Control of Prediction Start Timing of Network Load

Abstract

The present invention enables a prediction of the network load for the purpose of executing scaling out of elements included in a communication system to be initiated at an appropriate timing. A policy manager unit (90) determines whether it is necessary to predict the network load, on the basis of performance index value data. An AI unit (70) initiates a prediction of the network load in response to a determination that it is necessary to predict the network load. After a prediction of the network load has been initiated, the policy manager unit (90) determines whether a scale-out is necessary, on the basis of the network load prediction result. The policy manager unit (90), a lifecycle management unit (94), a container management unit (78), and a configuration management unit (76) implement scaling out of elements included in the communication system in response to a determination that a scale-out is necessary.

Description

Technical Field

[0001] The present invention relates to control of the prediction start timing of network load.

Background Art

[0002] Patent Document 1 describes obtaining the bandwidth for each network processing function of a communication device, and increasing the number of network software execution units used for network processing if the bandwidth is greater than a scale-out threshold.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to timely execute scale-out of elements included in a communication system as described in Patent Document 1, it is conceivable to execute scale-out based on the prediction result of network load.

[0005] However, constantly predicting the network load for all elements included in a communication system is wasteful of computer resource capacity and power consumption and is not desirable.

[0006] The present invention has been made in view of the above circumstances, and one of its objects is to start the prediction of network load for executing scale-out of elements included in a communication system at an appropriate timing.

Means for Solving the Problems

[0007] To solve the above problems, the scale-out execution system according to the present disclosure includes performance index value data acquisition means for acquiring performance index value data indicating the actual value of the performance index value related to the communication system, first determination means for determining whether prediction of network load is necessary based on the performance index value data, prediction start means for starting prediction of network load in response to determination that prediction of network load is necessary, second determination means for determining whether scale-out is necessary based on the prediction result of network load after the prediction of network load is started, and scale-out execution means for executing scale-out of elements included in the communication system in response to determination that scale-out is necessary.

[0008] In addition, the scale-out execution method according to the present disclosure includes acquiring performance index value data indicating the actual value of the performance index value related to the communication system, determining whether prediction of network load is necessary based on the performance index value data, starting prediction of network load in response to determination that prediction of network load is necessary, determining whether scale-out is necessary based on the prediction result of network load after the prediction of network load is started, and executing scale-out of elements included in the communication system in response to determination that scale-out is necessary.

Brief Description of the Drawings

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Embodiments for Carrying Out the Invention

[0010] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.

[0011] FIG. 1 and FIG. 2 are diagrams showing an example of a communication system 1 according to an embodiment of the present invention. FIG. 1 is a diagram focusing on the locations of the data center groups included in the communication system 1. FIG. 2 is a diagram focusing on the various computer systems installed in the data center groups included in the communication system 1.

[0012] As shown in FIG. 1, the data center groups included in the communication system 1 are classified into a central data center 10, a regional data center 12, and an edge data center 14.

[0013] The central data center 10 is, for example, distributed and several are arranged within the area covered by the communication system 1 (for example, within Japan).

[0014] The regional data centers 12 are, for example, distributed and dozens are arranged within the area covered by the communication system 1. For example, when the area covered by the communication system 1 is the entire territory of Japan, one or two regional data centers 12 may be arranged in each prefecture.

[0015] The edge data centers 14 are, for example, distributed and thousands are arranged within the area covered by the communication system 1. Also, each of the edge data centers 14 can communicate with the communication facility 18 equipped with the antenna 16. As shown in FIG. 1 here, one edge data center 14 may be able to communicate with several communication facilities 18. The communication facility 18 may include a computer such as a server computer. The communication facility 18 according to the present embodiment performs wireless communication with the UE (User Equipment) 20 via the antenna 16. For example, an RU (Radio Unit) described later is provided in the communication facility 18 equipped with the antenna 16.

[0016] In the central data center 10, regional data center 12, and edge data center 14 according to the present embodiment, a plurality of servers are respectively arranged.

[0017] In the present embodiment, for example, the central data center 10, regional data center 12, and edge data center 14 can communicate with each other. Also, the central data centers 10 can communicate with each other, the regional data centers 12 can communicate with each other, and the edge data centers 14 can communicate with each other.

[0018] As shown in FIG. 2, the communication system 1 according to the present embodiment includes a platform system 30, a plurality of radio access networks (RANs) 32, a plurality of core network systems 34, and a plurality of UEs 20. The core network system 34, RAN 32, and UE 20 cooperate with each other to realize a mobile communication network.

[0019] The RAN 32 is a computer system equipped with an antenna 16, corresponding to an eNB (eNodeB) in the fourth-generation mobile communication system (hereinafter referred to as 4G) and a gNB (NR base station) in the fifth-generation mobile communication system (hereinafter referred to as 5G). The RAN 32 according to the present embodiment is mainly implemented by a server group and communication facilities 18 arranged in the edge data center 14. Note that a part of the RAN 32 (for example, DU (Distributed Unit), CU (Central Unit), vDU (virtual Distributed Unit), vCU (virtual Central Unit)) may be implemented not in the edge data center 14 but in the central data center 10 or the regional data center 12.

[0020] The core network system 34 is a system corresponding to the EPC (Evolved Packet Core) in 4G and the 5G core (5GC) in 5G. The core network system 34 according to the present embodiment is mainly implemented by a server group arranged in the central data center 10 and the regional data center 12.

[0021] The platform system 30 according to this embodiment is configured, for example, on a cloud infrastructure, and as shown in FIG. 2, includes a processor 30a, a storage unit 30b, and a communication unit 30c. The processor 30a is a program control device such as a microprocessor that operates according to a program installed in the platform system 30. The storage unit 30b is, for example, a storage element such as a ROM or a RAM, a solid state drive (SSD), a hard disk drive (HDD), or the like. Programs executed by the processor 30a and the like are stored in the storage unit 30b. The communication unit 30c is a communication interface such as, for example, a NIC (Network Interface Controller) or a wireless LAN (Local Area Network) module. Note that SDN (Software-Defined Networking) may be implemented in the communication unit 30c. The communication unit 30c exchanges data with the RAN 32 and the core network system 34.

