Control device, control method, and program

Abstract

A control device according to one embodiment of the present invention is for embedding a virtual network for realizing service provision onto a physical network, and comprises: a first acquisition unit configured to acquire a predictive value for the traffic amount of the service and a predictive value for a power consumption amount at a physical node, among physical nodes constituting the physical network, which is not given an instruction value relating to the power consumption amount; a second acquisition unit configured to acquire information relating to the physical network; a third acquisition unit configured to acquire the instruction value that is given to at least some of the physical nodes constituting the physical network; a solution calculation unit configured to calculate, on the basis of the predictive value for the traffic amount, the predictive value for the power consumption amount, the information relating to the physical network, and the instruction value, and on the basis of a restriction condition that the power consumption amount of the physical nodes, to which the instruction value was given, should satisfy the instruction value, an optimum solution for a 2-stage robust optimization problem relating to allocation of virtual nodes, constituting the virtual network, to physical nodes and relating to route determination between the virtual nodes; and a control unit configured to control, on the basis of the allocation of the virtual nodes and the route determination represented by the optimum solution, the virtual network embedded in the physical network.

Description

Technical Field

[0001] The present invention relates to a control device, a control method, and a program.

Background Art

[0002] In recent years, with the development of NFV (Network Function Visualization), which is a virtualization technology, it has become possible to provide a huge variety of network services by flexibly combining various virtual network resources (VR: Virtual Resource) and virtual network functions (VNF: Virtual Network Function). In order to realize the provision of such services, it is necessary to appropriately allocate VR and VNF to physical resources for each service. In addition, it is necessary to appropriately determine an End-to-End path for sending the service and guarantee a desired communication performance (for example, communication delay time, etc.). For this reason, many control methods for VNF allocation and path determination in an NFV environment have been studied and reported.

[0003] By the way, in recent years, in order to reduce the environmental load, the movement to introduce renewable energy has been promoted worldwide. As a problem in operating a virtual network by utilizing renewable energy, there is a point that due to the uncertainty of the weather, it is impossible to supply a desired power generation amount that matches the demand amount, and over-supply or under-supply occurs. In contrast, for example, in Non-Patent Document 1, a virtual network control method for robustly preventing over-supply and under-supply is proposed under the assumption that there is also uncertainty in the traffic volume in addition to the amount of power of renewable energy.

Prior Art Documents

Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, the method proposed in Non-Patent Document 1 above assumes the adjustment of a closed supply-demand balance within the virtual network, and is not virtual network control under the provision of any command regarding the power consumption amount.

[0006] For example, consider the case where a power system (microgrid) operating a virtual network cooperates with the power grid and receives a demand response request. At this time, it is necessary to generate surplus power in the area that has received the command within the communication network. However, since the method proposed in Non-Patent Document 1 above assumes preventing oversupply, it cannot respond to the request.

[0007] Also, for example, consider the case where a communication network operator enters the power market. In this case, it is assumed that an appropriate amount of power for sale corresponding to the bidding price in the market is determined for each power trading transaction. Based on this, with the method proposed in Non-Patent Document 1 above, it is considered that surplus power that can generate the amount of power for sale cannot be produced, and economic losses may occur.

[0008] Therefore, when any command is given regarding the power consumption amount within the communication network, it is considered necessary to perform robust VNF allocation and route determination against the uncertainties of both the traffic volume and renewable energy.

[0009] One embodiment of the present invention has been made in view of the above points, and an object thereof is to realize virtual network control that is robust against the uncertainty of traffic volume and the uncertainty of renewable energy and satisfies a command regarding power consumption.

