JP6761781B2 - Allocation device, allocation method, and program. - Google Patents

Description

The present invention provides a technique for assigning each base station to one mobile traffic processing function in a mobile communication system including a plurality of base stations and a plurality of mobile traffic processing functions for processing mobile traffic generated from the plurality of base stations. It is related.

In a mobile communication system, mobile traffic generated when a terminal communicates via a base station has characteristics for each base station depending on the living conditions of human beings. For this reason, in a specific area, even if the bandwidth is tight due to the occurrence of a large amount of traffic, there may be a situation in which there is little traffic in the surrounding area and there is a margin in the bandwidth.

Various methods for flexibly allocating mobile network functions in order to obtain their statistical multiplex effects, instead of dividing and processing the mobile traffic generated for each base station, are being studied as next-generation mobile communication systems. For example, an architecture FASA (Flexible Access System Architecture) has been proposed in which the functions provided by an access network device are made into components and provided in a flexible combination.

In addition, as a technology to expand the multiplexing area of traffic, MFH (Mobile Front Hall), which is currently constructed in a fixed band (CPRI standard), is made into a variable band by packetization to accommodate multiplex in a PON system. (For example, Non-Patent Document 1).

FIG. 1 is a diagram showing an example of MFH based on a C-RAN (Centralized / Cloud Radio Access Network) architecture. As shown in FIG. 1, in the MFH, a plurality of base stations (including RRH1 and ONU2) having a small cover area are arranged, and BBU4 controls them. The BBU4 is connected to the ONU2 of the base station by an optical fiber by the OLT3 which is an optical termination function. OLT3 and BBU4 are examples of mobile traffic processing functions (which may also be referred to as network functions) that process mobile traffic.

Takayuki Kobayashi, Hiroshi Wang, Daisuke Kuno, Tatsuya Shimada, Jun Terada, Akihiro Otaka, "TDM-PON Upbandwidth Allocation Method Based on Traffic Statistical Multiplexing for Application to Mobile Front Hall", Shingaku Sodai 2016.

Based on the above situation, in order to make statistical multiplexing of traffic in the mobile network effective, a mobile network processing function that processes the base station and the mobile traffic generated there based on the time series change of the traffic in the base station. It is expected to decide the correspondence with.

That is, as shown in FIG. 2, for example, the allocation processing function 7 allocates the moving traffic to the functional component 6 on the moving traffic processing function side 5. The functional component 6 in FIG. 2 is, for example, an optical termination function (assuming the use of PON), a BBU function (assuming the C-RAN architecture), and the like shown in FIG.

Since it is desirable for the service provider to have a low cost for providing the service, when making the above allocation, the time series for each base station of the mobile traffic from the base station to the network (mobile traffic processing function) side. Based on the changes, it is necessary to make allocations so that the number of functional parts that need to be built is reduced. The “cost” in the present specification includes equipment construction costs, penalties for the number of allocation changes, and the like.

Here, Non-Patent Document 1 describes a band allocation technique for allocating a band to MFH by packetizing a mobile traffic from the base station on the premise that the base station is accommodated by PON and realizing a statistical multiplexing effect by a variable band. Proposed. This technology aims to reduce the excessive allocated bandwidth by making the conventional fixed bandwidth variable bandwidth according to the amount of mobile traffic that actually occurs, and reduces the allocated bandwidth to the maximum. It is not a technology to determine the accommodation method of the base station.

In addition, it is being considered to provide the BBU function separated by C-RAN on the virtualization platform by NFV and integrate and pool the BBU function, but the technology is a function that provides the BBU function. It is not a technology that determines the accommodation method of a base station so as to reduce the capacity of parts as much as possible.

The present invention has been made in view of the above points, and in a mobile communication system including a base station and a mobile traffic communication function for processing mobile traffic generated from the base station, the cost related to the mobile traffic processing function is small. An object of the present invention is to provide a technique for assigning a mobile traffic processing function to a base station.

