base station system, the operation control means is configured to execute operation control of the leader base station system based on the degree of operation being a result of the predicting, and the operation control means is configured to execute the operation control of the follower base station system based on the degree of operation being the result of the predicting and on the given reference ratio.

Further, in one aspect of the present invention, the control system further includes leader determination means for determining, from among a plurality of base station systems included in the one of the plurality of base station system groups, the leader base station system based on the number of other base station systems in each of which the degree of correlation with respect to a corresponding one of the plurality of base station systems is larger than a predetermined magnitude.

Further, in one aspect of the present invention, the degree-of-correlation identification means is configured to identify, as the degree of correlation, a representative value of a cross-correlation coefficient of a value indicating the degree of operation.

Further, in one aspect of the present invention, the degree of operation of each of the plurality of base station systems is a traffic amount or the number of accommodated persons in an area covered by the each of the plurality of the base station systems.

Further, according to one embodiment of the present invention, there is provided a control method including the steps of: identifying, for each pair of base station systems included in a plurality of base station systems, a degree of correlation of a transition of a degree of operation between the each pair of base station systems; classifying, based on the degree of correlation identified for the each pair of base station systems, the plurality of base station systems into a plurality of base station system groups; executing, for each of the plurality of base station system groups, operation control corresponding to the degree of operation of a leader base station system included in the each of the plurality of base station system groups, with respect to a follower base station system included in the each of the plurality of base station system groups; and removing, in accordance with a fact that a magnitude of a deviation of a ratio of the degree of operation of a follower base station system included in one of the plurality of base station system groups with respect to the degree of operation of a leader base station system included in the one of the plurality of base station system groups from a given reference ratio has satisfied a predetermined condition, the follower base station system from the one of the plurality of base station system groups.

DESCRIPTION OF EMBODIMENTS

One embodiment of the present invention is now described in detail with reference to the drawings.

FIG.1andFIG.2are diagrams for illustrating an example of a communication system1in the one embodiment of the present invention.FIG.1is a diagram focusing on locations of a group of data centers included in the communication system1.FIG.2is a diagram focusing on various types of computer systems implemented by the group of data centers included in the communication system1.

As illustrated inFIG.1, the group of data centers included in the communication system1is classified into central data centers10, regional data centers12, and edge data centers14.

For example, several central data centers10are arranged in a dispersed manner in an area covered by the communication system1(for example, in Japan).

For example, several tens of regional data centers12are arranged in a dispersed manner in the area covered by the communication system1. For example, when the area covered by the communication system1is the entire area of Japan, one or two regional data centers12may be arranged in each prefecture.

For example, several thousands of edge data centers14are arranged in a dispersed manner in the area covered by the communication system1. Further, each of the edge data centers14can perform communication to/from a communication facility18including an antenna16. In this case, as illustrated inFIG.1, one edge data center14may be able to perform communication to/from several communication facilities18. The communication facility18may include a computer such as a server computer. The communication facility18in this embodiment performs radio communication to/from a user equipment (UE)20via the antenna16.

A plurality of servers are arranged in each of the central data center10, the regional data center12, and the edge data center14in this embodiment.

In this embodiment, for example, the central data center10, the regional data center12, and the edge data center14are able to perform communication to/from one another. Further, communication is also allowed between the central data centers10, between the regional data centers12, and between the edge data centers14.

As illustrated inFIG.2, the communication system1in this embodiment includes a network operation system (NOS)30, a plurality of radio access networks (RANs)32, a plurality of core network systems34, and a plurality of UEs20. The core network system34, the RAN32, and the UE20cooperate with each other so that a mobile communication network is achieved.

The RAN32is a computer system corresponding to an eNodeB (eNB) in a fourth generation mobile communication system (hereinafter referred to as “4G”) or an NR base station (gNB) in a fifth generation mobile communication system (hereinafter referred to as “5G”), and is a computer system including the antenna16. The RAN32in this embodiment is mainly implemented by the communication facility18and a group of servers arranged in the edge data center14. A part of the RAN32(for example, a virtual distributed unit (vDU) and a virtual central unit (vCU) in 4G, or a distributed unit (DU) and a central unit (CU) in 5G) may be implemented by the central data center10or the regional data center12instead of the edge data center14.

The core network system34is a system corresponding to an evolved packet core (EPC) in 4G or a 5G core (5GC) in 5G. The core network system34in this embodiment is mainly implemented by a group of servers arranged in the central data center10or the regional data center12.

The NOS30in this embodiment is configured, for example, on a cloud platform and includes a processor30a, a storage unit30b, and a communication unit30c, as illustrated inFIG.2. The processor30ais a program control device such as a microprocessor which operates in accordance with a program installed in the NOS30. The storage unit30bis, for example, a storage element such as a ROM or RAM, a solid state drive (SSD), a hard disk drive (HDD), or the like. The storage unit stores a program to be executed by the processor30a, and the like. The communication unit30cis, for example, a communication interface such as an NIC or a wireless LAN module. Software-defined networking (SDN) may be implemented by the communication unit30c. The communication unit30ctransmits and receives data to and from the RAN32and the core network system34.

In this embodiment, the NOS30is implemented by a group of servers arranged in the central data center10. The NOS30may be implemented by a group of servers arranged in the regional data center12.

The communication system1in this embodiment provides network services such as a voice communication service and a data communication service to users who use the UE20.

The network service provided in this embodiment is not limited to a voice communication service and a data communication service. The network service provided in this embodiment may be, for example, an IoT service.

In this embodiment, a container type application execution environment such as Docker is installed in the servers arranged in the central data center10, the regional data center12, and the edge data center14, and containers can be deployed in those servers and operated. In those servers, clusters (Kubernetes clusters) managed by a container management tool such as the Kubernetes may be constructed. After that, a container-type application may be executed by a processor on the constructed cluster.

Further, the network service provided in this embodiment is implemented by a containerized network function (CNF), which is a container-based functional unit.

As illustrated inFIG.3, the communication system1in this embodiment includes a plurality of base station systems40.

In this case, for example, when no components of the RAN32are incorporated in the central data center10or the regional data center12, the above-mentioned base station system40refers to a system corresponding to the RAN32constructed in one edge data center14.

Further, in some cases, a part of the components of the RAN32is incorporated in the central data center10or the regional data center12. For example, it is assumed that a component of the RAN32constructed in one edge data center14and a component of the RAN32constructed in the central data center10or the regional data center12cooperate with each other. In this case, the above-mentioned base station system40refers to a system corresponding to the RAN32including those components cooperating with each other.

