Failure location estimation device, failure location estimation method, and failure location estimation program

Abstract

This failure location estimation device comprises: a network configuration information database that manages network configuration information of a multilayer network; a rule-based learning control unit that defines, as a rule, a relationship between an alarm characterizing a failure and a failure location / cause; a rule database that manages the rules defined by the rule-based learning control unit; and a failure location estimation function unit that estimates a failure location / cause using an alarm group generated from the multilayer network and the rules defined by the rule-based learning control unit. The failure location estimation device also has a correlation range derivation function of, on the basis of the network configuration information, converting the multilayer network into a network model from which associated information for defining a relationship between layers is excluded, and deriving the correlation range of a logical resource for the network model. The rule-based learning control unit and the failure location estimation function unit perform rule-based learning and failure location estimation by a rule-based learning type failure location estimation technology on the basis of the correlation range.

Description

Fault location estimation device, fault location estimation method, and fault location estimation program

[0001] The present invention relates to a fault location estimation device, a fault location estimation method, and a fault location estimation program.

[0002] When a fault occurs in the network, an alarm is sent, and maintenance personnel must analyze the alarm and isolate the faulty part.

[0003] As a support technology for network maintenance and operation, for example, a rule-learning type fault location estimation technology that autonomously estimates the fault location is known. In the rule-learning type fault location estimation technology, when learning the rules, the relationship between the alarms that characterize the fault and the fault location / cause is defined as a rule, and when estimating the fault location, the fault location / cause is estimated using the group of alarms that have occurred and the defined rule. A technology that automates the creation of rules is known (for example, see Patent Document 1).

[0004] Rule learning-based fault location estimation technology is based on the idea that "an alarm that occurs in a resource that is physically or logically distant from the fault location has no correlation with the fault," and calculates in advance the correlation range of all resources (the range in which an alarm may occur if the resource in question fails), and then performs rule learning and fault location estimation processing based on that correlation range.

[0005] Japanese Patent No. 6637854

[0006] When modeling a multi-layer network consisting of multiple layers, it is necessary to retain bundling information (communication connections and communication termination points) to define the relationships between each layer. If rule learning-based fault location estimation technology is applied to a model that also includes bundling information, and rule learning and fault location estimation are performed by directly determining the correlation range, the number of resources to be searched becomes enormous, resulting in an increase in processing load and a long estimation processing time.

[0007] The present invention has been made in light of the above-mentioned circumstances, and its object is to provide a fault location estimation device, a fault location estimation method, and a fault location estimation program that enable a reduction in the processing load and the estimation processing time during rule learning and fault location estimation when rule learning-type fault location estimation technology is applied to a multilayer network made up of multiple layers.

[0008] One aspect of the present invention is a fault location estimation device that applies a rule learning-based fault location estimation technique to a multilayer network composed of multiple layers to perform rule learning and fault location estimation. The fault location estimation device includes a network configuration information database that manages network configuration information of the multilayer network, a rule learning control unit that defines, as rules, the relationships between alarms that characterize faults and the fault locations and causes, a rule database that manages the rules defined by the rule learning control unit, and a fault location estimation function unit that estimates the fault location and cause using alarms generated from the multilayer network and the rules defined by the rule learning control unit. The fault location estimation device also has a correlation range derivation function that converts the multilayer network into a network model that excludes bundling information that defines the relationships between each layer, based on the network configuration information, and derives correlation ranges of logical resources for the network model. The rule learning control unit and the fault location estimation function unit perform rule learning and fault location estimation using the rule learning-based fault location estimation technique based on the correlation ranges.

[0009] One aspect of the present invention is a fault location estimation method that applies a rule learning fault location estimation technique to a multilayer network composed of multiple layers to perform rule learning and fault location estimation. The fault location estimation method includes a learning step of defining, as rules, relationships between alarms that characterize faults and fault locations / causes, and an estimation step of estimating the fault location / cause using alarms generated from the multilayer network and the rules defined in the learning step. The learning step and estimation step include the steps of converting the multilayer network into a network model that excludes bundling information that defines the relationships between each layer, based on network configuration information of the multilayer network, deriving correlation ranges of logical resources for the network model, and performing rule learning and fault location estimation using the rule learning fault location estimation technique based on the correlation ranges.

[0010] One aspect of the present invention is a fault location estimation program that causes a computer to execute at least some of the functions of the fault location estimation device described above.

[0011] According to the present invention, there are provided a fault location estimation device, a fault location estimation method, and a fault location estimation program that enable a reduction in the processing load and the estimation processing time during rule learning and fault location estimation when a rule learning type fault location estimation technique is applied to a multilayer network made up of multiple layers.