According to the disclosed technology, in a mobile communication system including a plurality of base stations and a plurality of mobile traffic processing functions for processing mobile traffic generated from the plurality of base stations, each base station has one mobile traffic processing function. It is an assignment device to be assigned
An input means for inputting cost information related to the mobile traffic processing function and time-series mobile traffic information for each base station.
The cost calculated from the cost information under the constraints on the amount of mobile traffic that can be allocated in each mobile traffic processing function and the number of base stations that can be allocated, and the cost related to the mobile traffic processing function to be constructed is minimized. It is equipped with a calculation means for calculating the allocation of mobile traffic processing functions to each base station by solving the optimization problem.
The calculation means further uses a penalty for the number of changes in the mobile traffic processing function assignment, provided that the mobile traffic processing function allocation to each base station is variable over a predetermined time length. By solving the optimization problem, an allocation device is provided that calculates the allocation of the mobile traffic processing function to the base station for each unit time within the predetermined time length .

According to the disclosed technology, in a mobile communication system including a base station and a mobile traffic processing function for processing mobile traffic generated from the base station, the mobile communication system is moved to the base station so that the cost related to the mobile traffic processing function is reduced. Technology for assigning traffic processing functions is provided.

It is a figure which shows the example of MFH (Mobile Front Hall) by the C-RAN architecture. It is a figure for demonstrating a problem. It is a system block diagram in embodiment of this invention. It is a figure which shows the structural example of the optimum allocation apparatus 100 in embodiment of this invention. It is a figure which shows the hardware configuration example of the optimum allocation apparatus 100 in embodiment of this invention. It is a figure for demonstrating the process of Example 1. FIG. It is a figure which shows the optimization model of Example 1. FIG. It is a figure for demonstrating the process of Example 2. FIG. It is a figure which shows the optimization model of Example 2. It is a figure for demonstrating the process of Example 3. FIG. It is a figure which shows the optimization model of Example 3. It is a flowchart of the process in Example 4.

Hereinafter, embodiments of the present invention (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.

(System configuration)
FIG. 3 shows a system configuration diagram according to the present embodiment. As shown in FIG. 3, the system in this embodiment includes an optimum allocation device 100 and a mobile communication system 200. The mobile communication system 200 includes a base station 201, a transfer function 202, and an optical termination function 203. In FIG. 3, one base station 201 and one transfer function 202 are shown, but this is for convenience of illustration, and a plurality of each is actually provided. Further, the optical termination function 203 is shown as the mobile traffic processing function, but this is an example, and the mobile traffic processing function may be a function other than the optical termination function 203.

In the present embodiment, it is assumed that the mobile communication system 200 uses an MFH to which a PON system is applied as shown in FIG. That is, the base station 201 in FIG. 3 is a device including RRH and OLT, the transfer function 202 is a function of the MFH section (BBU-RRH), and the optical network unit is an ONU.

By applying the PON system to the MFH section (BBU-RRH), it is possible to efficiently accommodate the mobile traffic generated from the base station due to the equipment sharing effect by sharing the core wire.

Further, in the present embodiment, by deploying the packetization function previously implemented in the BBU based on the C-RAN architecture to the base station 201, the accommodation rate of mobile traffic is improved by the statistical multiplexing effect of the PON section. It is said.

The optimum allocation device 100 shown in FIG. 3 inputs mobile traffic information (time series), network configuration information, and equipment construction cost for each base station, and processes mobile traffic generated from the base station on the station side (example: central aggregation). Determine the assignment of the optical termination function on the station side). The allocation information is sent from the optimum allocation device 100 to the transfer function 202, for example. The transfer function 202 transfers the mobile traffic according to the allocation. In addition, the allocation information is sent to the operator, and the operator constructs the equipment.

The optimum allocation device 100 may be a device connected to a network outside the mobile communication system, for example, the Internet, a device inside the mobile communication system, or a stand-alone device.

FIG. 4 is a functional configuration diagram of the optimum allocation device 100. As shown in FIG. 4, the optimum allocation device 100 includes an input unit 101 for inputting mobile traffic information (time series), network configuration information, and equipment construction cost for each base station, and a base station by solving an optimization problem. The optimization calculation unit 102 that determines the allocation of the optical termination function to the data, the output unit 103 that outputs the calculation result, and the data storage unit 104 that stores the input data, the data necessary for the calculation, the calculation process data, the calculation result, and the like. Have. The processing content of the optimization calculation unit 102 will be described later as the processing content executed by the optimum allocation device 100.