Means for Solving the Problems

[0010] To achieve the above object, a control device according to an embodiment is a control device that embeds a virtual network for realizing service provision on a physical network, and includes a first acquisition unit configured to acquire a predicted value of the traffic volume of the service and a predicted value of the power consumption of a physical node in the physical nodes constituting the physical network for which a command value regarding power consumption is not given, a second acquisition unit configured to acquire information regarding the physical network, a third acquisition unit configured to acquire the command value given to at least some of the physical nodes constituting the physical network, and a solution calculation unit configured to calculate an optimal solution to a two-stage robust optimization problem regarding the allocation of virtual nodes of the virtual network to physical nodes and the route determination between the virtual nodes, with the constraint condition that the power consumption of the physical nodes given the command value satisfies the command value, based on the predicted value of the traffic volume, the predicted value of the power consumption, the information regarding the physical network, and the command value, and a control unit configured to control the virtual network embedded in the physical network based on the allocation of the virtual nodes and the route determination represented by the optimal solution.

Effects of the Invention

[0011] It is possible to realize virtual network control that is robust against the uncertainty of traffic volume and the uncertainty of renewable energy and satisfies a command regarding power consumption.

Brief Description of the Drawings

[0012]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Mode for Carrying Out the Invention

[0013] Hereinafter, an embodiment of the present invention will be described. In this embodiment, a control device 10 capable of realizing virtual network control (VNF allocation and routing determination) that is robust against the uncertainty of traffic volume and the uncertainty of renewable energy and satisfies commands regarding power consumption will be described. Here, hereinafter, in order to distinguish from a virtual network, a network composed of physical nodes and physical links that require power is also referred to as a physical network. On the other hand, a network in which VNFs are virtual nodes and the paths between VNFs are virtual links is also referred to as a virtual network. Further, as physical nodes, for example, bases such as data centers are assumed, and it is assumed that command values regarding power consumption are given to at least some of the physical nodes in the physical network. Note that the command value regarding power consumption is an instruction in which the power consumption of the physical node is specified. Such a command value is given to the physical node in order to secure the necessary amount of power to respond to the request, for example, in response to a demand response request or a request to secure the amount of power sold.

[0014] <Theoretical Configuration> Hereinafter, the theoretical configuration of the present embodiment will be described.

[0015] A virtual network defined by a combination of a starting point (e.g., a base that accommodates users) and an end point (e.g., a base where a server is installed) and a VNF (e.g., a firewall) used to provide a service is regarded as the same as the service provided by this virtual network (SFC: Service Function Chaining), and N s Consider the problem of embedding N services into a physical network. s N s We consider the problem of embedding a virtual network into a physical network, where the virtual links can be split into any number of paths and embedded in any proportion into one or more physical links connected to the physical nodes.

[0016] The topology of a physical network is denoted as g(N,L), where N is the set of physical nodes and L is the set of physical links. n ⊆L is the set of physical links that flow into physical node n∈N, and O n Let L be the set of physical links that flow out from a physical node n∈N. Let S be the set of services, and V be the set of types of VNFs. Furthermore, let N r ⊆N is the set of physical nodes to which the power consumption command value is given,

[0017]

number

[0018] At this time, each service is denoted as g(V s , E s ). V s ⊆ V is the set of VNFs of the s-th service, and E s is the set of virtual links of the s-th service. Note that V includes the start node and end node of the service.

[0019] Also, the virtual link e ∈ E of the s-th service s is also denoted as (v so , v d ) interchangeably. However, v so is the start node of the virtual link e, and v d is the end node of the virtual link e.

[0020] An example of embedding a service into a physical network is shown in FIG. 1. In the example shown in FIG. 1, the case where the first service g(V1, E1) composed of start point → VNF2 → VNF3 → VNF4 → end point, the second service g(V2, E2) composed of start point → VNF1 → VNF2 → VNF3 → end point, etc. are embedded into the physical network g(N, L) is shown. Specifically, the first service g(V1, E1) is embedded in physical nodes 3 → physical node 5 → physical node 6 → physical node 2 → physical node 4. Similarly, the second service g(V2, E2) is embedded in physical nodes 1 → physical node 3 → physical node 4 → physical node 2 → physical node 6.