[0012] FIG. 1 is a block diagram showing an example of a fault location estimation system and a network NW to be monitored according to an embodiment. FIG. 2 is a block diagram showing an example of the hardware configuration of a fault location estimation device according to an embodiment. FIG. 3 is a block diagram showing an example of the functional configuration of a fault location estimation device according to an embodiment. FIG. 4 is a diagram showing a model definition related to a resource management model according to an embodiment. FIG. 5 is a diagram showing an example of a resource management model based on the model definition shown in FIG. 4. FIG. 6 is a diagram for explaining an example of a parent-child relationship in a network configuration. FIG. 7 is a first part of a flowchart showing the flow of a correlation range derivation process performed by a fault location estimation device (rule learning control unit and fault location estimation function unit) according to an embodiment. FIG. 8 is a second part of a flowchart showing the flow of a correlation range derivation process performed by a fault location estimation device (rule learning control unit and fault location estimation function unit) according to an embodiment. FIG. 9 is a third part of a flowchart showing the flow of a correlation range derivation process performed by a fault location estimation device (rule learning control unit and fault location estimation function unit) according to an embodiment. FIG. 10 is a diagram showing an example of a resource management model read out from the network configuration information database by the fault location estimation function unit in step S11. FIG. 11 is a diagram showing a directed graph of logical resources created by the fault location estimation function unit in step S12 based on the resource management model shown in FIG. 10. FIG. 12 is a diagram showing a directed graph of logical resources indicating the determination result of step S15. FIG. 13 is a diagram showing a directed graph indicating the result of repeatedly performing the processes of steps S12 to S17 until there are no LC / CP / XCs in the directed graph. FIG. 14 is a diagram showing a directed graph indicating the result of repeatedly performing the processes of steps S19 to S22 until there are no resources in the correlation range that have not been checked for the same communication section. FIG. 15 is a diagram showing a directed graph indicating the result of repeatedly performing the processes of steps S23 to S26 until there are no resources that have not been checked at the higher level. FIG. 16 is a diagram showing a directed graph indicating the result of repeatedly performing the processes of steps S27 to S30 until there are no resources that have not been checked at the lower level.Fig. 17 is a diagram showing a directed graph indicating the results of repeatedly performing the processes of steps S19 to S31 until the same communication section check, upper level check, and lower level check are completed for all correlation range resources. Fig. 18 is a diagram showing a directed graph indicating the results of repeatedly performing the processes of steps S32 to S34 until there are no physical resources corresponding to communication ports that have not yet been checked for the physical layer. Fig. 19 is a diagram showing the correlation range finally derived based on the directed graph shown in Fig. 13.

[0013] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0014] (Fault Location Estimation System) First, a fault location estimation system 1 according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the fault location estimation system 1 according to an embodiment and a network NW to be monitored.

[0015] The failure location estimation system 1 estimates the location of a failure that has occurred in a network NW, for example. The failure location estimation system 1 includes a monitoring device 2 and a failure location estimation device 3.

[0016] The network NW includes, for example, a plurality of physical devices PD (physical devices) connected to each other. When a failure occurs in any of the physical devices PD, a physical device PD existing within a correlation range issues an alarm. The network NW is a multi-layer network configured with multiple layers in which a plurality of logical layers are hierarchically stacked above a physical layer.

[0017] The monitoring device 2 is configured by a computer, for example, a server computer. The monitoring device 2 monitors the state of the network NW. When a failure occurs in the network NW, the monitoring device 2 collects alarms generated in the network NW. The monitoring device 2 passes the collected alarms to the failure location estimation device 3.

[0018] The fault location estimation device 3 is configured by a computer, for example, a server computer. The fault location estimation device 3 performs rule learning and fault location estimation for a multi-layer network configured by multiple layers, using a rule learning type fault location estimation technique.

[0019] (Hardware Configuration of Fault Location Estimation Device) Next, a hardware configuration of the fault location estimation device according to the embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of the hardware configuration of the fault location estimation device according to the embodiment.

[0020] As shown in FIG. 2, the failure location estimation device 3 includes a control circuit 11 , a communication module 12 , a user interface 13 , a storage 14 , a drive 15 , and a storage medium 16 .

[0021] The control circuit 11 is a circuit that controls the overall components of the fault location estimation device 3. The control circuit 11 includes a CPU (Central Processing Unit), RAM (Random Access Memory), and ROM (Read Only Memory). The ROM of the control circuit 11 stores programs and the like used in various processes in the fault location estimation device 3. The CPU of the control circuit 11 controls the entire fault location estimation device 3 in accordance with the programs stored in the ROM of the control circuit 11. The RAM of the control circuit 11 is used as a work area for the CPU of the control circuit 11.

[0022] The communication module 12 is, for example, a circuit used for communication with the monitoring device 2. The user interface 13 is an interface that manages communication between the user and the control circuit 11. The user interface 13 includes input devices and output devices. The input devices include, for example, a keyboard, a touch panel, and operation buttons. The output devices include, for example, an LCD (Liquid Crystal Display) or an EL (Electroluminescence) display. The user interface 13 converts input from the user into an electrical signal and then transmits it to the control circuit 11. The user interface 13 outputs the execution result based on the user input to the user.

[0023] The storage 14 includes, for example, a hard disk drive (HDD) or a solid state drive (SSD). The storage 14 stores programs, information, and the like used in various processes in the failure location estimation device 3.

[0024] The drive 15 is a device for reading software stored in the storage medium 16. The drive 15 includes, for example, a CD (Compact Disk) drive and a DVD (Digital Versatile Disk) drive.

[0025] The storage medium 16 is a medium that stores software electrically, magnetically, optically, mechanically, or chemically. The storage medium 16 may store programs for executing various processes in the failure location estimation device 3.