The optimum allocation device 100 according to the present embodiment can be realized by causing a computer to execute a program describing the processing contents described in the present embodiment. FIG. 5 is a diagram showing a hardware configuration example of the optimum allocation device 100 according to the present embodiment. The optimum allocation device 100 of FIG. 5 includes a drive device 300, an auxiliary storage device 302, a memory device 303, a CPU 304, an interface device 305, a display device 306, an input device 307, and the like, which are connected to each other by a bus B, respectively.

The program that realizes the processing in the optimum allocation device 100 is provided by, for example, a recording medium 301 such as a CD-ROM or a memory card. When the recording medium 301 storing the program is set in the drive device 300, the program is installed in the auxiliary storage device 302 from the recording medium 301 via the drive device 300. However, the program does not necessarily have to be installed from the recording medium 301, and may be downloaded from another computer via the network. The auxiliary storage device 302 stores the installed program and also stores necessary files, data, and the like.

The memory device 303 reads the program from the auxiliary storage device 302 and stores the program when the program is instructed to start. The CPU 304 realizes the function related to the optimum allocation device 100 according to the program stored in the memory device 303. The interface device 305 is used as an interface for connecting to a network. The display device 306 displays a programmatic GUI (Graphical User Interface) or the like. The input device 307 is composed of a keyboard, a mouse, buttons, a touch panel, and the like, and is used for inputting various operation instructions.

(Processing content executed by the optimum allocation device 100)
Hereinafter, Examples 1 to 4 will be described as examples of the processing contents executed by the optimum allocation device 100. In the following description of each embodiment, reference numerals 201 and 203 are used for "base station 201" and "optical termination function 203" in order not to cause confusion with the index in the optimization model. Absent.

(Example 1)
In the first embodiment, it is determined that the correspondence between the base station and the function of processing the mobile traffic generated from the base station over the entire time zone to be considered (mobile traffic processing function (optical termination function in the present embodiment)) is fixed. As a premise.

The optimum allocation device 100 allocates the mobile traffic processing function to the base station (quasi) so as to reduce the capacity of the required mobile traffic processing function based on the information of the time-series change of the mobile traffic for each base station. Determined by optimization processing. Here, the "base station" vs. "mobile traffic processing function" is N to 1 (N is an integer of 1 or more). That is, one mobile traffic processing function is assigned to each base station.

In the first embodiment, the (quasi) optimization process is executed in the optimization calculation unit 102 by the (quasi) optimization program. This point is the same in Examples 2 to 4. It should be noted that the description of "(quasi)" is not a method that is guaranteed to derive an exact optimum solution as in Example 4 described later, but is represented by a discovery method. It is intended to be a method for deriving a solution (not necessarily an exact optimal solution).

The process according to the first embodiment will be described with reference to FIG. First, the input unit 101, the optimum allocation apparatus 100, the network configuration information (installed capacity, such constraint information equipment configuration), facility construction cost c _j, and mobile traffic information (time-series) of each base station d _i ^t is Entered. FIG. 6 shows mobile traffic information (time series) for base stations # 0001 to # 0003 as an example.

Next, the optimization calculation unit 102 uses the input information to calculate the allocation so as to minimize the equipment construction cost in consideration of the constraints. In Example 1, the allocation is fixedly applied to the entire time considered. The total time to be considered is a predetermined time length, for example, 24 hours, 1 week, 1 month, or the like.

The calculation result of the allocation by the optimization calculation unit 102 is stored in the data storage unit 104 and output by the output unit 103. In the example of FIG. 6, the mobile traffic generated from the base station # 0001 and the mobile traffic generated from the base station # 0003 are assigned to the mobile traffic processing function # A, and the mobile traffic generated from the base station # 0002 is assigned to the mobile traffic processing function. The mobile traffic assigned to # B and generated from the base station # 0004 is assigned to the mobile traffic processing function # C.

Hereinafter, the processing content by the optimization calculation unit 102 will be described more specifically. The optimization calculation unit 102 in the first embodiment calculates the allocation of the mobile traffic processing function to the base station by solving the optimization problem. The optimization model (problem definition) is as follows.