[0021] Let the traffic volume generated by the s-th service (hereinafter, also referred to as "service s") be λ s . As the traffic volume λ s , for example, the data transfer rate bps, etc. can be mentioned. When performing future VNF allocation and routing determination, it is assumed that the traffic volume λ s is obtained as a predicted value by some prediction method. That is, considering the uncertainty of the predicted value of the traffic volume λ s , it is necessary to consider the problem of embedding the virtual network. Note that the traffic volume λ sAs prediction methods, various methods can be considered. For example, a time series model such as an autoregressive model is constructed to predict future traffic volume from time series data of past traffic volume, and a method of obtaining a predicted value of future traffic volume from this time series model can be mentioned. In addition, other methods such as a method using the average and variance of traffic volume in the past few days as predicted values can also be considered.

[0022] In addition, as the power source supplied to each physical node, renewable energy and contract power are assumed. Since the power supply amount of renewable energy depends on the natural environment and the like, there is uncertainty in its predicted value. Therefore, the maximum power amount μ n (hereinafter referred to as the maximum power for use.) also has uncertainty. That is, considering the uncertainty of the maximum power for use μ n , it is necessary to consider the virtual network embedding problem. In the following, the maximum power for use of a physical node for which a command value regarding the power consumption amount is not given, that is,

[0023]

Number

[0024] Under the above assumptions, a robust virtual network embedding problem is formulated for the uncertainty of traffic volume and the uncertainty of renewable energy. As a preparation for this, first, the uncertainty of traffic volume and the uncertainty of the maximum power for use of each physical node are described as the following polyhedral sets.

[0025]

Number

[0026]

Number

[0027]

Number

[0028] In the above uncertainty set (1), the parameter γ λ is a parameter that adjusts how much deviation exists from the nominal value. Similarly, in the above uncertainty set (2), the parameter γ μ is a parameter that adjusts how much deviation exists from the nominal value. These parameters γ λ and γ μ can also be said to be parameters that define the size of the uncertainty set.

[0029] With the above preparations, for the uncertainty of the traffic volume and the uncertainty of the maximum power consumption of the physical node described in the above uncertainty sets (1) and (2) respectively, the virtual network embedding problem that minimizes the total cost of the entire virtual network is formulated as the following two-stage robust optimization problem.

[0030]

Number

[0031] Also, y​l e,s ∈R (where l is the lowercase L) is a continuous variable that takes values from 0 to 1, and represents the ratio of embedding the virtual link e ∈ E of service s into the physical link l ∈ L. In the objective function (3a), f1(x s ) and f2(y n v,s ) are functions defined as follows respectively. l e,s

[0032]

Number

[0033] Equation (3b) represents the power consumption constraint in the physical node where the command value regarding the power consumption is given. d n v,s is the power consumption coefficient when the VNF v ∈ V in service s is assigned to the physical node n. Note that equation (3b) is an equation, assuming that the power consumption on the left side exactly matches the command value p s n , but it is not limited to this. For example, it may be an inequality such that the power consumption on the left side falls within a certain range (that is, within certain upper and lower limit values). Thus, even when using a relaxed command value such as suppressing the power consumption within a certain range as the command value regarding the power consumption (for example, a command value with the upper and lower limit values of the power consumption specified), the present embodiment can be similarly applied.

[0034] ​​Inequality (3c) represents the constraint on the maximum power consumption in a physical node for which no command value regarding the power consumption is given. Equation (3d) represents that each VNF of each service can be assigned to only one physical node. On the other hand, inequality (3e) means that in one service, a plurality of VNFs cannot be assigned to one physical node. Although the constraint condition of (3e) seems strict at first glance and may seem to narrow the practical application range, for example, by treating a combination of two or more VNFs to be assigned to a certain physical node as a new single VNF, it becomes possible to assign two or more VNFs to one physical node.