In addition, as illustrated inFIG.3, in this embodiment, for example, the plurality of base station systems40included in the communication system1are classified into a plurality of base station system groups42.

Each of the base station system groups42includes a leader base station system40abeing a representative base station system40of this base station system group42, and a follower base station system40bbeing a base station system40other than the leader base station system40a.

Further, the NOS30predicts, for example, at intervals of a predetermined unit period t1(for example, 30 minutes), for the leader base station system40a, a degree of operation (for example, a traffic amount or the number of accommodated persons) in a next unit period t1in this leader base station system40a. The NOS30may predict the degree of operation, for example, at the intervals of the predetermined unit period t1based on the number of accommodated persons or the traffic amount in this unit period t1and a time associated with this unit period t1(for example, a start time or an end time of this unit period t1). In this case, the degree of operation may be predicted through use of a trained machine learning model.

In addition, in this embodiment, the operation control of all of the base station systems40included in the base station system group42is performed based on the degree of operation being the prediction result for the leader base station system40aincluded in this base station system group42.

Description is further given of functions of the NOS30in this embodiment and processes executed by the NOS30while focusing on classification of the base station systems40in this embodiment (grouping of the base station systems40) and operation control of the base station systems40.

FIG.4is a functional block diagram for illustrating an example of functions implemented by the NOS30in this embodiment. It is not required to implement all of the functions ofFIG.4by the NOS30in this embodiment. Further, functions other than the functions ofFIG.4may be implemented.

As illustrated inFIG.4, the NOS30in this embodiment functionally includes, for example, a monitoring module50, a transition data generation module52, a degree-of-correlation identification module54, a classification module56, a leader determination module58, a ratio identification module60, a prediction module62, an operation control module64, a removal evaluation module66, and a removal module68.

The monitoring module50is mainly implemented by the communication unit30c. The transition data generation module52is mainly implemented by the processor30aand the storage unit30b. The degree-of-correlation identification module54, the classification module56, the leader determination module58, the ratio identification module60, the prediction module62, the removal evaluation module66, and the removal module68are mainly implemented by the processor30a. The operation control module64is mainly implemented by the processor30a, the storage unit30b, and the communication unit30c.

The above-mentioned functions may be implemented by executing, by the NOS30, a program that is installed in the NOS30, which is a computer, and that includes instructions corresponding to the above-mentioned functions. Further, this program may be supplied to the NOS30via a computer-readable information storage medium such as an optical disc, a magnetic disk, a magnetic tape, a magneto-optical disc, or the like, or via the Internet or the like.

In this embodiment, the monitoring module50monitors, for example, the plurality of base station systems40included in the communication system1. Then, in this monitoring, the monitoring module50acquires, for example, from each of the plurality of base station systems40included in the communication system1, parameter data indicating values of parameters including various performance indices such as the number of accommodated persons and the traffic amount. In this embodiment, for example, the acquisition of the parameter data is repeatedly performed at predetermined time intervals.

In this case, for example, each of the base station systems40may successively transmit the parameter data to the NOS30. Then, the monitoring module50of the NOS30may receive the parameter data transmitted from each base station system40. As another example, the monitoring module50of the NOS30may successively access each of the base station systems40, and may collect the parameter data from this base station system40.

FIG.5is a diagram for illustrating an example of data structure of the parameter data acquired from the base station system40. As illustrated inFIG.5, the parameter data includes, for example, an ID, date-and-time data, number-of-accommodated-person data, and traffic amount data. The ID included in the parameter data is, for example, identification information on this base station system40. The date-and-time data is, for example, data indicating date and time when for example, the number of accommodated persons and the traffic amount in this base station system40are identified. The number-of-accommodated-person data is, for example, data indicating the number of accommodated persons in the area covered by this base station system40. The traffic amount data is, for example, data indicating the traffic amount in the area covered by this base station system40.

Then, the monitoring module50outputs, for example, the acquired parameter data to the transition data generation module52. In this manner, in this embodiment, for example, the parameter data is accumulated in the transition data generation module52.

In this embodiment, the transition data generation module52generates, for example, based on the collected parameter data, transition data indicating a transition of the degree of operation of each of the plurality of base station systems40(for example, a transition of the traffic amount or a transition of the number of accommodated persons). In this case, the transition data generation module52may generate, for each of the IDs different from each other, the transition data of the base station system40associated with this ID based on the parameter data including this ID and the date-and-time data of a predetermined time range. This transition data includes, for example, traffic amount transition data being time-series data indicating the transition of the traffic amount, and number-of-accommodated-person transition data being time-series data indicating the transition of the number of accommodated persons. In this manner, a plurality of pieces of transition data each associated with the base station system40are generated.

In this embodiment, the degree-of-correlation identification module54identifies, for example, based on the transition data, a degree of correlation of the transition of the degree of operation for each pair of base station systems40.

In this case, the degree-of-correlation identification module54may calculate, for example, based on the transition data, a cross-correlation coefficient Rxy(τ) of a value indicating the degree of operation of the base station system40. The degree-of-correlation identification module54may calculate, for example, the cross-correlation coefficient Rxy(τ) of the value of the above-mentioned traffic amount transition data. Further, the degree-of-correlation identification module54may calculate, for example, the cross-correlation coefficient Rxy(τ) of the value of the above-mentioned number-of-accommodated-person transition data. The cross-correlation coefficient Rxy(τ) is calculated for each of a plurality of lags “τ”.

FIG.6is a graph for schematically showing an example of the cross-correlation coefficient Rxy(τ) calculated for a certain pair of base station systems40.

Further, the degree-of-correlation identification module54may identify a representative value of the cross-correlation coefficient of the value indicating the degree of operation as the degree of correlation of the transition of the degree of operation. Further, the degree-of-correlation identification module54may identify as well a value “t” of the lag “τ” at the time when this representative value is taken.

In the following description, it is assumed that the degree-of-correlation identification module54identifies the maximum value Rmax of the cross-correlation coefficient and the value “t” of the lag “τ” at the time when the value of the cross-correlation coefficient is the maximum value Rmax.

Then, the degree-of-correlation identification module54generates degree-of-correlation data indicating a combination between the above-mentioned value Rmax and the above-mentioned value “t”, which are calculated for each pair of base station systems40.FIG.7is a table for schematically showing an example of the degree-of-correlation data.FIG.7shows, as an example, pieces of degree-of-correlation data associated with pairs of base station systems40for six base station systems40having IDs of from 001 to 006. In the example ofFIG.7, the combination of the above-mentioned value Rmax and the above-mentioned value “t” is expressed by (Rmax, t). In the example ofFIG.7, the unit of lag “τ” is, for example, “minute.”