[0026] For example, the storage medium 16 stores a failure location estimation program that causes the control circuit 11 to execute the functions of the failure location estimation device 3. The control circuit 11 non-temporarily stores the failure location estimation program stored in the storage medium 16 in the storage 14. The control circuit 11 also loads the failure location estimation program stored in the storage 14 into RAM and executes it. In this way, the control circuit 11 executes the functions of the failure location estimation device 3 in cooperation with other elements.

[0027] (Functional Configuration of Fault Location Estimation Device) Next, the functional configuration of the fault location estimation device 3 according to the embodiment will be described with reference to Fig. 3. Fig. 3 is a block diagram showing an example of the functional configuration of the fault location estimation device 3 according to the embodiment. The fault location estimation device 3 is a device that applies a rule learning type fault location estimation technique to a multilayer network made up of multiple layers to perform rule learning and fault location estimation.

[0028] The fault location estimation device 3 has a data acquisition unit 31, a rule learning control unit 32, a fault location estimation function unit 33, a countermeasure management function unit 34, an alarm information database 36, a network configuration information database 37, a rule database 38, a fault history / countermeasure history database 39, a GUI unit 35, and an API unit 40.

[0029] The data acquisition unit 31 acquires network configuration information of the network NW to be monitored, to which the rule learning type fault location estimation technology is applied, from the monitoring device 2 or other devices. The data acquisition unit 31 also acquires an alarm group, which is a plurality of pieces of alarm information issued from the network NW to be monitored, from the monitoring device 2.

[0030] The network configuration information database 37 manages the network configuration information acquired by the data acquisition unit 31. The network configuration information database 37 also manages all resource information included in each resource as attributes.

[0031] The alarm information database 36 manages the alarm information acquired by the data acquisition unit 31 .

[0032] During rule learning, the rule learning control unit 32 defines, as rules, the relationship between alarms that characterize failures in the network NW and the location and cause of the failure. More specifically, in order to reduce the resources to be searched during rule learning, the rule learning control unit 32 converts the network NW, which is a multi-layer network, into a network model (resource management model) that excludes bundling information for defining the relationship between each layer, based on the network configuration information acquired from the network configuration information database 37, derives correlation ranges of logical resources for the network model (resource management model), and performs rule learning using rule learning-type failure location estimation technology based on the correlation ranges.

[0033] The rule database 38 manages the rules defined by the rule learning control unit 32 .

[0034] During rule learning, the failure location estimation function unit 33 estimates the location and cause of a failure by using a group of alarms generated from the multi-layer network and rules defined by the rule learning control unit 32. More specifically, in order to reduce the resources to be searched during rule learning, the failure location estimation function unit 33 converts the network NW, which is a multi-layer network, into a network model (resource management model) excluding bundling information for defining the relationship between each layer, based on the network configuration information acquired from the network configuration information database 37, derives a correlation range of logical resources for the network model (resource management model), and estimates the failure location using a rule learning type failure location estimation technique based on the correlation range.

[0035] The countermeasure management function unit 34 writes new countermeasures for failures into the failure history / countermeasure history database 39 according to input operations by the operator, and reads out existing countermeasure histories from the failure history / countermeasure history database 39 .

[0036] The failure history / countermeasure history database 39 manages the countermeasures written by the failure history / countermeasure history database 39. The failure history / countermeasure history database 39 also manages the failure history. The failure history may be, for example, an estimation result by the failure location estimation function unit 33 or information input by an operator via the countermeasure management function unit 34.

[0037] The GUI unit 35 has a function of creating and controlling a GUI, and receives information and instructions from an operator via the GUI, and presents information, such as an estimated fault location, to the operator.

[0038] The API unit 40 has a function of controlling the API, and is capable of communicating information and instructions with external devices via the API.

[0039] As can be seen from the above, the rule learning control unit 32 and the failure location estimation unit 33 have a correlation range derivation function that converts the network NW into a resource management model from which bundling information has been excluded, and derives the correlation range of logical resources for the resource management model. This correlation range derivation function may be possessed by the network configuration information database 37 instead of by the rule learning control unit 32 and the failure location estimation unit 33, and the network configuration information database 37 may provide the rule learning control unit 32 and the failure location estimation unit 33 with the derived correlation range of logical resources.

[0040] In other words, the failure point estimation device 3 has a correlation range derivation function that converts the network NW into a resource management model excluding bundling information and derives the correlation range of logical resources for the resource management model. The rule learning control unit 32 and the failure point estimation function unit 33 have this correlation range derivation function. Also, this correlation range derivation function may be possessed by the network configuration information database 37 instead of by the rule learning control unit 32 and the failure point estimation function unit 33.

[0041] (Model Definition Related to Resource Management Model) Fig. 4 is a diagram showing a model definition related to a resource management model according to an embodiment. Fig. 4 shows an example of a resource management model in which a network is defined based on a unified standard including a physical layer and a logical layer.

[0042] In FIG. 4, entities such as PPort (Physical Port), PLink (Physical Link), EQP (Equipment), EH (Equipment Holder), and PD (Physical Device) are defined in the physical layer.

[0043] A PPort is a physical port of a network device. There are also virtual PPorts.

[0044] A PLink represents a physical line connecting network devices, such as an optical fiber, a UTP cable, or a wireless connection.

[0045] EQP represents a network device package, card, optical module (SFP, etc.), etc. A PPort is connected to the EQP.