[0035] Also, when the traffic volume λ s and x n v,s are fixed, the set of y l e,s that can be obtained, y(λ s ,x n v,s ) is

[0036]

Number

[0037] (3a) to (3f) and the two-stage robust optimization problem formulated by (5a) to (5d) are solved in two stages. First, in the first stage, for the scenario where the traffic volume λ s is unknown and the maximum power consumption μ n is the worst case, the VNF assignment x n v,s ) that minimizes the node power congestion rate f1(x n v,sDetermine it. In the second stage, with the traffic volume λ s known, determine the embedding ratio y l e,s of the virtual link that minimizes the link congestion rate f2(y l e,s )(i.e., the path). As a result, a control solution (i.e., VNF assignment x n v,s and path determination y l e,s ) that is robust against the uncertainty of the traffic volume and the uncertainty of the renewable energy and satisfies the command regarding the power consumption can be obtained. Hereinafter, the specific solution procedure will be described.

[0038] Construct a solution algorithm for the two-stage robust optimization problem formulated in (3a) to (3f) and (5a) to (5d) based on the Column-and-constraint generation (C&CG) method. This C&CG method is an algorithm that decomposes the original problem into a master problem and a subproblem and obtains the solution of the original problem by alternately solving them. Hereinafter, (3a) to (3f) are collectively denoted as (3). Similarly, (5a) to (5d) are collectively denoted as (5). For other equation numbers, the same method shall be used when multiple equation numbers are collectively denoted.

[0039] Define the master problem at step K as follows.

[0040]

Equation

[0041]

Equation

[0042]

Number

[0043] Next, define the sub-problem at step K as follows.

[0044]

Number

[0045] The above sub-problem (7) is a bilevel optimization problem, and since the objective function is not linear, it is difficult to solve in its current form. Therefore, in order to avoid this, first, by converting the maximum value acquisition function f2(y l e,s ) into a linear constraint,

[0046]

Number

[0047]

Number

[0048]

Number

[0049] In the above maximization problem (9), π 1,l , π 2,l , π 3,l , ξ n e,s , θ l e,s respectively represent the dual variables for the constraint conditions of (8), (5b), (5c), (5d), y l e,s ≤1. The optimization problem (9) is a non-linear optimization problem because of the product of λ s and π 1,l , and the product of λ s and π 2,l . In this embodiment, the maximization problem (9) is solved by the primal-dual interior point method. Hereinafter, the optimal solution of the maximization problem (9) is denoted as λ s * (K), and the objective function value corresponding to this optimal solution is denoted as Q(K). At this time, the upper bound of the optimal solution of the original problem (3) is denoted as ψ UB (K), and

[0050]

Number

[0051] By alternately repeating the solution of the above master problem (6) and sub-problem (9), it is guaranteed that the upper bound and the lower bound asymptotically converge to the optimal solution of the original problem (3).

[0052] <Hardware Configuration of Control Device 10> Next, the hardware configuration of the control device 10 according to the present embodiment will be described with reference to FIG. 2. FIG. 2 is a diagram showing an example of the hardware configuration of the control device 10 according to the present embodiment.

[0053] As shown in FIG. 2, the control device 10 according to the present embodiment is realized by the hardware configuration of a general computer or computer system, and includes an input device 101, a display device 102, an external I / F 103, a communication I / F 104, a processor 105, and a memory device 106. These pieces of hardware are each communicably connected by a bus 107.

[0054] The input device 101 is, for example, a keyboard, a mouse, a touch panel, or the like. The display device 102 is, for example, a display or the like. Note that the control device 10 may not have at least one of the input device 101 and the display device 102, for example.

[0055] The external I / F 103 is an interface with an external device such as a recording medium 103a. The control device 10 can read from and write to the recording medium 103a via the external I / F 103. Examples of the recording medium 103a include a CD (Compact Disc), a DVD (Digital Versatile Disk), an SD memory card (Secure Digital memory card), a USB (Universal Serial Bus) memory card, and the like.

[0056] The communication I / F 104 is an interface for connecting the control device 10 to a communication network. The processor 105 is various arithmetic units such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), for example. The memory device 106 is various storage devices such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), a RAM (Random Access Memory), a ROM (Read Only Memory), or a flash memory, for example.

[0057] The control device 10 according to the present embodiment can realize the virtual network control process described later by having the hardware configuration shown in FIG. 2. Note that the hardware configuration shown in FIG. 2 is an example, and the control device 10 may have other hardware configurations. For example, the control device 10 may have a plurality of processors 105, may have a plurality of memory devices 106, or may have various hardware not shown in the figure.