[0022] In this embodiment, the platform system 30 is implemented by a server group arranged in the central data center 10. Note that the platform system 30 may be implemented by a server group arranged in the regional data center 12.

[0023] In this embodiment, for example, in response to a purchase request for a network service (NS) by a purchaser, the purchased network service is constructed in the RAN 32 and the core network system 34. Then, the constructed network service is provided to the purchaser.

[0024] For example, network services such as voice communication services and data communication services are provided to a purchaser who is an MVNO (Mobile Virtual Network Operator). The voice communication service and data communication service provided by this embodiment will ultimately be provided to the customers (end users) of the purchaser (MVNO in the above example) who use the UE20 shown in FIGS. 1 and 2. The end user can perform voice communication and data communication with other users via the RAN32 and the core network system 34. Also, the UE20 of the end user can access a data network such as the Internet via the RAN32 and the core network system 34.

[0025] In addition, in this embodiment, an IoT (Internet of Things) service may be provided to an end user who uses a robot arm, a connected car, or the like. And in this case, for example, an end user who uses a robot arm, a connected car, or the like may be the purchaser of the network service according to this embodiment.

[0026] In this embodiment, container-type virtualization application execution environments such as Docker (registered trademark) are installed on the servers arranged in the central data center 10, the regional data center 12, and the edge data center 14, and containers can be deployed and operated on these servers. In these servers, a cluster composed of one or more containers generated by such virtualization technology may be constructed. For example, a Kubernetes cluster managed by a container management tool such as Kubernetes (registered trademark) may be constructed. And the processor on the constructed cluster may execute a container-type application.

[0027] In this embodiment, the network service provided to the purchaser is composed of one or more functional units (e.g., network functions (NFs)). In this embodiment, the functional unit is implemented by an NF realized by virtualization technology. The NF realized by virtualization technology is referred to as a VNF (Virtualized Network Function). Note that it does not matter by what virtualization technology it is virtualized. For example, a CNF (Containerized Network Function) realized by container-type virtualization technology is also included in the VNF in this description. In this embodiment, the network service will be described as being implemented by one or more CNFs. Also, the functional unit according to this embodiment may correspond to a network node.

[0028] FIG. 3 is a diagram schematically showing an example of an operating network service. The network service shown in FIG. 3 includes NFs such as a plurality of RUs 40, a plurality of DUs 42, a plurality of CUs 44 (CU-CP (Central Unit - Control Plane) 44a and CU-UP (Central Unit - User Plane) 44b), a plurality of AMFs (Access and Mobility Management Function) 46, a plurality of SMFs (Session Management Function) 48, and a plurality of UPFs (User Plane Function) 50 as software elements.

[0029] In the example of FIG. 3, the RUs 40, DUs 42, CU-CP 44a, AMFs 46, and SMFs 48 correspond to elements of the control plane (C-Plane), and the RUs 40, DUs 42, CU-UP 44b, and UPFs 50 correspond to elements of the user plane (U-Plane).

[0030] Note that other types of NFs may be included as software elements in the network service. Also, the network service is implemented on computer resources (hardware elements) such as a plurality of servers.

[0031] And in this embodiment, for example, a communication service in a certain area is provided by the network service shown in FIG. 3.

[0032] And in this embodiment, it is assumed that a plurality of RUs 40, a plurality of DUs 42, a plurality of CU-UPs 44b, and a plurality of UPFs 50 shown in FIG. 3 belong to one end-to-end network slice.

[0033] FIG. 4 is a diagram schematically showing an example of the association between elements constructed in the communication system 1 in this embodiment. Note that the symbols M and N shown in FIG. 4 represent arbitrary integers of 1 or more, and indicate the relationship of the number of elements connected by a link. When both ends of the link are a combination of M and N, the elements connected by the link have a many-to-many relationship, and when both ends of the link are a combination of 1 and N or a combination of 1 and M, the elements connected by the link have a one-to-many relationship.

[0034] As shown in FIG. 4, the network service (NS), network function (NF), CNFC (Containerized Network Function Component), pod, and container have a hierarchical structure.

[0035] NS corresponds to, for example, a network service composed of a plurality of NFs. Here, NS may correspond to elements with a granularity such as 5GC, EPC, 5G RAN (gNB), 4G RAN (eNB), and the like.

[0036] In 5G, an NF corresponds to an element at a granularity level such as an RU, DU, CU-CP, CU-UP, AMF, SMF, UPF, etc. Also, in 4G, an NF corresponds to an element at a granularity level such as an MME (Mobility Management Entity), HSS (Home Subscriber Server), S-GW (Serving Gateway), vDU, vCU, etc. In this embodiment, for example, one NS contains one or more NFs. That is, one or more NFs are under the jurisdiction of one NS.

[0037] A CNFC corresponds to an element at a granularity level such as DU mgmt or DU Processing. A CNFC may be a microservice deployed on a server as one or more containers. For example, a certain CNFC may be a microservice that provides some of the functions of a DU, CU-CP, CU-UP, etc. Also, a certain CNFC may be a microservice that provides some of the functions of a UPF, AMF, SMF, etc. In this embodiment, for example, one NF contains one or more CNFCs. That is, one or more CNFCs are under the jurisdiction of one NF.

[0038] A pod refers to the minimum unit for managing Docker containers in, for example, Kubernetes. In this embodiment, for example, one CNFC contains one or more pods. That is, one or more pods are under the jurisdiction of one CNFC.

[0039] And in this embodiment, for example, one pod contains one or more containers. That is, one or more containers are under the jurisdiction of one pod.

[0040] Also, as shown in FIG. 4, the network slice (NSI) and the network slice subnet instance (NSSI) have a hierarchical structure.