In this embodiment, the classification module56classifies, for example, based on the degree of correlation identified for each pair of base station systems40, the plurality of base station systems40included in the communication system1into the plurality of base station system groups42. In this case, the classification module56may classify the plurality of base station systems40into the plurality of base station system groups42based on the representative value of the cross-correlation coefficient and the lag at the time when the cross-correlation coefficient takes the maximum value.

In this embodiment, for example, initial values for a threshold value th1of Rmax and a range of from τ1to τ2of the value “t” are predetermined. Then, the classification module56identifies, for each base station system40, the number of pieces of degree-of-correlation data satisfying a predetermined condition from among the pieces of degree-of-correlation data relating to combinations with other base station systems40. In this case, for example, the number of pieces of degree-of-correlation data satisfying a condition that “the value ‘t’ is within the range of from τ1to τ2, and the value Rmax is equal to or larger than the threshold value th1” is identified.

Then, the classification module56identifies the base station system40having the largest number of pieces of degree-of-correlation data satisfying the above-mentioned condition as a maximum-number base station system.

Then, the classification module56groups the maximum-number base station system identified as described above and one or a plurality of base station systems40satisfying the above-mentioned condition in relation to this maximum-number base station system as the base station systems40included in one base station system group42.

For example, it is assumed that the threshold value th1is 0.7, and the range of from τ1to τ2is from −30 to 30.

In this case, in the example ofFIG.7, in the base station system40having the ID of 001, a combination with the base station system40having the ID of 006 satisfies the above-mentioned condition.

In the base station system40having the ID of 002, a combination with the base station system40having the ID of 004 satisfies the above-mentioned condition.

In the base station system40having the ID of 003, a combination with the base station system40having the ID of 004 satisfies the above-mentioned condition.

In the base station system40having the ID of 004, a combination with the base station system40having the ID of any one of 002, 003, and 005 satisfies the above-mentioned condition.

In the base station system40having the ID of 005, a combination with the base station system40having the ID of 004 satisfies the above-mentioned condition.

In the base station system40having the ID of 006, a combination with the base station system40having the ID of 001 satisfies the above-mentioned condition.

Thus, in this case, the base station system40having the ID of 004 is identified as the maximum-number base station system. Then, the base station system40having the ID of 002, the base station system40having the ID of 003, the base station system40having the ID of 004, and the base station system40having the ID of 005 are grouped as the base station systems40included in one base station system group42.

In the following, a process relating to the identification of the maximum-number base station system and the grouping of the base station systems40is referred to as “classification process.”

Further, the classification module56executes the above-mentioned classification process for the remaining base station systems40. For example, the classification module56identifies, from among the remaining base station systems40, the base station system40having the largest number of pieces of degree-of-correlation data satisfying the above-mentioned condition as the maximum-number base station system. Then, the classification module56classifies one or a plurality of base station systems40satisfying the above-mentioned condition in relation to this maximum-number base station system as the base station systems40included in one new base station system group42.

In this manner, the above-mentioned classification process for the base station systems40that have not been included in the base station system group42is repeatedly executed so that the grouping of the base station systems40is performed.

In this embodiment, in the above-mentioned classification process, there is a possibility that a base station system40that is not present in any of the base station system groups42is present. In such a case, for example, the above-mentioned threshold value th1may be changed to a smaller value or the above-mentioned range of from τ1to τ2may be widened, and then the above-mentioned classification process may be executed.

In this embodiment, the leader determination module58determines, for example, for each base station system group42, the leader base station system40afrom among the plurality of base station systems40included in this base station system group42. In this case, the leader determination module58may determine the leader base station system40abased on the number of other base station systems40in each of which the degree of correlation with respect to this base station system40is larger than a predetermined magnitude.

The leader determination module58may determine, for example, the base station system40identified as the maximum-number base station system in the above-mentioned classification process as the leader base station system40ain the base station system group42including this base station system40. Then, the leader determination module58may determine each of the remaining base station systems40as the follower base station system40bin the base station system group42including this leader base station system40a.

In the example ofFIG.7, the base station system40having the ID of 004 is determined as the leader base station system40ain this base station system group42. Then, the base station system40having the ID of 002, the base station system40having the ID of 003, and the base station system40having the ID of 005 are determined as the follower base station systems40bin this base station system group42.

In the above-mentioned classification process, instead of using the maximum value Rmax of the cross-correlation coefficient, other representative values (for example, an average value, the minimum value, or the mean square of the cross-correlation coefficient) may be used. Further, a threshold value th1suitable for the above-mentioned representative value may be used.

Further, the leader determination module58is not required to determine the leader base station system40abased on the number of other base station systems40in each of which the degree of correlation with respect to this base station system40is larger than a predetermined magnitude. For example, the leader base station system40amay randomly be determined from among the plurality of base station systems40included in the base station system group42. Further, for example, the base station system40geographically closest to a center among the plurality of base station systems40included in the base station system group42may be determined as the leader base station system40ain this base station system group42.

In this embodiment, the ratio identification module60identifies, for example, a ratio of the degree of operation of the follower base station system40bincluded in this base station system group42with respect to the degree of operation of the leader base station system40aincluded in the base station system group42. In this case, when the base station system group42includes a plurality of follower base station systems40b, this ratio is identified for each of the follower base station systems40b.

For example, the ratio identification module60may identify a ratio of the traffic amount. For example, the ratio identification module60may identify a ratio of a representative value of the value of the traffic amount transition data included in the above-mentioned transition data. Further, the ratio identification module60may identify a ratio of the number of accommodated persons. For example, the ratio identification module60may identify a ratio of a representative value of the value of the number-of-accommodated-person transition data included in the above-mentioned transition data. In this case, examples of the representative value used when the ratio is identified include the maximum value, the average value, the minimum value, and the mean square.

In this embodiment, the prediction module62predicts, for example, the degree of operation of at least one software element included in the communication system1. The prediction module62predicts, for example, for each of the plurality of base station system groups42, the traffic amount or the number of accommodated persons in the area covered by the leader base station system40aincluded in this base station system group42.

In this case, for example, after the plurality of base station system groups42included in the communication system1are identified as described above, the monitoring module50may monitor only the leader base station system40a, and acquire the parameter data only from the leader base station system40a. Then, the prediction module62may predict the degree of operation of the leader base station system40a. In this manner, a monitoring load of the communication system1is reduced.

Then, as described above, the prediction module62may predict, at intervals of the predetermined unit period t1(for example, 30 minutes), for the leader base station system40a, the degree of operation in the next unit period t1in this leader base station system40a. In this case, for example, the traffic amount may be predicted. Further, the number of accommodated persons may be predicted.