[0058] <Functional Configuration of Control Device 10> Next, the functional configuration of the control device 10 according to the present embodiment will be described with reference to FIG. 3. FIG. 3 is a diagram showing an example of the functional configuration of the control device 10 according to the present embodiment.

[0059] As shown in FIG. 3, the control device 10 according to the present embodiment includes a predicted value collection unit 201, a physical network information collection unit 202, a command value acquisition unit 203, a control calculation unit 204, and a control unit 205. Each of these units is realized by a process executed by the processor 105 by one or more programs installed in the control device 10.

[0060] The predicted value collection unit 201 collects the predicted values of the traffic volume of each service and the predicted values of the maximum power consumption of each physical node (however, physical nodes for which no command value regarding power consumption is given). That is, the predicted value collection unit 201 acquires the nominal values and their deviations of the traffic volume of each service and the nominal values and their deviations of the maximum power consumption of each physical node for which no command value regarding power consumption is given.

[0061] In this embodiment, it is assumed that the predicted values of the traffic volume of each service and the predicted values of the maximum power consumption of each physical node are obtained by a prediction algorithm using a time series model or the like. For example, when performing scheduling of VNF allocation and path determination for a future day, the predicted value collection unit 201 acquires the predicted values of the traffic volume and the predicted values of the maximum power consumption up to one day ahead by some prediction algorithm. Here, it is assumed that these predicted values are the average value and variance at a certain sampling interval. In this case, the average value may be set as the nominal value, and the variance may be set as the deviation of the nominal value. Note that the sampling interval is a time interval set in advance according to the control specifications of VNF allocation and path determination, such as one minute or one hour.

[0062] Note that the nominal value and its deviation collected by the predicted value collection unit 201 are passed to the control calculation unit 204.

[0063] The physical network information collection unit 202 collects information regarding the topology of the physical network and various parameters (for example, power consumption coefficients, etc.).

[0064] Note that the information and various parameters collected by the physical network information collection unit 202 are passed to the control calculation unit 204.

[0065] The command value acquisition unit 203 acquires the command value regarding power consumption. That is, the command value acquisition unit 203 acquires the information indicating the physical node to which the command value regarding power consumption is given and the command value.

[0066] The command value acquired by the command value acquisition unit 203 is passed to the control calculation unit 204.

[0067] The control calculation unit 204 executes an algorithm for solving the two-stage robust optimization problems (3) and (5) using the information collected by the prediction value collection unit 201, the information collected by the physical network information collection unit 202, and the command value acquired by the command value acquisition unit 203. That is, the control calculation unit 204 alternately repeats solving the master problem (6) and the subproblem (9) in the first stage to calculate the VNF allocation x n v,s After calculating, in the second stage, it solves the subproblem (7) to calculate the routing decision y l e,s Thereby, the VNF allocation x n v,s representing the optimal control solution of the original problems (3) and (5) and the routing decision y l e,s are obtained.

[0068] Here, the control calculation unit 204 includes a first problem solver 211 and a second problem solver 212. The first problem solver 211 calculates the solution of the master problem (6) and calculates the lower bound of the optimal solution of the original problems (3) and (5). The second problem solver 212 calculates the solutions of the subproblem (9) and the subproblem (7) and calculates the upper bound of the optimal solution of the original problems (3) and (5). For example, when performing the above-described scheduling, the control calculation unit 204 may divide the two-stage robust optimization problems (3) and (5) for each sampling time interval and execute the solution algorithm independently for each.

[0069] The control unit 205 controls the virtual network according to the control solution calculated by the control calculation unit 204. Thereby, the VNF allocation and the routing decision represented by the optimal control solution are embedded in the physical network (that is, changed to the optimal VNF allocation and routing decision).