<Notation>
The notation used in the optimization model is as follows.

-I: Base station Index
J: Index for the optical termination function on the station side for mobile traffic from the base station
T: Hour Index
・ I: Set of base stations ・ J: Set of optical termination functions (on the station side) ・ T: Number of hours, t ∈ [1, T]
<Parameter>
The parameters used in the optimization model are:

· C _j: optical terminal function j of the equipment construction cost · U _j: Capacity · M j of the moving traffic can be allocated to the optical terminal function _j: maximum number of assignable base station optical terminal function j · d _i ^t: Amount of traffic generated from base station i at time t <variable>
The variables used in the optimization model are:

-Y _j : Binary variable that specifies the equipment construction of the optical termination function j (1 when constructing equipment, 0 otherwise)
-X _{i, j} : Binary variable that specifies the allocation of base station i to the optical termination function j (1 if assigned, 0 otherwise)
<Optimized model>
The optimization model of Example 1 is shown in FIG.

As shown in FIG. 7, the optimization calculation unit 102 minimizes Σ _j ∈ _J c _j · y _j shown in equation (1) under the constraints shown in equations (2) and (3). Calculate the assignment of station i to the optical termination function j. Specifically, x _{i and j} in each _{i and j} are obtained as the calculation result of the allocation.

Equation (1) shows that the equipment construction cost of the entire optical terminal function to be constructed is minimized. In the equation (2), the total of the traffic amounts of all the base stations assigned to the optical terminal function j in each time t in the total time T to be considered and each optical terminal function j is the optical terminal function j. Indicates a constraint that is less than or equal to the capacity of mobile traffic that can be allocated to. Equation (3) shows a constraint condition that the number of all base stations assigned to the optical termination function j in each optical termination function j is equal to or less than the maximum number of base stations that can be assigned to the optical termination function j. ..

(Example 2)
In the second embodiment, it is assumed that the correspondence between the base station and the mobile traffic function is variable. However, the mobile traffic function is not increased or decreased depending on the time to be considered, and its installed capacity is constant during the entire time to be considered. That is, for example, if the entire period to be considered is one day and the unit time that can change the correspondence between the base station and the mobile traffic function is one hour, the correspondence between the base station and the mobile traffic function is one hour in one day. Although it can vary from unit to unit, the equipment for mobile traffic is fixed for one day.

The optimum allocation device 100 allocates the mobile traffic processing function to the base station (“base station”) so as to reduce the capacity of the required mobile traffic processing function based on the information of the time-series change of the mobile traffic for each base station. The "moving traffic processing function" determines N to 1 (N is an integer of 1 or more) by (quasi) optimization processing.

The process in the second embodiment will be described with reference to FIG. First, in the same manner as in the first embodiment, the input unit 101 provides the optimum allocation device 100 with network configuration information (equipment capacity, equipment configuration constraint information, etc.), equipment construction cost _cj , and mobile traffic information for each base station. (time _{series) d} ^{i t} is input.

Next, the optimization calculation unit 102 uses the input information to calculate the allocation so as to minimize the equipment construction cost in consideration of the constraints. In the second embodiment, the allocation is changed with time.

The calculation result of the allocation by the optimization calculation unit 102 is stored in the data storage unit 104 and output by the output unit 103. In the example of FIG. 8, the allocation result of each time of time 1, time 2, ... time t, ... Is output. As an example, at time t, mobile traffic generated from base station # 0001 and mobile traffic generated from base station # 0003 are assigned to mobile traffic processing function # A, and mobile traffic generated from base station # 0002 and base station # 0002 are assigned. The mobile traffic generated from 0004 is assigned to the mobile traffic processing function # B.

Hereinafter, the processing content by the optimization calculation unit 102 will be described more specifically. Similar to the first embodiment, the optimization calculation unit 102 in the second embodiment calculates the allocation of the mobile traffic processing function to the base station by solving the optimization problem. The optimization model (problem definition) is as follows.

<Notation>
The notation used in the optimization model is as follows.