In this embodiment, the operation control module64executes, for example, the operation control of the leader base station system40abased on the degree of operation of the leader base station system40abeing the prediction result obtained by the prediction module62.

Then, in this embodiment, the operation control module64executes, for example, for each of the plurality of base station system groups42, the operation control corresponding to the degree of operation of the leader base station system40aincluded in this base station system group42, with respect to the follower base station system40bincluded in this base station system group42. For example, the operation control module64executes the operation control of the follower base station system40bbased on the degree of operation of the leader base station system40abeing the prediction result obtained by the prediction module62and on the ratio identified by the ratio identification module60. In this case, this ratio refers to, as described above, the ratio of the degree of operation of this follower base station system40bwith respect to the degree of operation of this leader base station system40a.

It is assumed that, for example, a traffic amount per unit period of the leader base station system40aincluded in a certain base station system group42, which is predicted by the prediction module62, is T1. Further, it is assumed that a ratio of the degree of operation of a certain follower base station system40bwhich is included in this base station system group42with respect to the degree of operation of this leader base station system40a, which is identified by the ratio identification module60, is “p”.

In this case, the operation control module64executes, for this leader base station system40a, operation control using T1as an input value (manipulated variable). Then, the operation control module64executes, for this follower base station system operation control using T1×p as the input value (manipulated variable).

The operation control module64may transmit a control signal relating to the operation control to the base station system40being a target of this operation control. Then, the base station system40that has received this control signal may execute the operation control corresponding to this control signal.

Further, the operation control module64may execute, for each base station system40, power consumption control of this base station system40. In this case, for example, an optimal processor frequency (for example, CPU frequency) in this base station system40may be determined so that power saving (operation in a power saving state) suitable for the above-mentioned input value is executed. Then, the operation control module64may control, for each base station system40, the CPU frequency of a CPU operating in this base station system40so that the determined frequency is achieved. In this case, data indicating a correspondence between the input value and the CPU frequency may be stored in the operation control module64. Then, the operation control module64may execute, for the CPU included in the leader base station system40a, the operation control of operating the CPU at the CPU frequency associated with the above-mentioned value T1. Further, the operation control module64may execute, for the CPU included in the follower base station system40b, the operation control of operating the CPU at the CPU frequency associated with the above-mentioned value T1×p.

In this case, for example, data in which a range of the input value and a power state (for example, a P-state) of the processor are associated with each other in advance may be stored in the operation control module64. Further, the operation control module64may control the processor included in the leader base station system40aso that the processor operates in a P-state associated with the above-mentioned value T1in this data. Further, the operation control module64may control the processor included in the follower base station system40bso that the processor operates in a P-state associated with the above-mentioned value T1×p in this data.

Description is further given of the power consumption control of the processor executed by the operation control module64.

FIG.8is a diagram for illustrating an example of a configuration of the operation control module64. As illustrated inFIG.8, the operation control module64includes a correspondence data storage module70, a power state identification module72, and a power consumption control module74. The correspondence data storage module70is mainly implemented by the storage unit30b. The power state identification module72is mainly implemented by the processor30a. The power consumption control module74is mainly implemented by the processor30aand the communication unit30c.

In this embodiment, the correspondence data storage module70stores, for example, for each of a plurality of power states into which the processor may be brought, correspondence data indicating a correspondence between a performance index value and the degree of operation relating to at least one software element included in the communication system1. In the following description, it is assumed that this power state is a P-state.

FIG.9is a diagram for illustrating an example of the correspondence data. As illustrated inFIG.9, the correspondence data includes, for example, degree-of-operation data indicating the degree of operation, and performance index value data indicating a performance index value associated with this degree of operation.

In the example ofFIG.9, the degree-of-operation data included in the correspondence data includes the traffic amount data indicating the traffic amount. Further, the performance index value data included in this correspondence data includes a plurality of combinations of P-state data, average processing time data, and average packet discard rate data. In the P-state data included in a certain combination, a P-state is indicated. In addition, in the average processing time data included in this combination, an average processing time associated with this P-state in this traffic amount is indicated. In the average packet discard rate data included in this combination, an average packet discard rate associated with this P-state in this traffic amount is indicated.

In this embodiment, for each software element such as a functional unit (NF) included in the base station system40, for example, a DU, a CU, or the like, a load test or simulation is performed in advance in a simulated environment simulating this software element. Then, for each of the plurality of P-states, under a state in which the processor in the simulated environment is set to this P-state, a relationship between a load (traffic amount per unit period) with respect to the software element and the average processing time or the average packet discard rate is identified.

For example, the average processing time or the average packet discard rate may be measured in advance while changing the size of data input per unit period to the simulated environment. For example, the average processing time or the average packet discard rate may be measured for packets having a given data size while changing the number of packets input per unit period.

Then, the correspondence data illustrated inFIG.9is generated based on results of the load test or the simulation in the simulated environment as described above.

In this embodiment, the above-mentioned average processing time refers to, for example, an average value of times from when the simulated environment of the software element receives packets having a given data size to when the execution of the process in this software element is ended. Further, the above-mentioned average packet discard rate refers to, for example, a rate of the number of discarded packets with respect to the number of packets received by the simulated environment of the software element.

Further, the correspondence data may be created based on, for example, a heuristic in an actual environment instead of the results of the load test or the simulation.

In this embodiment, for example, a plurality of pieces of correspondence data associated with traffic amounts different from each other, which are associated with types of the software element, are stored in the correspondence data storage module70. In the following, the plurality of pieces of correspondence data associated with the traffic amounts different from each other, which are associated with the types of the software element, are referred to as “correspondence data set.”

In this embodiment, the power state identification module72identifies, for example, based on the correspondence data, any of the power states for reaching a given target relating to the performance index value in the degree of operation being the prediction result obtained by the prediction module62. In this case, for example, the power state identification module72may identify a power state having the lowest power consumption among the power states for reaching the given target relating to the performance index value in the degree of operation being the prediction result obtained by the prediction module62. In the following, the power state identified as described above is referred to as “target power state.”

In this case, for example, the power state identification module72may calculate, based on the traffic amount T1per unit period predicted for the leader base station system40a, a traffic amount T2per unit period per one DU included in this leader base station system40a. For example, when the number of DUs included in the leader base station system40ais three, a value T1/3may be calculated as the value T2.

Then, the power state identification module72may identify the correspondence data including the traffic amount data having the value of T2from among the plurality of pieces of correspondence data included in the correspondence data set associated with the DU. Then, the power state identification module72may identify, based on the identified correspondence data, P-states in which the value of the corresponding average processing time data is equal to or smaller than a given target value. Then, the power state identification module72may identify a P-state having the lowest power consumption among those P-states as a target P-state.