[0046] EH is a container for equipment, and represents a rack, chassis, slot, etc.

[0047] The PD represents the actual state of the device to be managed. The physical configuration is represented by the EH / EQP connected to the PD.

[0048] In the logical layer, entities called TPE (Termination Point Encapsulation), FRE (Forwarding Relationship Encapsulation), NFD (Network Forwarding Domain), and TL (Topological Link) are defined.

[0049] TPE represents the end point of a logical layer. TPE refers to a TPE in a lower logical layer or a PPort in a physical layer, and represents the hierarchical relationship of layers.

[0050] The TPE includes a TCP (Termination Connection Point) and a CP (Connection Point).

[0051] TCP is a termination point: TCP is responsible for terminating connectivity within the layer.

[0052] A CP is a relay point, which has the role of relaying connectivity within a layer.

[0053] FRE represents the flow of information between endpoints.

[0054] FRE includes NC (Network Connection), LC (Link Connection), and XC (Cross Connection).

[0055] NC represents the connectivity from TCP within a layer to the TCP that can be reached. The structure of the NW within a layer is represented by the subordinate LC and XC.

[0056] LC represents connectivity between devices based on the lower layer NC.

[0057] XC represents connectivity within a device based on the NFD of the lower layer, and corresponds to the routing function of a router or the switch function of a switch.

[0058] The NFD represents the transfer capability of a device, which is the basis for creating an XC. The NFD is basically used only in the Logical Device (LD) layer.

[0059] The TL represents logical connectivity between devices in the LD layer, and is located at the lowest level of the logical layer.

[0060] (Resource Management Model) Figure 5 is a diagram showing an example of a resource management model based on the above definition. As shown in Figure 5, the resource management model can be thought of as a network model in which multiple logical layers are layered above a physical layer. The parents of a physical port include EQP, EH, and PD, but these are omitted in Figure 5.

[0061] In the resource management model shown in Figure 5, the rule learning control unit 32 excludes bundling information before deriving the correlation range of each logical resource during rule learning, and the fault location estimation function unit 33 excludes bundling information before deriving the correlation range of each logical resource during fault location estimation. This reduces the resources involved in the search for the correlation range. In a multi-layer network model consisting of multiple layers, bundling information is information for defining the relationships between each layer that must be maintained. For example, the bundling information includes communication connections and communication termination points.

[0062] First, the rule learning control unit 32 and the fault location estimation function unit 33 determine the following (a) to (d) as candidates for the correlation range of each logical resource in the resource management model shown in Fig. 5. Here, the correlation range is the range within which an alarm is confirmed when a resource fails. (a) The resource itself that is the search target of the correlation range, and the communication connection and communication termination point within the same communication section as that resource. (b) The logical resource in the upper layer that includes the correlation range resource (communication connection and communication termination point). (c) The logical resource in the lower layer that constitutes the correlation range resource (communication connection and communication termination point). (d) The communication port in the physical layer corresponding to the correlation range resource, and the physical resource that is the direct parent of that physical port. The candidates for the correlation range of each logical resource (a) to (d) are the same as the correlation range of each logical resource in the conventional rule learning type fault location estimation technology.

[0063] Here, the parent-child relationship in the network configuration will be described with reference to Fig. 6. Fig. 6 is a diagram for explaining an example of the parent-child relationship in the network configuration.

[0064] This example shows the parent-child relationships in physical resources as a prerequisite. In physical resources, the components of each device form a tree structure, and the parent-child relationships are defined in this tree structure. For example, from the perspective of a communication port, the package to which the communication port belongs and the chassis to which the package belongs are the direct parents from the perspective of the communication port.

[0065] In Figure 6, the left side of the arrow (→) is the parent node and the right side is the child node. In physical resources, a child node does not have multiple parent nodes. For example, in a box-type device, PD is the parent node of PPort.

[0066] When managing a chassis / card, the PD is positioned as the parent of the chassis, the chassis is positioned as the parent of the card, and the card is positioned as the parent of the PPort.

[0067] In a large-scale device such as a transmission device, the PD is positioned as the parent of the Rack, the Rack is positioned as the parent of the Shelf, the Shelf is positioned as the parent of the Card, the Card is positioned as the parent of the Module, and the Module is positioned as the parent of the PPort.

[0068] When the connection relationships for each layer of logical resources are utilized, the connectivity between communication end points in the upper layer is composed of a plurality of combinations of the connectivity between communication end points of information transfer in the lower layer.

[0069] Next, the rule learning control unit 32 and the fault location estimation function unit 33 preliminarily exclude the following bundling information (communication connections and communication termination points) for defining the relationships between the layers in the resource management model shown in FIG.

[0070] LC: Indicates connectivity between devices based on the lower layer NC or TL, and is excluded because it is not directly related to the communication flow of each layer.

[0071] XC: This indicates connectivity within a device based on the NFD that expresses the device's transfer capability, and is excluded because it is not directly related to the transfer function.

[0072] CP: Indicates connectivity within a layer based on the lower layer TCP, and is excluded because it is not directly related to the termination function of each layer.