[0041] NSI can also be said to be an end-to-end virtual circuit spanning multiple domains (e.g., from RAN 32 to core network system 34). NSI can be a slice for high-speed large-capacity communication (e.g., for eMBB: enhanced Mobile Broadband), a slice for high-reliability and low-latency communication (e.g., for URLLC: Ultra-Reliable and Low Latency Communications), or a slice for connecting a large number of terminals (e.g., for mMTC: massive Machine Type Communication). NSSI can also be said to be a single-domain virtual circuit obtained by splitting NSI. NSSI can be a slice of the RAN domain, a slice of a transport domain such as the MBH (Mobile Back Haul) domain, or a slice of the core network domain.

[0042] In this embodiment, for example, one NSI includes one or more NSSIs. That is, one or more NSSIs are under the control of one NSI. Note that in this embodiment, multiple NSIs may share the same NSSI.

[0043] Also, as shown in FIG. 4, generally, the relationship between NSSI and NS is a many-to-many relationship.

[0044] Also, in this embodiment, for example, one NF can belong to one or more network slices. Specifically, for example, one NF can be set with an NSSAI (Network Slice Selection Assistance Information) including one or more S-NSSAIs (Sub Network Slice Selection Assist Information). Here, S-NSSAI is information associated with a network slice. Note that the NF may not belong to a network slice.

[0045] FIG. 5 is a functional block diagram showing an example of functions implemented in the platform system 30 according to the present embodiment. Note that not all of the functions shown in FIG. 5 need to be implemented in the platform system 30 according to the present embodiment, and functions other than those shown in FIG. 5 may be implemented.

[0046] As shown in FIG. 5, the platform system 30 according to the present embodiment functionally includes, for example, an operation support system (OSS) unit 60, an end-to-end orchestration (E2EO) unit 62, a service catalog storage unit 64, a big data platform unit 66, a data bus unit 68, an AI (Artificial Intelligence) unit 70, a monitoring function unit 72, an SDN controller 74, a configuration management unit 76, a container management unit 78, and a repository unit 80. The OSS unit 60 includes an inventory database 82, a ticket management unit 84, a fault management unit 86, and a performance management unit 88. The E2EO unit 62 includes a policy manager unit 90, a slice manager unit 92, and a lifecycle management unit 94. These elements are mainly implemented by a processor 30a, a storage unit 30b, and a communication unit 30c.

[0047] The functions shown in FIG. 5 may be installed in the platform system 30, which is one or more computers, and implemented by the processor 30a executing a program including instructions corresponding to the functions. This program may be supplied to the platform system 30 via a computer-readable information storage medium such as an optical disk, a magnetic disk, a magnetic tape, a magneto-optical disk, a flash memory, or the like, or via the Internet or the like. Also, the functions shown in FIG. 5 may be implemented by circuit blocks, memories, and other LSIs. It is understood by those skilled in the art that the functions shown in FIG. 5 can be realized in various forms such as only hardware, only software, or a combination thereof.

[0048] The container management unit 78 executes the life cycle management of containers. For example, processes related to the construction of containers, such as the deployment and setting of containers, are included in the life cycle management.

[0049] Here, the platform system 30 according to the present embodiment may include a plurality of container management units 78. And, for each of the plurality of container management units 78, a container management tool such as Kubernetes, and a package manager such as Helm may be installed. And, each of the plurality of container management units 78 may execute the construction of containers, such as the deployment of containers, for the server group (for example, a Kubernetes cluster) associated with the container management unit 78.

[0050] Note that the container management unit 78 does not necessarily have to be included in the platform system 30. The container management unit 78 may be provided, for example, in the server (that is, RAN 32 or core network system 34) managed by the container management unit 78, or may be provided in another server co-located with the server managed by the container management unit 78.

[0051] In the present embodiment, the repository unit 80 stores, for example, the container images of the containers included in the function unit group (for example, NF group) that realizes the network service.

[0052] The inventory database 82 is a database in which inventory information is stored. The inventory information includes, for example, information about the servers arranged in RAN 32 and core network system 34 and managed by the platform system 30.

[0053] In this embodiment, inventory data is stored in the inventory database 82. The inventory data indicates the configuration of the element group included in the communication system 1 and the current status of the association between the elements. Further, the inventory data indicates the status of the resources (for example, the usage status of the resources) managed by the platform system 30. The inventory data may be physical inventory data or logical inventory data. Physical inventory data and logical inventory data will be described later.

[0054] FIG. 6 is a diagram showing an example of the data structure of physical inventory data. The physical inventory data shown in FIG. 6 is associated with one server. The physical inventory data shown in FIG. 6 includes, for example, a server ID, location data, building data, floor number data, rack data, specification data, network data, a list of operating container IDs, a cluster ID, and the like.

[0055] The server ID included in the physical inventory data is, for example, an identifier of the server associated with the physical inventory data.

[0056] The location data included in the physical inventory data is, for example, data indicating the location (for example, the address of the location) of the server associated with the physical inventory data.

[0057] The building data included in the physical inventory data is, for example, data indicating the building (for example, the building name) in which the server associated with the physical inventory data is located.

[0058] The floor number data included in the physical inventory data is, for example, data indicating the floor on which the server associated with the physical inventory data is located.

[0059] The rack data included in the physical inventory data is, for example, an identifier of the rack in which the server associated with the physical inventory data is located.

[0060] The spec data included in the physical inventory data is, for example, data indicating the specs of the server associated with the physical inventory data, and the spec data includes, for example, the number of cores, memory capacity, hard disk capacity, etc.

[0061] The network data included in the physical inventory data is, for example, data indicating information about the network of the server associated with the physical inventory data, and the network data includes, for example, the NICs provided by the server, the number of ports provided by the NICs, the port IDs of the ports, etc.

[0062] The list of running container IDs included in the physical inventory data is, for example, data indicating information about one or more containers running on the server associated with the physical inventory data, and the list of running container IDs includes, for example, a list of identifiers (container IDs) of instances of the containers.

[0063] The cluster ID included in the physical inventory data is, for example, an identifier of the cluster (e.g., Kubernetes cluster) to which the server associated with the physical inventory data belongs.