As another example, the power state identification module72may identify, based on the identified correspondence data, P-states in which a value of the corresponding average packet discard rate data is equal to or smaller than a given target value. Then, the power state identification module72may identify a P-state having the lowest power consumption among those P-states as a target P-state.

The power consumption becomes larger as the P-state becomes higher. For example, a state in which the P-state is P6has the highest power consumption, and thereafter the power consumption is decreased in order of P5, P4, P3, P2, and P1.

FIG.10is a graph for schematically showing an example of a relationship between the traffic amount per unit period (for example, 30 minutes) and the average processing time for each of the plurality of P-states (P1to P6).FIG.11is a graph for schematically showing an example of a relationship between the traffic amount per unit period (for example, 30 minutes) and the average packet discard rate for each of the plurality of P-states (P1to P6).

As shown inFIG.10, when the traffic amount per unit period is fixed, the average processing time becomes shorter as the P-state becomes higher. Further, as shown inFIG.11, when the traffic amount per unit period is fixed, the average packet discard rate becomes lower as the P-state becomes higher.

For example, it is assumed that, based on the correspondence data, as shown inFIG.10, in the traffic amount T2per unit period, P4to P6are identified as the P-states in which the value of the average processing time data is equal to or smaller than a given target value “c”. In this case, from among those P-states, P4being the P-state having the lowest power consumption may be identified as the target P-state.

Further, it is assumed that, based on the correspondence data, as shown inFIG.11, in the traffic amount T2per unit period, P3to P6are identified as the P-states in which the value of the average packet discard rate data is equal to or smaller than a given target value “d”. In this case, from among those P-states, P3being the P-state having the lowest power consumption may be identified as the target P-state.

In this embodiment, the power consumption control module74operates, for example, a processor for executing at least one software element in the identified power state. For example, in the above-mentioned case, the processor for executing the software element of the DU included in this leader base station system40amay be operated in the target P-state identified as described above.

Further, in this embodiment, the power state identification module72may identify, based on the correspondence data, the power state having the lowest power consumption as a first state among the power states for reaching the given target relating to the average processing time in the degree of operation being the prediction result.

Then, the power state identification module72may identify, based on the correspondence data, the power state having the lowest power consumption as a second state among the power states for reaching the given target relating to the average packet discard rate in the degree of operation being the prediction result.

In the above-mentioned case, P4corresponds to the first state, and P3corresponds to the second state. In this case, the target relating to the average processing time cannot be reached in P3being the second state, but both of the target relating to the average processing time and the target relating to the average packet discard rate can be reached in P4being the first state.

In view of the above, the power consumption control module74may operate the processor for executing the at least one software element in one of the first state or the second state being the power state having higher power consumption. For example, in the above-mentioned case, the processor for executing the software element of the DU included in the leader base station system40amay be operated in P4being the first state. In this manner, when there are a plurality of targets, all of those targets can be reached.

When a priority is given to suppression of power consumption than reaching of the target, the power consumption control module74may operate the processor for executing the at least one software element in one of the first state or the second state being the power state having lower power consumption.

Further, the above-mentioned power consumption control is also applicable to the follower base station system40b.

For example, the power state identification module72may calculate, based on the traffic amount T1per unit period predicted for the leader base station system40a, and on the above-mentioned ratio “p” relating to this follower base station system40b, a traffic amount T3per unit period per one DU. For example, when the number of DUs included in this follower base station system40bis three, a value T1×p/3 may be calculated as the value T3.

Then, the processor for executing the software element of the DU included in this follower base station system40bmay be operated in the target P-state identified based on the value T3as described above.

Further, the power state identification module72may identify, based on the correspondence data, for each P-state, a range of the traffic amount per unit period associated with this P-state. Then, the power state identification module72may identify a P-state associated with the range in which the traffic amount T2per unit period is included as the target P-state.

Further, as the above-mentioned degree of operation, instead of using the traffic amount in the area covered by the at least one software element per unit period, the number of accommodated persons in the area covered by the at least one software element may be used.

Further, the above-mentioned power consumption control is also applicable to an NF (CU or the like) other than the DU. Further, the above-mentioned power consumption control is also applicable to a software element included in the core network system34, for example, a UPF or the like, without being limited to the base station system40.

Further, the correspondence data is not required to be associated with the type of the software element, and may be associated with the entire base station system40. Then, the processor for executing the software element included in the base station system40may be operated in the power state determined based on the degree of operation predicted for this base station system40and the correspondence data associated with this base station system40.

Further, the operation control in this embodiment is not limited to the power consumption control. For example, in this embodiment, control of a capacity such as the number of accommodated persons allocated to a network slice and control of a resource amount allocated to a network slice may be performed.

Further, in this embodiment, even after the plurality of base station system groups42included in the communication system1are identified, the monitoring of the follower base station system40bmay be performed.

In this embodiment, the removal evaluation module66evaluates, for example, a magnitude of a deviation of a ratio of the degree of operation of the follower base station system40bincluded in the base station system group42with respect to the degree of operation of the leader base station system40aincluded in this base station system group42from a given reference ratio.

In this embodiment, the removal module68removes, for example, in accordance with the fact that the above-mentioned magnitude of the deviation has satisfied a predetermined condition, the follower base station system40bsatisfying the condition from the base station system group42including this follower base station system40b.

In this case, the removal evaluation module66may identify, for each of the follower base station systems40b, a removal evaluation value being a value associated with the above-mentioned magnitude of the deviation. Then, the removal module68may remove the follower base station system40bin which the identified removal evaluation value satisfies a predetermined condition from the base station system group42including this follower base station system40b.

In this embodiment, for example, every time a predetermined removal determination timing (for example, a timing of once in several months) is reached, the monitoring module50may acquire pieces of parameter data from all of the base station systems40over a predetermined time period. In this case, a monitoring interval for the leader base station system40abeing a target of prediction of the degree of operation performed by the prediction module62and a monitoring interval for all of the base station systems40at the removal determination timing may be different from each other. For example, the parameter data may be collected at intervals of 100 milliseconds for the leader base station system40a, and the parameter data may be collected at intervals of 3 seconds for the follower base station system40b.

In addition, the removal evaluation module66may identify, for each of a plurality of dates and times, the degree of operation of each base station system40at this date and time.

FIG.12is a table for schematically showing an example of degrees of operation identified for a certain base station system group42including six base station systems40. In the example ofFIG.12, the IDs of those six base station systems40are 101, 102, 103, 104, 105, and 106, respectively. Further, it is assumed that the base station system40having the ID of 101 is the leader base station system40ain this base station system group42.