[0073] 7 to 9 are flowcharts showing the flow of the correlation range derivation process performed by the fault location estimation device 3 (rule learning control unit 32 and fault location estimation function unit 33) according to the embodiment. The correlation range derivation process excludes resources that define relationships between layers in the management model, and uses a directed graph in which new relationships are redefined. The excluded resources are LC, CP, and XC. For convenience, LC, CP, and XC will be referred to below as "LC / CP / XC."

[0074] Fig. 10 is a diagram showing an example of a resource management model. Figs. 11 to 19 are diagrams showing directed graphs of logical resources in the middle of the correlation range derivation process. In Figs. 10 to 19, EQP, EH, and PD are parents of physical ports and can become correlation ranges, but because they are not directly related to the correlation range derivation process, EQP, EH, and PD are not shown in Figs. 10 to 19.

[0075] The correlation range derivation process is performed by the rule learning control unit 32 when learning a rule, and by the failure location estimation function unit 33 when estimating a failure location. For convenience, the following description will be given assuming that the correlation range derivation process is performed by the failure location estimation function unit 33. Furthermore, instead of being performed by the rule learning control unit 32 and the failure location estimation function unit 33, the correlation range derivation process may be performed within the network configuration information database 37.

[0076] In step S 11 , the failure location estimation function unit 33 reads out a resource management model from the network configuration information database 37 .

[0077] FIG. 10 is a diagram showing an example of the resource management model read out from the network configuration information database 37 by the failure location estimation function unit 33 in step S11.

[0078] In step S12, the failure location estimation function unit 33 creates a directed graph of logical resources based on the resource management model shown in Fig. 10. Alternatively, the failure location estimation function unit 33 updates the directed graph of logical resources through the processes of steps S14 to S17, which will be described later.

[0079] Fig. 11 is a diagram showing a directed graph of logical resources created by the failure location estimation function unit 33 in step S12 based on the resource management model shown in Fig. 10. In the directed graph of logical resources shown in Fig. 11, long-dashed arrows are edges showing the hierarchical relationship between FRE and TL, short-dashed arrows are edges showing the relationship between FRE, TL and TPE, solid arrows are edges showing the hierarchical relationship between TPE, dot-dash ellipses are nodes showing resources (LC / XC / CP) used to define the relationship between logical layers, and solid ellipses are nodes showing resources that are the location of a failure and the location where an alarm occurs.

[0080] In a directed graph, hierarchical relationships across layers can be identified as different edges. Specifically, the relationship between "TPE of logical layer N" and "TPE of logical layer N-1," the relationship between "FRE of logical layer N" and "FRE of logical layer N-1," and the connection relationship between the same layers (the connection relationship between TPE and FRE) can be identified as different edges.

[0081] In step S13, the failure location estimation function unit 33 determines whether or not there is an LC / CP / XC in the directed graph.

[0082] If the result of the determination in step S13 is that there is an LC / CP / XC in the directed graph (YES in step S13), the failure location estimation function unit 33 acquires the resources of the LC / CP / XC in step S14.

[0083] Next, in step S15, the fault location estimation function unit 33 determines whether or not both an incoming edge and an outgoing edge indicating a hierarchical relationship exist for the LC / CP / XC node. If the determination result in step S15 indicates that both an incoming edge and an outgoing edge exist (YES in step S15), the process proceeds to step S16. Conversely, if neither an incoming edge nor an outgoing edge exists (NO in step S15), the process skips step S16 and proceeds to step S17.

[0084] 12 is a diagram showing a directed graph of logical resources indicating the determination result of step S15. In the directed graph shown in Fig. 12, nodes surrounded by short-dashed rounded rectangles represent LC / CP / XC nodes that have both incoming and outgoing edges for edges indicating hierarchical relationships, and nodes surrounded by long-dashed rounded rectangles represent other LC / CP / XC nodes.

[0085] In step S16, for LC / CP / XC nodes that have both incoming and outgoing edges (i.e., nodes surrounded by short-dashed rounded rectangles in the directed graph shown in Figure 12), the fault location estimation function unit 33 newly connects the source end point of the incoming edge and the destination end point of the outgoing edge with an edge that represents a hierarchical relationship.

[0086] Next, in step S17, the failure location estimation function unit 33 deletes the node and the edges that have contact points with the node. That is, it deletes the LC / CP / XC node that has both an incoming edge and an outgoing edge (i.e., the node surrounded by a short-dashed rounded rectangle in the directed graph shown in FIG. 12) and the edges that have contact points with this node.

[0087] Thereafter, the failure location estimation function unit 33 returns to the process of step S12 and updates the directed graph of the logical resources, thereby excluding resources (LC / CP / XC) that define relationships between logical layers, and creating a directed graph in which new relationships are redefined.

[0088] The processes of steps S12 to S17 are performed for all LCs / CPs / XCs. That is, the failure location estimation function unit 33 repeatedly performs the processes of steps S12 to S17 in the process of step S13 until there are no more LCs / CPs / XCs in the directed graph (the determination result in step S13 becomes NO).

[0089] 13 is a diagram showing a directed graph that shows the results of repeatedly performing the processes of steps S12 to S17 until there are no more LCs / CPs / XCs in the directed graph. In other words, FIG. 13 shows a directed graph in which all resources (LCs / CPs / XCs) that define relationships between logical layers are excluded and new relationships are redefined.