[0064] The logical inventory data includes topology data indicating the current status of associations between a plurality of elements included in the communication system 1, as shown in FIG. 4. For example, the logical inventory data includes topology data including an identifier of a certain NS and identifiers of one or more NFs under the NS. Also, for example, the logical inventory data includes topology data including an identifier of a certain network slice and identifiers of one or more NFs belonging to the network slice.

[0065] Also, the inventory data may include data indicating the current situation such as the geographical relationship or topological relationship among the elements included in the communication system 1. As described above, the inventory data includes location data indicating the location where the elements included in the communication system 1 are operating, that is, the current location of the elements included in the communication system 1. From this, it can be said that the inventory data indicates the current situation of the geographical relationship (for example, geographical proximity) among the elements.

[0066] Also, the logical inventory data may include NSI data indicating information related to network slices. The NSI data indicates, for example, the identifier of an instance of a network slice and attributes such as the type of the network slice. Also, the logical inventory data may include NSSI data indicating information related to network slice subnets. The NSSI data indicates, for example, the identifier of an instance of a network slice subnet and attributes such as the type of the network slice subnet.

[0067] In addition, the logical inventory data may include NS data indicating information about the NS. The NS data indicates, for example, an identifier of an instance of the NS and attributes such as the type of the NS. Further, the logical inventory data may include NF data indicating information about the NF. The NF data indicates, for example, an identifier of an instance of the NF and attributes such as the type of the NF. Further, the logical inventory data may include CNFC data indicating information about the CNFC. The CNFC data indicates, for example, an identifier of an instance and attributes such as the type of the CNFC. Further, the logical inventory data may include pod data indicating information about the pods included in the CNFC. The pod data indicates, for example, an identifier of an instance of the pod and attributes such as the type of the pod. Further, the logical inventory data may include container data indicating information about the containers included in the pod. The container data indicates, for example, a container ID of an instance of the container and attributes such as the type of the container.

[0068] The container ID of the container data included in the logical inventory data and the container ID included in the list of operating container IDs included in the physical inventory data are used to associate the instance of the container with the server on which the instance of the container is operating.

[0069] In addition, data indicating various attributes such as a host name and an IP address may be included in the above-described data included in the logical inventory data. For example, the container data may include data indicating the IP address of the container corresponding to the container data. Further, for example, the NF data may include data indicating the IP address and the host name of the NF indicated by the NF data.

[0070] In addition, the logical inventory data may include data indicating an NSSAI including one or more S-NSSAIs set for each NF.

[0071] In addition, the inventory database 82 is capable of appropriately grasping the status of resources in cooperation with the container management unit 78. Then, based on the latest status of the resources, the inventory database 82 appropriately updates the inventory data stored in the inventory database 82.

[0072] In addition, for example, in response to actions such as the construction of new elements included in the communication system 1, the configuration change of elements included in the communication system 1, the scaling of elements included in the communication system 1, and the replacement of elements included in the communication system 1 being executed, the inventory database 82 updates the inventory data stored in the inventory database 82.

[0073] In addition, the inventory database 82 may include data indicating the importance of the location where each NF is provided for each NF. For example, an important area flag may be associated with the inventory data of a gNB covering an area including government offices, fire departments, hospitals, etc.

[0074] In addition, the inventory database 82 may include data indicating the importance of services for elements such as NS, NF, and network slices. For example, a purchaser specifies an SLA that the NS to be purchased should meet, and an important service flag may be associated with the inventory data of the elements that need to guarantee the performance corresponding to the SLA.

[0075] The service catalog storage unit 64 stores service catalog data. The service catalog data may include, for example, service template data indicating logic used by the lifecycle management unit 94. This service template data includes information necessary for constructing a network service. For example, the service template data includes information defining NS, NF, and CNFC, and information indicating the correspondence relationship of NS-NF-CNFC. Also, for example, the service template data includes a workflow script for constructing a network service.

[0076] As an example of service template data, NSD (NS Descriptor) can be cited. NSD is associated with a network service and indicates the types of a plurality of functional units (for example, a plurality of CNFs) included in the network service. Note that the number of each type of functional unit such as CNF included in the network service may be indicated in the NSD. Also, the file name of the CNFD described later related to the CNF included in the network service may be indicated in the NSD.

[0077] Also, as an example of service template data, CNFD (CNF Descriptor) can be cited. The CNFD may indicate computer resources (for example, CPU, memory, hard disk, etc.) required by the CNF. For example, for each of a plurality of containers included in the CNF, the CNFD may indicate computer resources (CPU, memory, hard disk, etc.) required by the container.

[0078] Also, the service catalog data may include information regarding a threshold value (for example, a threshold value for anomaly detection) for comparison with a calculated performance index value used by the policy manager unit 90. The performance index value will be described later.

[0079] In addition, the service catalog data may include, for example, slice template data. The slice template data includes information necessary for executing the instantiation of a network slice, and includes, for example, the logic used by the slice manager unit 92.

[0080] The slice template data includes the information of "Generic Network Slice Template" defined by GSMA (GSM Association) (where "GSM" is a registered trademark). Specifically, the slice template data includes the template data of a network slice (NST), the template data of a network slice subnet (NSST), and the template data of a network service. In addition, the slice template data includes information indicating the hierarchical structure of these elements as shown in FIG. 4.

[0081] In this embodiment, for example, in response to a purchase request for an NS by a purchaser, the life cycle management unit 94 constructs a new network service for which the purchase request has been made.

[0082] For example, in response to a purchase request, the life cycle management unit 94 may execute a workflow script associated with the purchased network service. By executing this workflow script, the life cycle management unit 94 may instruct the container management unit 78 to deploy the containers included in the newly purchased network service. Then, the container management unit 78 may obtain the container image corresponding to the container from the repository unit 80 and deploy the container corresponding to the container image to the server.

[0083] In addition, in this embodiment, the lifecycle management unit 94, for example, performs scaling and replacement of elements included in the communication system 1. Here, the lifecycle management unit 94 may output a container deployment instruction or a deletion instruction to the container management unit 78. Then, the container management unit 78 may execute processes such as container deployment and container deletion according to the instruction. In this embodiment, the lifecycle management unit 94 enables execution of scaling and replacement that cannot be handled by tools such as Kubernetes of the container management unit 78.