In this case, it is assumed that the ratios of the degrees of operation of the follower base station systems40bhaving the IDs of 102, 103, 104, 105, and 106 with respect to the degree of operation of the leader base station system40ahaving the ID of 101, which are identified by the ratio identification module60, are p12, p13, p14, p15, and p16, respectively. In the following description, each of those ratios is referred to as “reference ratio.”

In addition,FIG.12shows, for each of three dates and times (0:00:00, 0:00:03, and 0:00:06), the degree of operation (for example, the value of the number-of-accommodated-person data or the value of the traffic amount data) indicated by the parameter data including the date-and-time data indicating this date and time.

For example, it is assumed that the degrees of operation of the base station systems40having the IDs of 101, 102, 103, 104, 105, and 106 at the date and time “0:00:00” are T11, T12, T13, T14, T15, and T16, respectively.

In addition, in this embodiment, the removal evaluation module66may calculate, for the follower base station system a normalized degree of operation being a value obtained by dividing the degree of operation by the above-mentioned reference ratio. In the example ofFIG.12, the normalized degrees of operation of the base station systems40having the IDs of 102, 103, 104, 105, and 106 at the date and time “0:00:00” are represented by T12′, T13′, T14′, T15′, and T16′, respectively. As shown inFIG.12, T12′, T13′, T14′, T15′, and T16′ are a value obtained by dividing T12by p12, a value obtained by dividing T13by p13, a value obtained by dividing T14by p14, a value obtained by dividing T15by p15, and a value obtained by dividing T16by p16, respectively.

Further, it is assumed that the degrees of operation of the base station systems40having the IDs of 101, 102, 103, 104, 105, and 106 at the date and time “0:00:03” are T21, T22, T23, T24, T25, and T26, respectively. In this case, a value obtained by dividing T22by p12may be identified as a normalized degree of operation T22′ of the base station system40having the ID of 102. Further, a value obtained by dividing T23by p13may be identified as a normalized degree of operation T23′ of the base station system40having the ID of 103. Further, a value obtained by dividing T24by p14may be identified as a normalized degree of operation T24′ of the base station system40having the ID of 104. Further, a value obtained by dividing T25by p15may be identified as a normalized degree of operation T25′ of the base station system40having the ID of 105. Further, a value obtained by dividing T26by p16may be identified as a normalized degree of operation T26′ of the base station system having the ID of 106.

Further, it is assumed that the degrees of operation of the base station systems40having the IDs of 101, 102, 103, 104, 105, and 106 at the date and time “0:00:06” are T31, T32, T33, T34, T35, and T36, respectively. In this case, a value obtained by dividing T32by p12may be identified as a normalized degree of operation T32′ of the base station system40having the ID of 102. Further, a value obtained by dividing T33by p13may be identified as a normalized degree of operation T33′ of the base station system40having the ID of 103. Further, a value obtained by dividing T34by p14may be identified as a normalized degree of operation T34′ of the base station system40having the ID of 104. Further, a value obtained by dividing T35by p15may be identified as a normalized degree of operation T35′ of the base station system40having the ID of 105. Further, a value obtained by dividing T36by p16may be identified as a normalized degree of operation T36′ of the base station system having the ID of 106.

In addition, in this embodiment, also for the dates and times after the date and time “0:00:06,” the degree of operation is similarly identified for each of the base station systems40.

In addition, the removal evaluation module66identifies, for each of the plurality of dates and times, the removal evaluation value for each follower base station system40b.

In this case, for example, a difference D between the normalized degree of operation of the follower base station system40band the degree of operation of the leader base station system40amay be identified. A magnitude of the difference D identified in this manner is associated with the magnitude of the deviation of the ratio of the degree of operation of this follower base station system40bwith respect to the degree of operation of the leader base station system40afrom the given reference ratio. In the example ofFIG.12, for the date and time “0:00:00,” values of T12′-T11, T13′-T11, T14′-T11, T15′-T11, and T16′-T11may be identified. In the following, the values of T12′-T11,113′-T11, T14′-T11, T15′-T11, and T16′-T11are expressed as D12, D13, D14, D15, and D16, respectively.

Further, similarly, for the date and time “0:00:03,” values of T22′-T21, T23′-T21, T24′-T21, T25′-T21, and T26′-T21may be identified. In the following, the values of T22′-T21, T23′-T21, T24′-T21, T25′-T21, and T26′-T21are expressed as D22, D23, D24, D25, and D26, respectively.

Further, similarly, for the date and time “0:00:06,” values of T32′-T31, T33′-T31, T34′-T31, T35′-T31, and T36′-T31may be identified. In the following, the values of T32′-T31, T33′-T31, T34′-T31, T35′-T31, and T36′-T31are expressed as D32, D33, D34, D35, and D36, respectively.

Also for the dates and times after the date and time “0:00:06,” the difference D between the normalized degree of operation of the follower base station system40band the degree of operation of the leader base station system40ais similarly identified.

In addition, in accordance with the fact that the magnitude of the difference D between a value indicating the degree of operation of the leader base station system40aincluded in the base station system group42and a value obtained by dividing a value indicating the degree of operation of the follower base station system40bincluded in this base station system group42by the reference ratio has satisfied a predetermined condition, the removal module68may remove this follower base station system40bfrom this base station system group42.

In this case, for example, the removal evaluation module66may calculate, for each follower base station system40b, the removal evaluation value for this follower base station system40bbased on the value of the difference D identified for each of the plurality of dates and times as described above. Then, the removal module68may remove the follower base station system40bin which the calculated removal evaluation value satisfies a predetermined condition from the base station system group42including this follower base station system40b.

For example, the removal evaluation module66may calculate an average of the differences D identified a plurality of times for the follower base station system40bas the removal evaluation value of this follower base station system40b. For example, for the follower base station system40b, an average of the differences D identified for the above-mentioned plurality of dates and times may be calculated as the removal evaluation value of this follower base station system40b. Then, the removal module68may remove the follower base station system40bin which the calculated average satisfies a predetermined condition from the base station system group42including this follower base station system40b. For example, when an absolute value of the calculated average or a square of the average value is larger than a predetermined value, this follower base station system40bmay be removed from the base station system group42including this follower base station system40b.