[0070] Note that, as an example, the case of performing scheduling of VNF allocation and routing for one day in the future has been described, but this is just one of the application examples and is not limited thereto. For example, it can be similarly applied when calculating the optimal VNF allocation and route in real time and dynamically controlling the virtual network. Specifically, if it is possible to collect the predicted value of the traffic volume of each service and the predicted value of the maximum power consumption (nominal value and its deviation) at each physical node (however, the physical node for which no command value regarding power consumption is given) for each sampling point, and it is possible to obtain the command value regarding power consumption, then each time such collection and acquisition are performed, the control calculation unit 204 may execute the solution algorithm, and the control unit 205 may update the VNF allocation and route.

[0071] <Virtual Network Control Process> Next, the flow of the virtual network control process according to the present embodiment will be described with reference to FIG. 4. FIG. 4 is a flowchart showing an example of the flow of the virtual network control process according to the present embodiment. Note that the service g(V s ,E s )(s = 1, ···, N s ) to be embedded is assumed to be given to the control device 10 in advance.

[0072] First, the predicted value collection unit 201 collects the predicted value of the traffic volume of each service (nominal value and its deviation) and the predicted value of the maximum power consumption (nominal value and its deviation) at each physical node (however, the physical node for which no command value regarding power consumption is given) (step S101).

[0073] Next, the physical network information collection unit 202 collects information regarding the topology of the physical network and various parameters (for example, power consumption coefficient, etc.) (step S102). However, if the topology of the physical network and the values of various parameters have not been changed since the last collection, this step may not be executed.

[0074] Next, the command value acquisition unit 203 acquires the command value for the physical node to which the command value regarding the power consumption is given (step S103).

[0075] Subsequently, the control calculation unit 204 executes an algorithm for solving the two-stage robust optimization problems (3) and (5) using the information collected or acquired in steps S101 to S103 above, and calculates an optimal control solution (step S104). Details of this step will be described later.

[0076] Then, the control unit 205 controls the virtual network according to the control solution calculated in step S104 above (step S105).

[0077] Here, details of the calculation process of the control solution in step S104 above will be described with reference to FIG. 5. FIG. 5 is a flowchart showing an example of the flow of the calculation process of the control solution according to the present embodiment.

[0078] First, the control calculation unit 204 sets step K = 0, ψ UB (0) = ∞, and sets the initial value λ s (0) (step S201). Note that the initial value λ s (0) may be set to any value belonging to Λ s . Also, at this time, a parameter ε>0 for determining the end condition of the first stage may be set to a finite value.

[0079] Next, the control calculation unit 204 solves the master problem (6) by the first problem solver 211, and obtains the optimal solutions x n *v,s (K) and r1 * (K) and ψ LB (K) (step S202).

[0080] Next, the control calculation unit 204 solves the sub-problem (9) by the second problem solver 212, and obtains the optimal solutions λ s * (K) and ψ UBObtain (K) (step S203). At this time, the control calculation unit 204 calculates ψ UB (K) = min(ψ UB (K), ψ UB (K - 1)) to update ψ UB (K).

[0081] Next, the control calculation unit 204 uses a preset parameter ε (or the parameter ε set in step S201 above) to determine whether ψ UB (K) - ψ LB (K) ≤ ε is satisfied (step S204).

[0082] If it is not determined in step S204 above that ψ UB (K) - ψ LB (K) ≤ ε is satisfied, the control calculation unit 204 adds 1 to step K to update step K (step S205), and returns to step S202. As a result, steps S202 to S203 are repeatedly executed until ψ UB (K) - ψ LB (K) ≤ ε is satisfied.

[0083] On the other hand, if it is determined in step S204 above that ψ UB (K) - ψ LB (K) ≤ ε is satisfied, the control calculation unit 204 fixes λ s * (K), and solves the sub-problem (7) by the second problem-solving unit 212 to obtain the optimal solution y l *e,s (step S206).

[0084] Thus, the optimal solutions x n *v,s (K) and y l *e,s are obtained, which are the optimal control solutions (optimal solutions) of the original problems (3) and (5). Note that in step S206 above, it is necessary to solve the sub-problem (7), but this optimization problem can be reduced to a linear programming problem regarding only y l e,s and can be easily solved.