[0084] In addition, the lifecycle management unit 94 may output a communication path creation instruction to the SDN controller 74. For example, the lifecycle management unit 94 presents two IP addresses at both ends of the communication path to be created to the SDN controller 74, and the SDN controller 74 creates a communication path connecting these two IP addresses. The created communication path may be managed in association with these two IP addresses.

[0085] In addition, the lifecycle management unit 94 may output a communication path creation instruction to the SDN controller 74 for the communication path between two IP addresses associated with the two IP addresses.

[0086] In this embodiment, the slice manager unit 92, for example, performs instantiation of a network slice. In this embodiment, the slice manager unit 92 performs instantiation of a network slice by executing the logic indicated by the slice template stored in the service catalog storage unit 64.

[0087] The slice manager unit 92 is configured to include functions of, for example, NSMF (Network Slice Management Function) and NSSMF (Network Slice Sub-network Management Function) as described in the specification "TS28 533" of 3GPP (Registered Trademark) (Third Generation Partnership Project). The NSMF is a function for generating and managing network slices and provides management services for NSIs. The NSSMF is a function for generating and managing network slice sub-networks that constitute a part of a network slice and provides management services for NSSIs.

[0088] Here, the slice manager unit 92 may output a configuration management instruction related to the instantiation of a network slice to the configuration management unit 76. Then, the configuration management unit 76 may execute configuration management such as settings according to the configuration management instruction.

[0089] Also, the slice manager unit 92 may present two IP addresses to the SDN controller 74 and output an instruction to create a communication path between these two IP addresses.

[0090] In this embodiment, for example, the configuration management unit 76 executes configuration management such as setting of an element group such as an NF according to a configuration management instruction received from the life cycle management unit 94 or the slice manager unit 92.

[0091] In this embodiment, for example, the SDN controller 74 creates a communication path between two IP addresses associated with the creation instruction according to a communication path creation instruction received from the life cycle management unit 94 or the slice manager unit 92. The SDN controller 74 may create a communication path between two IP addresses using a known path calculation method such as Flex Algo.

[0092] Here, for example, the SDN controller 74 may construct NSIs and NSSIs for aggregation routers, servers, etc. existing between communication paths using segment routing technology (e.g., SRv6 (Segment Routing IPv6)). Also, the SDN controller 74 may issue commands for setting a common VLAN (Virtual Local Area Network) for a plurality of NFs to be configured and commands for allocating the bandwidth and priority indicated by the setting information to the VLAN, thereby generating NSIs and NSSIs across the plurality of NFs to be configured.

[0093] Note that the SDN controller 74 may execute operations such as changing the maximum value of the available bandwidth for communication between two IP addresses without constructing a network slice.

[0094] The platform system 30 according to the present embodiment may include a plurality of SDN controllers 74. And each of the plurality of SDN controllers 74 may execute processes such as creating communication paths for a network device group such as an AG associated with the SDN controller 74.

[0095] In the present embodiment, the monitoring function unit 72 monitors, for example, the element group included in the communication system 1 according to a given management policy. Here, the monitoring function unit 72 may monitor the element group according to a monitoring policy specified by a purchaser, for example, when purchasing a network service.

[0096] In the present embodiment, the monitoring function unit 72 performs monitoring at various levels, such as the slice level, NS level, NF level, CNFC level, and hardware level such as a server.

[0097] The monitoring function unit 72 may be set to a module that outputs metric data to hardware such as a server or software elements included in the communication system 1 so that monitoring at various levels described above can be performed. Here, for example, the NF may output metric data indicating a metric that can be measured (specified) in the NF to the monitoring function unit 72. Also, the server may output metric data indicating a metric related to hardware that can be measured (specified) in the server to the monitoring function unit 72.

[0098] Also, for example, the monitoring function unit 72 may deploy a sidecar container on the server that aggregates metric data indicating metrics output from a plurality of containers in units of CNFC (microservices). This sidecar container may include an agent called an exporter. The monitoring function unit 72 may repeatedly execute, at a given monitoring interval, a process of obtaining metric data aggregated in units of microservices from the sidecar container using the mechanism of a monitoring tool such as Prometheus that can monitor container management tools such as Kubernetes.

[0099] The monitoring function unit 72 may monitor, for example, performance indicator values for the performance indicators described in "TS 28.552, Management and orchestration; 5G performance measurements" or "TS 28.554, Management and orchestration; 5G end to end Key Performance Indicators (KPI)". And the monitoring function unit 72 may obtain metric data indicating the monitored performance indicator values.

[0100] Then, in this embodiment, the monitoring function unit 72 generates performance indicator value data indicating the performance indicator values of the elements included in the communication system 1 at the aggregation unit by executing a process (enrichment) of aggregating metric data in a predetermined aggregation unit.

[0101] For example, for one gNB, by aggregating metric data indicating the metrics of elements (such as network nodes like DU42 and CU44) under the gNB, performance metric value data of the gNB is generated. In this way, performance metric value data indicating the communication performance in the area covered by the gNB is generated. Here, for example, in each gNB, performance metric value data indicating multiple types of communication performance such as traffic volume (throughput) and latency may be generated. Note that the communication performance indicated by the performance metric value data is not limited to traffic volume and latency.

[0102] Then, the monitoring function unit 72 outputs the performance metric value data generated by the above-described enrichment to the data bus unit 68.

[0103] In this embodiment, the data bus unit 68 receives, for example, the performance metric value data output from the monitoring function unit 72. Then, based on the one or more received performance metric value data, the data bus unit 68 generates a performance metric value file including the one or more performance metric value data. Then, the data bus unit 68 outputs the generated performance metric value file to the big data platform unit 66.

[0104] Also, elements such as network slices, NS, NF, CNFC, etc. included in the communication system 1 and hardware such as servers send various alert notifications (for example, alert notifications triggered by the occurrence of a failure) to the monitoring function unit 72.