Further, the removal evaluation module66may calculate a variance or a standard deviation of the differences D identified a plurality of times for the follower base station system40bas the removal evaluation value of this follower base station system40b. For example, for the follower base station system40b, a variance or a standard deviation of the differences D identified for the above-mentioned plurality of dates and times may be calculated as the removal evaluation value of this follower base station system40b. Then, the removal module68may remove the follower base station system40bin which the magnitude of the calculated variance or standard deviation satisfies a predetermined condition (for example, is larger than a predetermined value) from the base station system group42including this follower base station system40b.

Further, the follower base station system40bsatisfying at least one of a condition that the absolute value of the average value or the square of the average value is larger than the predetermined value or a condition that the variance or the standard deviation is larger than the predetermined value may be removed from the base station system group42including this follower base station system40b.

Further, the removal evaluation module66may generate statistical data obtained by aggregating values of the differences D identified for a plurality of dates and times for all of the follower base station systems40b. In the following, data corresponding to the value of the difference D, which is included in the statistical data generated as described above, is referred to as “sample.” Then, the removal evaluation module66may identify the standard deviation of the samples aggregated for all of the follower base station systems40bas described above. Then, the removal evaluation module66may identify, for each of the follower base station systems40b, the number of samples each having a larger absolute value than a predetermined multiple (for example, a double or a triple) of the identified standard deviation as the removal evaluation value of this follower base station system40b.

As another example, the removal evaluation module66may identify the number of samples not being included in a predetermined confidence interval (for example, a 95% confidence interval or a 99% confidence interval) as the removal evaluation value of this follower base station system40b.

Then, the removal module68may remove the follower base station system40bin which the identified number of samples is larger than a predetermined number from the base station system group42including this follower base station system40b.

As another example, the removal module68may remove the follower base station system40bin which a proportion of the identified number of samples with respect to the total number of samples for this follower base station system40bis larger than a predetermined proportion from the base station system group42including this follower base station system40b.

Further, the removal module68may remove the follower base station system40bsatisfying a condition that a distribution of the differences D identified a plurality of times is different from those of other follower base station systems40bfrom this base station system group42. For example, the removal module68may remove the follower base station system40bhaving the largest number of samples each identified as the above-mentioned removal evaluation value from the base station system group42including this follower base station system40b.

Further, the removal evaluation module66may generate, as the above-mentioned statistical data, a histogram in which a section is set for each range of the value of the difference D determined in advance, and which indicates the number (frequency value) of differences D of each follower base station system40bincluded in this section. Then, the removal evaluation module66may identify the removal evaluation value described above based on the generated histogram. For example, the removal evaluation module66may identify, for each follower base station system40b, a ratio of the number of samples included in each of the sections outside of the confidence interval in the generated histogram as the removal evaluation value. Then, the removal module68may remove the follower base station system40bin which the identified ratio is significantly different from those of other follower base station systems40bfrom the base station system group42including this follower base station system40b.

Further, the removal evaluation module66may determine, for each of the plurality of dates and times, whether or not the value of the above-mentioned difference D of each follower base station system40bsatisfies a predetermined condition. For example, it may be determined whether or not a condition that the absolute value of the above-mentioned difference D or the square of the value of the above-mentioned difference D is larger than a predetermined value is satisfied. Then, the removal module68may remove the follower base station system40bsatisfying a condition that, in the determination related to the plurality of dates and times, the number of times of satisfying this condition is equal to or larger than a predetermined number of times from the base station system group42including this follower base station system40b.

Further, in this embodiment, a difference between a value obtained by multiplying a value indicating the degree of operation of the leader base station system40aincluded in the base station system group42by the reference ratio and a value indicating the degree of operation of the follower base station system40bincluded in this base station system group42may be used as the above-mentioned difference D. In addition, the removal module68may remove this follower base station system40bfrom the base station system group42including this follower base station system40bin accordance with the fact that the magnitude of the difference D has satisfied the predetermined condition. The magnitude of the difference D identified in this manner is associated with the magnitude of the deviation of the ratio of the degree of operation of this follower base station system40bwith respect to the degree of operation of the leader base station system40afrom the given reference ratio.

In the following, the base station system40removed from the base station system group42as described above is referred to as “removed base station system.”

For example, the removed base station system may be set so as not to be included in any of the base station system groups42. In addition, for this removed base station system, operation control similar to that for the leader base station system40adescribed above may be executed.

Further, for example, the removed base station system may be included in another base station system group42. For example, the monitoring module50may acquire parameter data over a predetermined time period for the removed base station system. Then, the transition data generation module52may generate transition data of the removed base station system based on the parameter data acquired as described above.

Then, the degree-of-correlation identification module54may identify, based on the transition data, the degree of correlation of the transition of the degree of operation between the leader base station system40aof each base station system group42and the removed base station system. Then, this removed base station system may be included as the follower base station system40bin the base station system group42including the leader base station system40ahaving the largest degree of correlation.

As another example, in accordance with the determination on the removal of the base station system40from the base station system group42, the classification module56may reclassify all of the base station systems40included in the communication system1.

As used herein, “reclassification” refers to, for example, as described above, a process of identifying, by the degree-of-correlation identification module54, the degree of correlation of the transition of the degree of operation between a pair of base station systems40again, a process of classifying, based on the identified degree of correlation, the plurality of base station systems40included in the communication system1into the plurality of base station system groups42again, and the like.

In this embodiment, in some cases, a tendency of a transition of the degree of operation of a specific follower base station system40bchanges for some reasons such as an increase of the number of subscribers in a specific area, for example.

Such a follower base station system40bis brought into a state in which the operation control corresponding to the degree of operation of the leader base station system40acannot be accurately performed. Thus, this follower base station system should no longer be included in this base station system group42including this follower base station system40b.

In view of the above, in this embodiment, as described above, in accordance with the fact that the magnitude of the deviation of the ratio of the degree of operation of the follower base station system40bwith respect to the degree of operation of the leader base station system40afrom the given reference ratio has satisfied the predetermined condition, this follower base station system40bis removed from the base station system group42including this follower base station system40b.

In this manner, according to this embodiment, the follower base station system40bwhich should not be included in the base station system group42can be accurately removed from this base station system group42.

Description is now given of an example of a flow of a process relating to grouping of the base station systems40executed by the NOS30in this embodiment, with reference to the flow chart exemplified inFIG.13.

In this process example, it is assumed that, with the monitoring performed by the monitoring module50, the parameter data is acquired for the plurality of base station systems40included in the communication system1, and the parameter data is accumulated in the transition data generation module52.

Further, in this process example, the base station system40classified into any of the base station system groups42is referred to as “classified base station system,” and the base station system40not classified into any of the base station system groups42is referred to as “unclassified base station system.” In the initial state, all of the base station systems40are unclassified base station systems.

First, the transition data generation module52generates transition data for a predetermined time range, for each of the plurality of unclassified base station systems (Step S101).