[0085] <Summary> As described above, under the situation where command values regarding power consumption amounts are given to at least some of the physical nodes in the physical network, the control device 10 according to the present embodiment can robustly satisfy those command values with respect to the uncertainty of traffic volume and the uncertainty of renewable energy, and realize virtual network control (VNF allocation and routing determination).

[0086] In the virtual network control process for realizing the above virtual network control, considering that there is uncertainty in the prediction regarding the traffic volume and the amount of power of renewable energy, in order to perform VNF allocation and routing determination, it is possible to robustly prevent deterioration of communication performance, congestion, cost increase due to purchase of insufficient power, increase in environmental load due to reduction in utilization rate of renewable energy, etc. that may occur due to the influence of prediction errors. In addition to this, in order to also satisfy the command regarding power consumption amount, for example, it becomes possible to respond to a demand response request or generate the amount of power sold necessary for power market transactions.

[0087] Also, the virtual network control process (particularly, the control solution calculation process) according to the present embodiment is a solution algorithm based on the theory of mathematical optimization called two-stage robust optimization. In this theory, two-stage robust optimization based on a decision-making process is performed, and it is known that a solution with reduced conservativeness can be obtained compared to simple robust optimization. This means that even if there are errors in the prediction of traffic volume and renewable energy, it is possible to suppress deterioration of communication performance and occurrence of congestion, prevent the occurrence of purchase cost of insufficient power and reduction in utilization rate of renewable energy, and perform VNF allocation and routing determination with low conservativeness.

[0088] The present invention is not limited to the above specifically disclosed embodiments, and various modifications, changes, combinations with known technologies, etc. are possible without departing from the scope of the claims.

Explanation of Reference Numerals

[0089] 10 Control device 101 Input device 102 Display device 103 External I / F 103a Recording medium 104 Communication I / F 105 Processor 106 Memory device 107 Bus 201 Predicted value collection unit 202 Physical network information collection unit 203 Command value acquisition unit 204 Control calculation unit 205 Control unit 211 First problem solving unit 212 Second problem solving unit

Claims

1. A control device for allocating a virtual network onto a physical network, based on a predicted value of the traffic volume of a service provided by the virtual network, a predicted value of the available power of a physical node constituting the physical network, information regarding the physical network, and a predetermined command value, performs allocation of virtual nodes constituting the virtual network to the physical nodes or route determination between the virtual nodes, wherein the command value is the power consumption that a physical node to which the command value is given should satisfy, or a range of the power consumption that a physical node to which the command value is given should satisfy. The control device.

2. A control device for allocating a virtual network onto a physical network, based on a predicted value of the traffic volume of a service provided by the virtual network, a predicted value of the available power of a physical node constituting the physical network, information regarding the physical network, and a predetermined command value, performs allocation of virtual nodes that are robust against prediction errors regarding both the predicted value of the traffic volume and the predicted value of the power consumption to the physical nodes or robust route determination between the virtual nodes, wherein the command value is the power consumption that a physical node to which the command value is given should satisfy, or a range of the power consumption that a physical node to which the command value is given should satisfy. The control device.

3. A control device for allocating a virtual network onto a physical network, based on a predicted value of the traffic volume of a service provided by the virtual network, a predicted value of the available power of a physical node constituting the physical network, information regarding the physical network, and a predetermined command value, performs allocation of virtual nodes constituting the virtual network to the physical nodes or route determination between the virtual nodes, wherein the command value is the power consumption that a physical node to which the command value is given should satisfy, or a range of the power consumption that a physical node to which the command value is given should satisfy. The control method.

4. A control device for allocating a virtual network onto a physical network, Based on the predicted traffic volume of the service provided by the virtual network, the predicted available power of the physical nodes constituting the physical network, the information on the physical network, and a predetermined command value, perform the allocation of virtual nodes to the physical nodes that are robust to the prediction error regarding both the predicted traffic volume and the predicted power consumption, or perform path determination between the robust virtual nodes. The command value is the power consumption that the physical node to which the command value is given should satisfy, or the range of the power consumption that the physical node to which the command value is given should satisfy. A control method.