[0105] Then, when the monitoring function unit 72 receives, for example, the above-described alert notification, it outputs alert message data indicating the notification to the data bus unit 68. Then, the data bus unit 68 generates an alert file by collecting the alert message data indicating one or more notifications into one file, and outputs the alert file to the big data platform unit 66.

[0106] In this embodiment, for example, the big data platform unit 66 stores the performance index value file and the alert file output from the data bus unit 68.

[0107] In this embodiment, for example, a plurality of pre-trained machine learning models are stored in the AI unit 70 in advance. The AI unit 70 executes estimation processing such as future prediction processing of the usage status and service quality of the communication system 1 using various machine learning models stored in the AI unit 70. The AI unit 70 may generate estimation result data indicating the result of the estimation processing.

[0108] The AI unit 70 may execute the estimation processing based on the file stored in the big data platform unit 66 and the above-described machine learning model. This estimation processing is suitable for performing long-term trend prediction at a low frequency.

[0109] Also, the AI unit 70 can acquire the performance index value data stored in the data bus unit 68. The AI unit 70 may execute the estimation processing based on the performance index value data stored in the data bus unit 68 and the above-described machine learning model. This estimation processing is suitable for performing short-term prediction at a high frequency.

[0110] In this embodiment, for example, the performance management unit 88 calculates a performance index value (for example, KPI) based on a plurality of metric data, based on the metrics indicated by these metric data. The performance management unit 88 may calculate a performance index value (for example, a performance index value related to an end-to-end network slice) that is a comprehensive evaluation of a plurality of types of metrics that cannot be calculated from a single metric data. The performance management unit 88 may generate comprehensive performance index value data indicating the performance index value that is a comprehensive evaluation.

[0111] Note that the performance management unit 88 may obtain the above-described performance index value file from the big data platform unit 66. Further, the performance management unit 88 may obtain the estimation result data from the AI unit 70. Then, based on at least one of the performance index value file or the estimation result data, performance index values such as KPIs may be calculated. Note that the performance management unit 88 may directly obtain metric data from the monitoring function unit 72. Then, based on the metric data, performance index values such as KPIs may be calculated.

[0112] In this embodiment, for example, the fault management unit 86 detects the occurrence of a fault in the communication system 1 based on at least any one of the above-described metric data, the above-described alert notification, the above-described estimation result data, and the above-described comprehensive performance index value data. The fault management unit 86 may detect the occurrence of a fault that cannot be detected from a single metric data or a single alert notification, for example, based on a predetermined logic. The fault management unit 86 may generate detection fault data indicating the detected fault.

[0113] Note that the fault management unit 86 may directly obtain metric data and alert notifications from the monitoring function unit 72. Further, the fault management unit 86 may obtain a performance index value file and an alert file from the big data platform unit 66. Further, the fault management unit 86 may obtain alert message data from the data bus unit 68.

[0114] In this embodiment, for example, the policy manager unit 90 executes a predetermined determination process based on at least any one of the above-described metric data, the above-described performance index value data, the above-described alert message data, the above-described performance index value file, the above-described alert file, the above-described estimation result data, the above-described comprehensive performance index value data, and the above-described detection fault data.

[0115] Then, the policy manager unit 90 may execute an action according to the result of the determination process. For example, the policy manager unit 90 may output an instruction to construct a network slice to the slice manager unit 92. Also, the policy manager unit 90 may output an instruction for scaling or replacement of elements to the lifecycle management unit 94 according to the result of the determination process.

[0116] The policy manager unit 90 according to the present embodiment is capable of acquiring the performance index value data stored in the data bus unit 68. Then, the policy manager unit 90 may execute a predetermined determination process based on the performance index value data acquired from the data bus unit 68. Also, the policy manager unit 90 may execute a predetermined determination process based on the alert message data stored in the data bus unit 68.

[0117] The ticket management unit 84 generates, for example in the present embodiment, a ticket indicating the content to be notified to the administrator of the communication system 1. The ticket management unit 84 may generate a ticket indicating the content of the generated failure data. Also, the ticket management unit 84 may generate a ticket indicating the values of the performance index value data and the metric data. Also, the ticket management unit 84 may generate a ticket indicating the determination result by the policy manager unit 90.

[0118] Then, the ticket management unit 84 notifies the generated ticket to the administrator of the communication system 1. The ticket management unit 84 may, for example, send an e-mail with the generated ticket attached to the e-mail address of the administrator of the communication system 1.

[0119] Hereinafter, the generation of the performance index value file, the determination process based on the performance index value data stored in the data bus unit 68, and the estimation process based on the performance index value data stored in the data bus unit 68 will be further described.

[0120] FIG. 7 is a diagram schematically showing an example of the data bus section 68 according to the present embodiment. As shown in FIG. 7, the data bus section 68 according to the present embodiment includes, for example, a plurality of queues 100 that hold performance index value data in a first-in, first-out list structure.

[0121] Each queue 100 belongs to either the first queue group 102a or the second queue group 102b.

[0122] Also, in the present embodiment, for example, in the monitoring function section 72, a plurality of aggregation processes 104 are operating. Each aggregation process 104 has an element to be aggregated in the aggregation process 104 set in advance. For example, each aggregation process 104 has a gNB to be aggregated in the aggregation process 104 set in advance. Then, each aggregation process 104 acquires metric data from an NF (for example, RU40, DU42, and CU-UP44b) under the gNB that is the aggregation target in the aggregation process 104. And the aggregation process 104 executes an enrichment process of generating performance index value data indicating the communication performance of the gNB based on the acquired metric data.

[0123] Also, in the present embodiment, for example, the aggregation process 104 and the queue 100 are associated in advance. For the sake of convenience, in FIG. 7, it is shown that the aggregation process 104 and the queue 100 are associated in a one-to-one relationship, but the aggregation process 104 and the queue 100 may be associated in a many-to-many relationship.