[0090] Next, in step S18, the failure point estimation function unit 33 selects correlation range search target resources and saves the selected correlation range search target resources as correlation range resources.

[0091] Subsequently, in step S19, the failure point estimation function unit 33 determines whether or not there is a resource in the correlation range that has not yet been checked for the same communication section.

[0092] If the result of the judgment in step S19 is that there is a resource in the correlation range that has not been checked for the same communication section (YES in step S19), in step S20, the failure location estimation function unit 33 acquires the correlation range resource that has not been checked for the same communication section.

[0093] Subsequently, in step S20, the failure point estimation function unit 33 saves correlation range resources that have not been checked in the same communication section, and communication connections and communication termination points in the same communication section, as correlation range resources.

[0094] In step S21, the failure location estimation function unit 33 changes the resource (that is, the resource determined in step S19 as not having been checked for the same communication section) to a resource determined as having been checked for the same communication section.

[0095] Thereafter, the failure location estimation function unit 33 returns to the processing of step S19. The processing of steps S20 to S22 is performed for all resources for which the same communication section has not been checked. That is, the failure location estimation function unit 33 repeatedly performs the processing of steps S19 to S22 until there are no resources for which the same communication section has not been checked in the processing of step S19 (the determination result in step S19 becomes NO).

[0096] 14 is a diagram showing a directed graph indicating the results of repeatedly performing the processes of steps S19 to S22 until there are no resources in the correlation range resource that have not yet been checked for the same communication section. In the directed graph shown in FIG. 14, the correlation range search target resource and the communication connections (NC) and communication termination points (TCP) in the same communication section as the resource in the same logical layer (N-1) are recorded in the correlation range resource.

[0097] Next, in step S23, the failure point estimation function unit 33 determines whether or not there is an upper-level resource that has not been checked in the correlation range resources.

[0098] If the result of the determination in step S23 is that there is a resource in the correlation range that has not been checked above (YES in step S23), in step S24 the failure point estimation function unit 33 acquires the correlation range resource that has not been checked above.

[0099] Subsequently, in step S25, the failure point estimation function unit 33 saves all the source side end points of the incoming edges of the upper level unchecked resources as correlation range resources.

[0100] In step S26, the failure point estimation function unit 33 changes the resource (i.e., the resource determined in step S23 as not having been checked by the upper level) to a resource that has been checked by the upper level.

[0101] Thereafter, the failure location estimation function unit 33 returns to the processing of step S23. The processing of steps S23 to S26 is performed for all resources that have not been checked from the higher level. That is, the failure location estimation function unit 33 repeatedly performs the processing of steps S23 to S26 in the processing of step S23 until there are no more resources that have not been checked from the higher level (the determination result in step S23 becomes NO).

[0102] 15 is a diagram showing a directed graph that indicates the results of repeatedly performing the processes of steps S23 to S26 until there are no more unchecked higher-level resources. In the directed graph shown in FIG. 15, "TCP in logical layer N," which is enclosed in a short-dashed rounded rectangle, is the source endpoint of the incoming edge of "TCP in logical layer N-1," which is a correlation range resource, and is therefore saved as a new correlation range resource. Also, "NC in logical layer N," which is enclosed in a short-dashed rounded rectangle, is the source endpoint of the incoming edge of "NC in logical layer N-1," which is a correlation range resource, and is therefore saved as a new correlation range resource.

[0103] Next, in step S27, the failure point estimation function unit 33 determines whether or not there is an unchecked lower resource in the correlation range resource.

[0104] If the result of the determination in step S27 is that there is a resource in the correlation range that has not been checked below (YES in step S27), the failure point estimation function unit 33 acquires the correlation range resource that has not been checked below (YES in step S28).

[0105] Subsequently, in step S29, the failure point estimation function unit 33 stores all the leading end points of the outgoing edges of the unchecked lower level resources as correlation range resources.

[0106] In step S30, the failure point estimating function unit 33 changes the status of the resource (i.e., the resource determined in step S27 as not having been checked at the lower level) to that of having been checked at the lower level.

[0107] Thereafter, the failure location estimation function unit 33 returns to the processing of step S27. The processing of steps S27 to S30 is performed for all unchecked lower-level resources. That is, the failure location estimation function unit 33 repeatedly performs the processing of steps S27 to S30 in the processing of step S27 until there are no unchecked lower-level resources (the determination result in step S27 becomes NO).

[0108] 16 is a diagram showing a directed graph that indicates the results of repeatedly performing the processes of steps S27 to S30 until there are no unchecked lower-level resources. In the directed graph shown in FIG. 16, "TCP in logical layer 0," which is enclosed in a short-dashed rounded rectangle, is the leading end point of the outgoing edge of "TCP in logical layer N-1," which is a correlation range resource, and is therefore saved as a new correlation range resource.

[0109] Furthermore, since the "NFD of logical layer 0" surrounded by the short-dashed rounded rectangle is the leading end point of the outgoing edge of the "NC of logical layer N-1" which is a correlation range resource, it is saved as a new correlation range resource.

[0110] Furthermore, since "TL of logical layer 0", which is surrounded by a short-dashed rounded rectangle, is the leading end point of the outgoing edge of "NC of logical layer N-1", which is a correlation range resource, it is saved as a new correlation range resource.