Then, the degree-of-correlation identification module54generates, based on the transition data generated in the process step of Step S101, for each pair of base station systems40, degree-of-correlation data associated with this pair (Step S102).

Then, the classification module56identifies, for each of the plurality of unclassified base station systems, the number of pieces of degree-of-correlation data satisfying the above-mentioned predetermined condition among the pieces of degree-of-correlation data relating to combinations with other unclassified base station systems (Step S103).

Then, the classification module56identifies the unclassified base station system having the largest number identified in the process step of Step S103as the maximum-number base station system (Step S104).

Then, the classification module56allocates, to a new base station system group42, the maximum-number base station system identified in the process step of Step S104and one or a plurality of unclassified base station systems satisfying the above-mentioned predetermined condition in relation to this maximum-number base station system (Step S105). The unclassified base station systems allocated to the new base station system group42as described above become classified base station systems.

Then, the leader determination module58determines the maximum-number base station system identified in the process step of Step S104as the leader base station system40ain the base station system group42to which the unclassified base station systems have been allocated in the process step of Step S105(Step S106).

Then, the leader determination module58determines the remaining base station systems40allocated to the base station system group42in the process step of Step S105as the follower base station systems40bin this base station system group42(Step S107).

Then, the ratio identification module60identifies, for each of the follower base station systems40bdetermined in the process step of Step S107, the ratio “p” of the degree of operation of this follower base station system40bwith respect to the degree of operation of the leader base station system40adetermined in the process step of Step S106(Step S108).

Then, the classification module56confirms whether or not all of the base station systems40have become the classified base station systems (Step S109).

When all of the base station systems40have not become the classified base station systems (Step S109: N), the process returns to the process step of Step S103. At this time, the process step of Step S103may be executed after the predetermined condition in the process step of Step S103is changed.

When it is confirmed that all of the base station systems have become the classified base station systems in the process step of Step S109(Step S109: Y), the process illustrated in this process example is ended.

Next, description is given of an example of a flow of a process relating to the operation control of the base station system40executed by the NOS30in this embodiment, with reference to the flow chart exemplified inFIG.14. In this process example, it is assumed that the correspondence data is stored in advance in the correspondence data storage module70.

For example, the process steps of from Step S201to Step S205are independently executed for each of the plurality of base station system groups42.

First, the prediction module62waits until a prediction timing that occurs at the intervals of the predetermined unit period t1is reached (Step S201).

When the prediction timing is reached, the prediction module62predicts, based on the parameter data of the leader base station system40aof this base station system group42, which has been acquired in the latest unit period t1, the traffic amount T1of this leader base station system40ain the next unit period t1(Step S202).

Then, the power state identification module72identifies the target P-state of this leader base station system40a(Step S203). In the process step of Step S203, for example, the target P-state of this leader base station system40ais identified based on the traffic amount T1identified in the process step of Step S202.

Then, the power state identification module72identifies the target P-state for each of the one or the plurality of follower base station systems40bincluded in this base station system group42(Step S204). In the process step of Step S204, for example, for each of the one or the plurality of follower base station systems40b, the target P-state of this follower base station system40bis identified based on the traffic amount T1identified in the process step of Step S202and the ratio “p” identified in the process step of Step S108for this follower base station system40b.

Then, the power consumption control module74executes the operation control for each of the base station systems40included in this base station system group42(Step S205), and the process returns to the process step of Step S201. In the process step of Step S205, for example, the leader base station system40ais controlled so that the CPU of the server included in this leader base station system40ais operated in the target P-state identified in the process step of Step S203. Further, the follower base station system40bis controlled so that the CPU of the server included in this follower base station system40bis operated in the target P-state identified for this follower base station system40bin the process step of Step S204.

Next, description is given of an example of a flow of a process relating to removal of the base station systems40executed by the NOS30in this embodiment, with reference to the flow chart exemplified inFIG.15.

For example, the process steps of from Step S301to Step S308are independently executed for each of the plurality of base station system groups42.

In this process example, the removal evaluation module66monitors whether the predetermined removal determination timing (for example, a timing of once in several months) is reached (Step S301).

When the removal determination timing is reached, the monitoring module50acquires pieces of parameter data from all of the base station systems40included in this base station system group42over a predetermined time period (Step S302).

Then, the removal evaluation module66calculates, for each of the plurality of dates and times included in this predetermined time period, the normalized degree of operation of each of the plurality of follower base station systems40bincluded in this base station system group42(Step S303). In this case, for example, for each follower base station system40b, the normalized degree of operation of this follower base station system40bis calculated based on the degree of operation indicated by the parameter data of the follower base station system40band the reference ratio (for example, the ratio “p” identified in the process step of Step S108) of this follower base station system40b.

Then, the removal evaluation module66calculates, for the each of the plurality of dates and times, the difference D for each of the plurality of follower base station systems40bincluded in this base station system group42(Step S304). In this case, for example, for each follower base station system40b, the difference D between the degree of operation of the leader base station system40aand the normalized degree of operation of the follower base station system40bcalculated in the process step of Step S303is calculated as the difference D in this follower base station system40b.

Then, the removal evaluation module66identifies the removal evaluation value for each of the plurality of follower base station systems40bincluded in this base station system group42(Step S305). In this case, for example, for each follower base station system40b, the removal evaluation value of this follower base station system40bis calculated based on the difference D in the follower base station system40bidentified for each of the plurality of dates and times in the process step of Step S304.

Then, the removal module68determines, for each of the plurality of follower base station systems40b, whether or not this follower base station system40bis a base station system to be removed based on the removal evaluation value generated in the process step of Step S305(Step S306).

Then, the removal module68confirms whether or not there is a follower base station system40bdetermined as the base station system to be removed in the process step of Step S306(Step S307).

When there is no follower base station system40bdetermined as the base station system to be removed (Step S307: N), the process returns to the process step of Step S301.

When there is a follower base station system40bdetermined as the base station system to be removed (Step S307: Y), the removal module68removes the base station system to be removed from this base station system group42(Step S308), and the process returns to the process step of Step S301.

In this embodiment, only the leader base station system40aincluded in the base station system group42is a prediction target of the degree of operation, and the degree of operation is not predicted for the follower base station system40b. In this manner, according to this embodiment, the processing load of the communication system1can be reduced.

Further, in this embodiment, the power consumption control is performed by controlling the power state such as the P-state. Thus, according to this embodiment, while the communication performance is ensured, power consumption control having high readiness with respect to the change in degree of operation can be performed.

It should be noted that the present invention is not limited to the above-mentioned embodiment.