5. In a control device that allocates a virtual network onto a physical network, Based on the predicted traffic volume of the service provided by the virtual network, the predicted available power of the physical nodes constituting the physical network, the information on the physical network, and a predetermined command value, execute a process for allocating the virtual nodes constituting the virtual network to the physical nodes or for determining a path between the virtual nodes. The command value is the power consumption that the physical node to which the command value is given should satisfy, or the range of the power consumption that the physical node to which the command value is given should satisfy. A program.

6. In a control device that allocates a virtual network onto a physical network, Based on the predicted traffic volume of the service provided by the virtual network, the predicted available power of the physical nodes constituting the physical network, the information on the physical network, and a predetermined command value, execute a process for allocating virtual nodes that are robust to the prediction error regarding both the predicted traffic volume and the predicted power consumption to the physical nodes, or perform path determination between the robust virtual nodes. The command value is the power consumption that the physical node to which the command value is given should satisfy, or the range of the power consumption that the physical node to which the command value is given should satisfy. A program.

7. A control device for embedding a virtual network that realizes service provision onto a physical network, A first acquisition unit configured to acquire a predicted value of the traffic volume of the service and a predicted value of the available power of physical nodes that have not been given command values regarding power consumption among the physical nodes constituting the physical network; A second acquisition unit configured to acquire information regarding the physical network; A third acquisition unit configured to acquire the command values given to at least some of the physical nodes constituting the physical network; Based on the predicted value of the traffic volume, the predicted value of the power consumption, the information regarding the physical network, and the command values, with the constraint that the power consumption of the physical nodes given the command values satisfies the command values, a solution calculation unit configured to calculate an optimal solution to a two-stage robust optimization problem regarding the allocation of virtual nodes of the virtual network to physical nodes and the path determination between the virtual nodes; A control unit configured to control a virtual network embedded in the physical network based on the allocation and path determination of virtual nodes represented by the optimal solution; having; The control device, wherein the command value is the power consumption that the physical node given the command value should satisfy, or the range of the power consumption that the physical node given the command value should satisfy.

8. The predicted value of the traffic volume is represented by a nominal value of the traffic volume and a deviation from the nominal value, The control device according to claim 7, wherein the predicted value of the power consumption is represented by a nominal value of the power consumption of the physical nodes that have not been given the command value and a deviation from the nominal value.

9. The solution calculation unit Based on the column-and-constraint generation method, after decomposing the two-stage robust optimization problem into a master problem and a subproblem, in the first stage, the master problem and the subproblem are alternately solved to calculate an optimal solution regarding the allocation of virtual nodes, and in the second stage, the subproblem is solved to calculate an optimal solution regarding the path determination. The control device according to claim 7 or 8.

10. A control device that embeds a virtual network for realizing service provision on a physical network A first acquisition procedure for acquiring a predicted value of the traffic volume of the service and a predicted value of the available power consumption of physical nodes in the physical network for which no command value regarding power consumption is given among each physical node constituting the physical network; A second acquisition procedure for acquiring information regarding the physical network; A third acquisition procedure for acquiring the command values given to at least some of the physical nodes among each physical node constituting the physical network; Based on the predicted value of the traffic volume, the predicted value of the power consumption, the information regarding the physical network, and the command value, with the constraint that the power consumption of the physical node given the command value satisfies the command value, a solution calculation procedure for calculating an optimal solution to a 2-stage robust optimization problem regarding the allocation of virtual nodes constituting the virtual network to physical nodes and the path determination between the virtual nodes; A control procedure for controlling the virtual network embedded in the physical network based on the allocation and path determination of the virtual nodes represented by the optimal solution; Execute, The command value is the power consumption that the physical node given the command value should satisfy, or the range of the power consumption that the physical node given the command value should satisfy. A control method.

11. A program for causing a computer to function as the control device according to any one of Claims 7 to 9.

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