[0124] Hereinafter, the aggregation process 104 associated with the queue 100 included in the first queue group 102a will be referred to as the first group aggregation process 104a. Also, the aggregation process 104 associated with the queue 100 included in the second queue group 102b will be referred to as the second group aggregation process 104b.

[0125] Then, each first-group aggregation process 104a aggregates metric data from the previous aggregation to the current time at a predetermined time interval (e.g., every minute) associated with the first-group aggregation process 104a, thereby generating performance metric value data.

[0126] The first-group aggregation process 104a acquires metric data from one or more NFs associated with the first-group aggregation process 104a, for example, at one-minute intervals. Then, the first-group aggregation process 104a aggregates the metric data for the same aggregation period to generate performance metric value data for that aggregation period.

[0127] Then, each time the first-group aggregation process 104a generates performance metric value data, it enqueues the performance metric value data into one or more queues 100 associated with the first-group aggregation process 104a.

[0128] Then, each second-group aggregation process 104b aggregates metric data from the previous aggregation to the current time at a predetermined time interval (e.g., every 15 minutes) associated with the second-group aggregation process 104b, thereby generating performance metric value data.

[0129] The second-group aggregation process 104b acquires metric data from one or more NFs associated with the second-group aggregation process 104b, for example, at 15-minute intervals. Then, the second-group aggregation process 104b aggregates the metric data for the same aggregation period to generate performance metric value data for that aggregation period.

[0130] Then, each time the second-group aggregation process 104b generates performance metric value data, it enqueues the performance metric value data into one or more queues 100 associated with the second-group aggregation process 104b.

[0131] In this embodiment, the maximum number of performance metric value data that can be stored in the queues 100 included in the first queue group 102a is predetermined. Here, for example, it is assumed that a maximum of 240 pieces of performance metric value data can be stored in the queue 100. That is, the maximum number is "240".

[0132] Also, in this embodiment, the maximum number of performance metric value data that can be stored in the queues 100 included in the second queue group 102b is predetermined. Here, for example, it is assumed that a maximum of 4 pieces of performance metric value data can be stored in the queue 100. That is, the maximum number is "4".

[0133] In this embodiment, for example, a certain NF may be associated with both the first group aggregation process 104a and the second group aggregation process 104b. And the NF may output the types of metric data aggregated in the first group aggregation process 104a at one-minute intervals to the first group aggregation process 104a. And the NF may output the types of metric data aggregated in the second group aggregation process 104b at 15-minute intervals to the second group aggregation process 104b.

[0134] The types of metric data output to the first group aggregation process 104a and the types of metric data output to the second group aggregation process 104b may be the same or different.

[0135] Here, for example, for some of the metrics to be monitored in the NF, the metric data for which real-time monitoring is desirable may be output to the first group aggregation process 104a.

[0136] And, for example, in the present embodiment, in the policy manager unit 90, a plurality of determination processes 106 (see FIGS. 8 and 9) are operating. Among these determination processes 106, some execute determination processing based on the performance index value data stored in the data bus unit 68, and the rest execute determination processing based on the files stored in the big data platform unit 66.

[0137] Among the determination processes 106 according to the present embodiment, there are those that acquire performance index value data indicating the actual values of the performance index values related to the communication system 1. For example, there is a determination process 106 that acquires the performance index value data in response to the performance index value data being enqueued in the queue 100 included in the first queue group 102a.

[0138] In the present embodiment, for the queue 100 included in the first queue group 102a, any performance index value data included in the queue 100 can be accessed (acquired) without being dequeued.

[0139] And, based on the performance index value data to be acquired, the determination process 106 determines the state of the communication system 1. Here, for example, the state of the element associated with the determination process 106 included in the communication system 1 may be determined. For example, the state of the element that is the aggregation target in the first group aggregation process 104a that generates the performance index value data acquired by the determination process 106 may be determined. Hereinafter, such a determination process 106 will be referred to as an actual result determination process 106a.

[0140] In the present embodiment, for example, the actual result determination process 106a and the queue 100 are associated in advance. For convenience, in FIGS. 8 and 9, it is shown that the actual result determination process 106a and the queue 100 are associated in a one-to-one relationship, but the actual result determination process 106a and the queue 100 may be associated in a many-to-many relationship.

[0141] Here, for example, when performance index value data is enqueued in the queue 100 included in the first queue group 102a, the data bus unit 68 may output a notification indicating that the performance index value data has been enqueued to one or a plurality of performance determination processes 106a associated with the queue 100.

[0142] And the performance determination process 106a that has received the notification may obtain the latest performance index value data stored in the queue 100 in response to the reception of the notification.

[0143] Also, among the determination processes 106 according to the present embodiment, there is one that obtains estimation result data indicating the estimation result by the estimation process 108 (see FIG. 9) associated with the determination process 106. And the determination process 106 determines the state of the communication system 1 based on the obtained estimation result data. Here, for example, the state of an element associated with the determination process 106 included in the communication system 1 may be determined. For example, the state of an element that is the aggregation target in the first aggregation process 104a that generates the performance index value data obtained by the estimation process 108 may be determined. Hereinafter, such a determination process 106 will be referred to as a prediction determination process 106b.

[0144] Also, in the present embodiment, for example, in the AI unit 70, a plurality of estimation processes 108 (see FIG. 9) are operating. Some of these estimation processes 108 execute an estimation process based on the performance index value data stored in the data bus unit 68, and the rest execute an estimation process based on the files stored in the big data platform unit 66.

[0145] Also, in the present embodiment, for example, the estimation process 108 and the queue 100 are associated in advance. For the sake of convenience, in FIG. 8, it is shown that the estimation process 108 and the queue 100 are associated in a one-to-one relationship, but the estimation process 108 and the queue 100 may be associated in a many-to-many relationship.

[0146] In this embodiment, for example, each estimation process 108 acquires performance index value data stored in a queue 100 included in the first queue group 102a corresponding to the estimation process 108. Then, based on the performance index value data, the estimation process executes an estimation process predetermined in the estimation process 108.