-I: Base station Index
J: Index for the optical termination function on the station side for mobile traffic from the base station
T: Hour Index
・ I: Set of base stations ・ J: Set of optical termination functions (on the station side) ・ T: Number of hours, t ∈ [1, T]
<Parameter>
The parameters used in the optimization model are:

· C _j: optical terminal function j of the equipment construction cost · U _j: Capacity · M j of the moving traffic can be allocated to the optical terminal function _j: maximum number of assignable base station optical terminal function j · d _i ^t: Amount of traffic generated from base station i at time t <variable>
The variables used in the optimization model are:

-Y _j : Binary variable that specifies the equipment construction of the optical termination function j (1 when constructing equipment, 0 otherwise)
-X _{i, j, t} : Binary variable that specifies the allocation of the base station i to the optical termination function j (1 if assigned, 0 otherwise)
<Optimized model>
The optimization model of the second embodiment is shown in FIG.

As shown in FIG. 9, the optimization calculation unit 102 minimizes Σ _j ∈ _J c _j · y _j shown in equation (4) under the constraints shown in equations (5) and (6). Calculate the assignment of station i to the optical termination function j. Specifically, x _{i, j, t at} each i, each j, and each t are obtained as the calculation result of the allocation.

Equation (4) shows that the equipment construction cost of the entire optical terminal function to be constructed is minimized. In the equation (5), the total of the traffic amounts of all the base stations assigned to the optical terminal function j in each time t in the total time T to be considered and each optical terminal function j is the optical terminal function j. Indicates a constraint that is less than or equal to the capacity of mobile traffic that can be allocated to. Equation (6) shows a constraint condition that the number of all base stations assigned to the optical termination function j in each optical termination function j is equal to or less than the maximum number of base stations that can be assigned to the optical termination function j. ..

(Example 3)
In the third embodiment, the correspondence between the base station and the mobile traffic function is variable as in the second embodiment, but in order to minimize the number of changes in the correspondence relationship, the change for each base station or the change for all base stations is made. In addition to the purpose of optimizing the penalty, calculate the correspondence between the base station and the mobile traffic function. As in the second embodiment, the construction of the equipment for the mobile traffic function is fixed at the entire time to be considered.

The process according to the third embodiment will be described with reference to FIG. First, the input unit 101, the optimum allocation apparatus 100, the network configuration information (installed capacity, such constraint information equipment configuration), facility construction cost _{c j,} mobile traffic information _{(time-series)} of each base station _d ^{i t,} and , Penalty information regarding base station assignment change is entered. The information on the penalty for changing the allocation of the base station is, for example, a penalty function described later. In the example of FIG. 10, two types of penalty information, (a) and (b), are shown. In the case shown in (a), the penalty regarding the number of allocation changes for each base station is used, and in the case shown in (b), the penalty regarding the total number of allocation changes is used.

Next, the optimization calculation unit 102 uses the input information to calculate the allocation so that the total of the equipment construction cost and the penalty is (quasi) minimum, taking into consideration the constraints. Also in the third embodiment, the allocation is changed with time.

The calculation result of the allocation by the optimization calculation unit 102 is stored in the data storage unit 104 and output by the output unit 103. In the example of FIG. 10, the allocation result of each time of time 1, time 2, ... time t, ... Is output. As an example, at time t, mobile traffic generated from base station # 0001 and mobile traffic generated from base station # 0003 are assigned to mobile traffic processing function # A, and mobile traffic generated from base station # 0002 and base station # 0002 are assigned. The mobile traffic generated from 0004 is assigned to the mobile traffic processing function # B.

Hereinafter, the processing content by the optimization calculation unit 102 will be described more specifically. Similar to the first and second embodiments, the optimization calculation unit 102 in the third embodiment calculates the allocation of the mobile traffic processing function to the base station by solving the optimization problem. The optimization model (problem definition) is as follows. As an example, a case where the penalty is applied to the total number of base station-mobile traffic processing function allocation changes is described.

<Notation>
The notation used in the optimization model is as follows.