[0111] Furthermore, since "TCP on logical layer 0", which is enclosed in a dashed-dotted rounded rectangle, is the leading end point of the outgoing edge of "TCP on logical layer N-1", which is a correlation range resource, it is saved as a new correlation range resource.

[0112] Furthermore, "NFD of logical layer 0", which is surrounded by a dashed-dotted rounded rectangle, is the leading end point of the outgoing edge of "NC of logical layer N", which is a correlation range resource, and is therefore saved as a new correlation range resource.

[0113] Furthermore, since the "NC of logical layer N-1" surrounded by the rounded dashed rectangle is the leading end point of the outgoing edge of the "NC of logical layer N" which is the correlation range resource, it is saved as a new correlation range resource.

[0114] Furthermore, "TL of logical layer 0", which is surrounded by a rounded rectangle with dashed lines, is the leading end point of the outgoing edge of "NC of logical layer N-1", which has been newly saved as a correlation range resource, and therefore has been saved as a new correlation range resource.

[0115] Next, in step S31, the failure point estimation function unit 33 determines whether the same communication section check, upper level check, and lower level check have been completed for all correlation range resources.

[0116] If the result of the determination in step S31 shows that the same communication section check, upper level check, and lower level check have not been completed for all of the correlation range resources (NO in step S31), the failure location estimation function unit 33 returns to the processing in step S19 and repeats the processing in steps S19 to S31. That is, in the processing in step S31, the failure location estimation function unit 33 repeats the processing in steps S19 to S31 until the same communication section check, upper level check, and lower level check have been completed for all of the correlation range resources (YES in step S31).

[0117] 17 is a diagram showing a directed graph indicating the results of repeatedly performing the processes of steps S19 to S31 until the same communication section check, upper level check, and lower level check have been completed for all correlation range resources. In the directed graph shown in FIG. 17, "TCP of logical layer 0," which is enclosed in a short-dashed rounded rectangle, is the end point of the same section as the correlation range resource "TL of logical layer 0," and is therefore saved as a new correlation range resource.

[0118] Furthermore, since "TCP of logical layer n-1", which is enclosed in a long-dashed rounded rectangle, is the end point of the same section as "NC of logical layer N-1", which is a correlation range resource, it is saved as a new correlation range resource.

[0119] Furthermore, "TCP of logical layer 0" enclosed in a long-dashed rounded rectangle is the end point of the same section as "TL of logical layer 0," which is a correlation range resource, and is therefore saved as a new correlation range resource.

[0120] Furthermore, "TCP of logical layer N", which is enclosed in a dashed-dotted rounded rectangle, is the end point of the same section as "NC of logical layer N", which is a correlation range resource, and is therefore saved as a new correlation range resource.

[0121] Furthermore, since "TCP of logical layer N-1", which is enclosed in a dashed-dotted rounded rectangle, is the end point of the same section as "NC of logical layer N-1", which is a correlation range resource, it is saved as a new correlation range resource.

[0122] Furthermore, "TCP of logical layer 0" enclosed in a dashed-dotted rounded rectangle is the end point of the same section as "TL of logical layer 0," which is a correlation range resource, and is therefore saved as a new correlation range resource.

[0123] Next, in step S32, the failure point estimation function unit 33 determines whether or not there is a communication endpoint corresponding to a communication port that has not yet been checked in the physical layer within the correlation range.

[0124] If the result of the determination in step S32 is that there is a communication endpoint corresponding to a communication port that has not been checked in the physical layer within the correlation range (YES in step S32), the fault location estimation function unit 33 saves the corresponding communication port in the physical layer and the physical resource that is the direct parent of that physical port as correlation range resources in step S33.

[0125] Next, in step S34, the failure point estimation function unit 33 changes the resource (i.e., the physical resource corresponding to the communication port for which the physical layer has not been checked) to one for which the physical layer has been checked.

[0126] Thereafter, the failure location estimation function unit 33 returns to the processing of step S32. The processing of steps S32 to S34 is performed for all physical resources corresponding to communication ports that have not yet been checked in the physical layer. That is, the failure location estimation function unit 33 repeatedly performs the processing of steps S32 to S34 in the processing of step S32 until there are no physical resources corresponding to communication ports that have not yet been checked in the physical layer (the determination result in step S32 is NO).

[0127] 18 is a diagram showing a directed graph indicating the results of repeatedly performing the processes of steps S32 to S34 until there are no more physical resources corresponding to communication ports that have not yet been checked in the physical layer. In the directed graph shown in FIG. 18, the "PPort of the physical layer" enclosed in a short-dashed rounded rectangle is a communication port related to a logical resource within the correlation range, and is therefore saved as a correlation range resource.

[0128] In the process of step S32, when there are no more physical resources corresponding to communication ports that have not been checked in the physical layer, that is, when the determination result in step S32 is NO, the process of deriving the correlation range ends.

[0129] Fig. 19 is a diagram showing a directed graph representing the finally derived correlation range. In detail, Fig. 19 is a diagram showing the finally derived correlation range based on the directed graph shown in Fig. 13 in which resources defining relationships between layers are excluded from the management model shown in Fig. 10 and new relationships are redefined.

[0130] Thereafter, the failure location estimation function unit 33 estimates the failure location by the rule learning type failure location estimation technique using the derived correlation range.