-I: Base station Index
J: Index for the optical termination function on the station side for mobile traffic from the base station
T: Hour Index
・ I: Set of base stations ・ J: Set of optical termination functions (on the station side) ・ T: Number of hours, t ∈ [1, T]
<Parameter>
The parameters used in the optimization model are:

· C _j: optical terminal function j of the equipment construction cost · U _j: Capacity · M j of the moving traffic can be allocated to the optical terminal function _j: maximum number of assignable base station optical terminal function j · d _i ^t: Amount of traffic generated from base station i at time t <variable>
The variables used in the optimization model are:

-Y _j : Binary variable that specifies the equipment construction of the optical termination function j (1 when constructing equipment, 0 otherwise)
-X _{i, j, t} : Binary variable that specifies the allocation of the base station i to the optical termination function j (1 if assigned, 0 otherwise)
-Z: Total number of all base station allocation changes for optical termination function <Penalty function>
The penalty functions used in the optimization model are:

・ Λ (z): Penalty imposed according to the total number of allocation changes <optimization model>
The optimization model of Example 3 is shown in FIG.

As shown in FIG. 11, the optimization calculation unit 102 performs Σ _j ∈ _J c _j · y _j shown in equation (7) under the constraints shown in equations (8), (9), and (10). Calculate the allocation of the base station i to the optical termination function j that minimizes + λ (z). Specifically, x _{i, j, t at} each i, each j, and each t are obtained as the calculation result of the allocation.

Equation (7) shows that the sum of the equipment construction cost and the penalty of the entire optical terminal function to be constructed is minimized. In the equation (8), the total of the traffic amounts of all the base stations assigned to the optical termination function j in each time t in the total time T to be considered and each optical termination function j is the optical termination function j. Indicates a constraint that is less than or equal to the capacity of mobile traffic that can be allocated to. Equation (9) shows a constraint condition that the number of all base stations assigned to the optical termination function j in each optical termination function j is equal to or less than the maximum number of base stations that can be assigned to the optical termination function j. .. Equation (10) indicates the total number of allocation changes in the total time T to be considered.

In the above example, the penalty is used for the objective function as a part of the cost, but it may be used as a constraint condition that the penalty is suppressed to a certain value or less.

(Example 4)
In the fourth embodiment, in the allocation of the first to third embodiments, in order to reduce the calculation time by the (quasi) optimization processing in the optimization calculation unit 102, the allocation to be randomly performed is an upper bound, and the mobile traffic of the base station is performed. By using the lower bound information obtained by dividing the above and allowing allocation to multiple mobile traffic processing functions, when a feasible solution in the lower bound is obtained, it is used as the solution, and when it is not obtained, the upper bound is used. Evaluate whether or not a feasible solution can be obtained at the midpoint between the boundary and the lower bound, and if it is obtained, set the midpoint as a new upper bound, and if not, set the midpoint as a new lower bound. Is narrowed down to finally derive an acceptable (quasi) optimal solution.

The processing procedure of the optimization calculation unit 102 in the fourth embodiment will be described with reference to the flowchart of FIG. In the following processing, UB indicates the upper bound value obtained at the time of performing the treatment, and LB indicates the lower bound value obtained at the time of performing the processing.

In step S101, the equipment construction cost UB_0 of the solution obtained by randomly allocating the base station to the mobile traffic processing function while satisfying the constraint condition on the equipment configuration is set as the initial upper bound value (UB: = UB_0). ). For example, when the method according to the first embodiment is used, in step S101, the total of the traffic amounts of all the base stations assigned to the optical termination function j at each time t and each optical termination function j is the optical termination. The constraint condition that it is less than or equal to the capacity of the mobile traffic that can be assigned to the function j, and the number of all base stations assigned to the optical terminal function j in each optical terminal function j are the bases that can be assigned to the optical terminal function j. Allocations that satisfy the constraint condition that the number of stations is less than or equal to the maximum number are randomly generated, and the equipment construction cost of the allocation is set to UB_0. Here, the case where the method according to Example 1 is used is shown, but the method according to Examples 2 to 3 may be used.

In step S102, on the premise that the mobile traffic of the base station is divided and allocation to a plurality of mobile traffic processing functions is allowed, the optimum allocation solution is calculated while satisfying the constraints, and the equipment of the obtained solution is installed. The construction cost LB_0 is set as the initial lower bound value (LB: = LB_0). For example, when a method according to the first embodiment is used, in step S102, it is assumed that the mobile traffic generated from a single base station is divided and assigned to a plurality of mobile traffic processing functions, and each time t And, in each optical termination function j, the constraint condition that the total traffic amount of all base stations assigned to the optical termination function j is equal to or less than the capacity of mobile traffic that can be assigned to the optical termination function j, and each In the optical termination function j, the equipment is constructed within the allocation satisfying the constraint that the total number of base stations assigned to the optical termination function j is equal to or less than the maximum number of base stations that can be assigned to the optical termination function j. The allocation that minimizes the cost is specified by the minimization algorithm, and the equipment construction cost of the allocation is set to LB_0. Here, the case where the method according to Example 1 is used is shown, but the method according to Examples 2 to 3 may be used.

In step S103, it is checked as a constraint satisfaction problem whether or not the solution that realizes the obtained lower bound value (LB) is a feasible solution. The feasible solution is a solution in which the mobile traffic of the base station is assigned to the mobile traffic processing function without being divided. Further, the feasible solution that realizes LB is a feasible solution in which the equipment construction cost is LB or less. Similarly, the feasible solution that realizes UB is a feasible solution whose equipment construction cost is UB or less. Such a viable solution is, for example, the above-mentioned constraint condition of the capacity of the mobile traffic that can be allocated, the constraint condition of the maximum number of base stations that can be allocated, and the constraint condition of the equipment construction cost (LB or UB or V). It can be found as a solution to the constraint satisfaction problem that satisfies.

If there is a feasible solution (Yes in S104), the process proceeds to step S105, the feasible solution that realizes LB is set as the optimum solution, and the process ends. If there is no feasible solution (No in S104), the process proceeds to step S106.

In step S106, it is checked whether or not "UB-LB <target value" (the target value is a preset value) is satisfied, and if it is satisfied (Yes in S106), the process proceeds to step S107 to realize UB. Set the possible solution as a quasi-optimal solution and end the process.

If “UB-LB <target value” is not satisfied (No in S106), the process proceeds to step S108.

In step S108, V is set to (UB + LB) / 2, and whether or not the solution that realizes V is a feasible solution is checked as a constraint satisfaction problem. If there is a feasible solution (Yes in S109), the process proceeds to step S111, and the process proceeds to step S108 with UB as V. If there is no feasible solution (No in S109), the process proceeds to step S110, and the process proceeds to step S103 with LB as V.

(About the effect of each example)
According to Examples 1 and 2, it is possible to allocate the mobile traffic processing function to the base station so that the capacity of the mobile traffic processing function is reduced.

According to the third embodiment, the change in the allocation relationship between the base station and the mobile traffic processing function can be minimized in view of the effect of reducing the equipment construction cost.

According to Example 4, the (quasi) optimization program that implements the desired allocation according to Examples 1 to 3 above may take an enormous amount of time to calculate as the computational load increases and the scale of the problem increases. is there. In Example 4, the method of Examples 1 to 3 is used to determine a desirable allocation in a short time by applying an easily computable constraint satisfaction problem algorithm instead of directly applying the optimization algorithm. Can be done.

(Summary of embodiments)
According to the present embodiment, in a mobile communication system including a plurality of base stations and a plurality of mobile traffic processing functions for processing mobile traffic generated from the plurality of base stations, each base station has one mobile traffic processing function. An allocation device to be assigned to, an input means for inputting cost information related to the mobile traffic processing function and time-series mobile traffic information for each base station, and the amount of mobile traffic that can be assigned in each mobile traffic processing function, and allocation. A calculation method for calculating the allocation of mobile traffic processing functions to each base station by solving an optimization problem that minimizes the cost of the mobile traffic processing functions constructed under the constraints on the number of possible base stations. An allocation device characterized by being provided is provided.

The optimum allocation device 100, the input unit 101, and the optimization calculation unit 102 are examples of the allocation device, the input means, and the calculation means, respectively. In addition, the facility construction cost and the penalty related to the number of allocation changes are both examples of costs.

The calculation means solves the optimization problem under the condition that the allocation of the mobile traffic processing function to each base station is variable within a predetermined time length, thereby performing a unit time within the predetermined time length. The allocation of the mobile traffic processing function to the base station may be calculated for each.