[0131] (Effect) In the embodiment, the network NW, which is a multi-layer network, is converted into a resource management model excluding bundling information for defining the relationships between each layer, and correlation ranges of logical resources are derived for the converted resource management model. Then, rule learning and fault location estimation are performed using a rule learning fault location estimation technique based on the derived correlation ranges. This significantly reduces the number of resources to be searched during rule learning and fault location estimation. As a result, it is possible to reduce the processing load and shorten the estimation processing time during rule learning and fault location estimation.

[0132] In other words, a fault location estimation device, a fault location estimation method, and a fault location estimation program are provided that enable a reduction in the processing load and the estimation processing time when rule learning and fault location estimation technology is applied to a multi-layer network consisting of multiple layers.

[0133] The embodiments of the present invention have been described above with reference to the drawings. However, the above embodiment is merely an example of a configuration that embodies the present invention. That is, it is clear that the present invention is not limited to the above embodiment. Therefore, addition, omission, substitution, and other modifications of components may be made within the scope of the technical concept of the present invention.

[0134] In short, the present invention is not limited to the above-described embodiments, and various modifications can be made in the implementation stage without departing from the spirit of the invention. Furthermore, the embodiments may be implemented in appropriate combinations, in which case the combined effects can be obtained. Furthermore, the above-described embodiments include various inventions, and various inventions can be extracted by combining selected elements from the disclosed elements. For example, if the problem can be solved and the desired effect can be obtained even if some elements are deleted from all elements shown in the embodiments, the configuration from which these elements are deleted can be extracted as an invention.

[0135] DESCRIPTION OF SYMBOLS 1...Fault location estimation system 2...Monitoring device 3...Fault location estimation device 11...Control circuit 12...Communication module 13...User interface 14...Storage 15...Drive 16...Storage medium 31...Data acquisition unit 32...Rule learning control unit 33...Fault location estimation function unit 34...Countermeasure management function unit 35...GUI unit 36...Alarm information database 37...Network configuration information database 38...Rule database 39...Fault history and countermeasure history database 40...API unit

Claims

1. A fault location estimation device that applies rule learning type fault location estimation technology to a multi-layer network composed of multiple layers to perform rule learning and fault location estimation, comprising: a network configuration information database that manages network configuration information of said multi-layer network; a rule learning control unit that defines, as rules, the relationships between alarms that characterize faults and the fault locations and causes; a rule database that manages the rules defined by said rule learning control unit; and a fault location estimation function unit that estimates the fault location and cause using alarms generated from said multi-layer network and the rules defined by said rule learning control unit, and also has a correlation range derivation function that converts said multi-layer network into a network model that excludes bundling information for defining the relationships between each layer, based on said network configuration information, and derives correlation ranges of logical resources for said network model, and said rule learning control unit and said fault location estimation function unit perform rule learning and fault location estimation using said rule learning type fault location estimation technology based on said correlation ranges.

2. The fault location estimation device according to claim 1, wherein the rule learning control unit and the fault location estimation function unit have the correlation range deriving function.

3. The fault location estimation device according to claim 1, wherein the bundling information includes a communication connection and a communication termination point.

4. The fault location estimation device according to claim 1, wherein the rule learning control unit and the fault location estimation function unit create a directed graph of the logical resources based on the multilayer network, and derive the correlation range using the directed graph.

5. The fault location estimation device according to claim 4, wherein, when a node corresponding to the bundling information has both an incoming edge and an outgoing edge indicating a hierarchical relationship in the directed graph, the rule learning control unit and the fault location estimation function unit newly connect the source end point of the incoming edge and the destination end point of the outgoing edge with an edge indicating the hierarchical relationship, delete the node corresponding to the bundling information and the edge having a contact point with the node corresponding to the bundling information, and create a new directed graph in which the new relationship is redefined.

6. The fault location estimation device according to claim 5, wherein the multilayer network is a network in which a plurality of logical layers are hierarchically arranged above a physical layer, and wherein the rule learning control unit and the fault location estimation function unit derive the correlation range by saving, in the new directed graph, correlation range search target resources as correlation range resources, saving communication connections and communication termination points in the same communication section as the correlation range search target resources as the correlation range resources, saving all source end points of the incoming edges that indicate the hierarchical relationships of the correlation range resources as the correlation range resources, saving all destination end points of the outgoing edges that indicate the hierarchical relationships of the correlation range resources as the correlation range resources, and saving physical resources related to the correlation range resources as the correlation range resources, thereby deriving the correlation range.

7. A fault location estimation method for applying a rule learning type fault location estimation technology to a multi-layer network composed of multiple layers to perform rule learning and fault location estimation, comprising: a learning step of defining, as rules, relationships between alarms that characterize faults and fault locations / causes; and an estimation step of estimating the fault location / cause using alarms generated from said multi-layer network and the rules defined in said learning step, wherein said learning step and said estimation step comprise: a step of converting said multi-layer network into a network model excluding bundling information for defining the relationships between each layer, based on network configuration information of said multi-layer network; a step of deriving correlation ranges of logical resources for said network model; and a step of performing rule learning and fault location estimation by said rule learning type fault location estimation technology based on said correlation ranges.

8. A fault location estimation program that causes a computer to execute at least a portion of the functions of the fault location estimation device according to claim 1.

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