The calculation means may calculate the allocation of the mobile traffic processing function to each base station by further using the penalty regarding the number of changes in the allocation of the mobile traffic processing function in the optimization problem.

The calculation means sets the cost of the solution obtained by randomly assigning the base station to the mobile traffic processing function as the initial value of the upper bound value, and divides the mobile traffic of the base station into a plurality of mobile traffic processing functions. There is a feasible solution that realizes the lower bound value by solving the constraint satisfaction problem, starting from the initial value, while using the cost of solving the optimization problem calculated on the premise that allocation is allowed as the initial value of the lower bound value. Find out if it does, calculate the allocation that realizes the lower bound value if it exists, and if it does not exist, constrain the existence of a viable solution of the value between the upper and lower bound values at that time and solve the constraint satisfaction problem. It may be confirmed by this, and the interval between the upper bound value and the lower bound value may be narrowed.

Although the present embodiment has been described above, the present invention is not limited to such a specific embodiment, and various modifications and changes can be made within the scope of the gist of the present invention described in the claims. It is possible.

100 Optimal allocation device 101 Input unit 102 Optimization calculation unit 103 Output unit 104 Data storage unit 200 Mobile communication system 201 Base station 202 Transfer function 203 Optical termination function 300 Drive device 301 Recording medium 302 Auxiliary storage device 303 Memory device 304 CPU
305 Interface device 306 Display device 307 Output device

Claims (4)

In a mobile communication system including a plurality of base stations and a plurality of mobile traffic processing functions for processing mobile traffic generated from the plurality of base stations, each base station is assigned to one mobile traffic processing function.
An input means for inputting cost information related to the mobile traffic processing function and time-series mobile traffic information for each base station.
The cost calculated from the cost information under the constraints on the amount of mobile traffic that can be allocated in each mobile traffic processing function and the number of base stations that can be allocated, and the cost related to the mobile traffic processing function to be constructed is minimized. It is equipped with a calculation means for calculating the allocation of mobile traffic processing functions to each base station by solving the optimization problem.
The calculation means further uses a penalty for the number of changes in the mobile traffic processing function assignment, provided that the mobile traffic processing function allocation to each base station is variable over a predetermined time length. An allocation device characterized by calculating the allocation of mobile traffic processing functions to a base station for each unit time within the predetermined time length by solving an optimization problem . The calculation means sets the cost of the solution obtained by randomly assigning the base station to the mobile traffic processing function as the initial value of the upper bound value, and divides the mobile traffic of the base station into a plurality of mobile traffic processing functions. There is a feasible solution that realizes the lower bound value by solving the constraint satisfaction problem, starting from the initial value, while using the cost of solving the optimization problem calculated on the premise that allocation is allowed as the initial value of the lower bound value. Find out if it does, calculate the allocation that realizes the lower bound value if it exists, and if it does not exist, constrain the existence of a viable solution between the upper and lower bound values at that time and solve the constraint satisfaction problem. The allocation device according to claim 1 , wherein the interval between the upper bound value and the lower bound value is narrowed. In a mobile communication system including a plurality of base stations and a plurality of mobile traffic processing functions for processing mobile traffic generated from the plurality of base stations, allocation performed by an allocation device that allocates each base station to one mobile traffic processing function. It ’s a method,
An input step for inputting cost information related to the mobile traffic processing function and time-series mobile traffic information for each base station, and
The cost calculated from the cost information under the constraints on the amount of mobile traffic that can be allocated in each mobile traffic processing function and the number of base stations that can be allocated, and the cost related to the mobile traffic processing function to be constructed is minimized. It has a calculation step to calculate the allocation of mobile traffic processing functions to each base station by solving the optimization problem.
In the calculation step, the assigning device further penalizes the number of changes in the mobile traffic processing function allocation to each base station, provided that the mobile traffic processing function allocation to each base station is variable within a predetermined time length. An allocation method, which is used to calculate the allocation of a mobile traffic processing function to a base station for each unit time within the predetermined time length by solving the optimization problem . A program for operating a computer as each means in the allocation device according to claim 1 or 2 .

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