[0147] Here, for example, in response to the performance index value data being enqueued in the queue 100 included in the first queue group 102a, the estimation process 108 acquires performance index value data for a most recent predetermined number or a most recent predetermined period including at least the latest performance index value data among the performance index value data stored in the queue 100.

[0148] Here, for example, in response to the performance index value data being enqueued in the queue 100 included in the first queue group 102a, the data bus unit 68 may output a notification indicating that the performance index value data has been enqueued to one or a plurality of estimation processes 108 associated with the queue 100.

[0149] Then, in response to receiving the notification, the estimation process 108 that has received the notification may acquire performance index value data for a most recent predetermined number or a most recent predetermined period including at least the latest performance index value data among the performance index value data stored in the queue 100.

[0150] Here, for example, the estimation process 108 shown in FIG. 9 acquires 60 pieces of estimation index value data including the latest performance index value data. These performance index value data correspond to the performance index value data for the most recent 60 minutes including the latest performance index value data. Then, based on the performance index value data, the estimation process 108 executes an estimation process.

[0151] For example, assume that a first group aggregation process 104a associated with a specific gNB generates performance metric value data related to the gNB by aggregating metric data related to elements included in the gNB (e.g., elements under the gNB). Then, an estimation process 108 that acquires the performance metric value data generated by the first group aggregation process 104a acquires 60 pieces of performance metric value data including the latest performance metric value data stored in the queue 100 in response to the performance metric value data being enqueued in the queue 100.

[0152] In this case, the estimation process 108 uses a pre-stored learned machine learning model in the AI unit 70 to predict the level of the network load of the gNB from the current time to 20 minutes ahead based on these 60 pieces of performance metric value data. Here, for example, predictions such as traffic volume (throughput) and latency may be made as the level of the gNB's network load.

[0153] This machine learning model may be, for example, an existing prediction model. Also, for example, this machine learning model may be a learned machine learning model in which supervised learning using a plurality of training data has been performed in advance. And each of these plurality of training data may include, for example, learning input data indicating the traffic volume for 60 minutes up to a given time point at the gNB and teacher data indicating the level of the network load (e.g., traffic volume and latency) from the given time point to 20 minutes ahead at the gNB.

[0154] Note that the estimation process 108 does not necessarily need to acquire a part of the performance metric value data stored in the queue 100 as described above, and may acquire all of the performance metric value data stored in the queue 100.

[0155] Then, the estimation process 108 outputs estimation result data indicating the execution result (estimation result) of the estimation process to the prediction determination process 106b associated with the estimation process 108. Then, the prediction determination process 106b acquires the estimation result data. And the prediction determination process 106b determines the state of the communication system 1 based on the acquired estimation result data.

[0156] As described above, the queue 100 according to the present embodiment is associated with the aggregation process 104, the performance determination process 106a, the prediction determination process 106b, and the estimation process 108.

[0157] Also, in the present embodiment, for example, the data bus unit 68 generates a performance index value file including at least a part of the performance index value data stored in the queue 100 at a frequency lower than the frequency at which the AI unit 70 acquires the performance index value data.

[0158] For example, the data bus unit 68 may generate a performance index value file including the performance index value data stored in the queue 100 after the timing when the performance index value file was last generated at a predetermined time interval.

[0159] Here, the time interval may or may not match the time (60 minutes in the above example) corresponding to the maximum number of performance index value data that can be stored in the queue 100 included in the first queue group 102a.

[0160] Also, for example, the data bus unit 68 may generate a file including all the performance index value data stored in the queue 100 in response to all the performance index value data included in the generated performance index value file being dequeued. That is, a file including all the performance index value data stored in the queue 100 may be generated in response to all the performance index value data stored in the queue 100 being replaced.

[0161] In addition, in this embodiment, when 60 pieces of performance index value data are stored in the queue 100 included in the first queue group 102a and new performance index value data is enqueued, the oldest performance index value data stored in the queue 100 is dequeued. That is, the oldest performance index value data stored in the queue 100 is deleted from the queue 100.

[0162] And in this embodiment, when 4 pieces of performance index value data are stored in the queue 100 included in the second queue group 102b, the data bus unit 68 generates a performance index value file that combines these 4 pieces of performance index value data into one file. Then, the data bus unit 68 outputs the generated performance index value file to the big data platform unit 66.

[0163] And the data bus unit 68 dequeues all the performance index value data stored in the queue 100. That is, all the performance index value data stored in the queue 100 is deleted from the queue 100.

[0164] In this way, the processes executed in response to the generation of the performance index value file are different between the queue 100 included in the first queue group 102a and the queue 100 included in the second queue group 102b. In the queue 100 included in the second queue group 102b, all the performance index value data stored in the queue 100 is deleted from the queue 100 in response to the generation of the performance index value file. On the other hand, in the queue 100 included in the first queue group 102a, dequeueing in response to the generation of the performance index value file is not executed.

[0165] In this embodiment, for example, when a network service is constructed, not only the elements included in the network service but also, as shown in FIG. 8, the queue 100, the aggregation process 104, and the performance determination process 106a associated with the elements are generated.

[0166] At this time, the policy manager unit 90 may refer to the inventory data to check the attributes of the elements associated with the generated performance determination process 106a. Then, the policy manager unit 90 may generate a performance determination process 106a in which a workflow according to the confirmed attributes is set. Then, the performance determination process 106a may execute a determination process by executing the workflow set in the performance determination process 106a.

[0167] And in this embodiment, for example, the performance determination process 106a determines whether scaling out is necessary based on the acquired performance metric value data.

[0168] Then, in this embodiment, for example, when it is determined that scaling out is necessary, the platform system 30 executes scaling out of the elements included in the communication system 1. For example, the policy manager unit 90, the lifecycle management unit 94, the container management unit 78, and the configuration management unit 76 may cooperate with each other to execute scaling out. For example, based on the performance metric value data related to a specific gNB, when it is determined that scaling out is necessary, scaling out of the DU 42 or CU-UP 44b included in the gNB may be executed.

[0169] For example, the performance determination process 106a may determine whether the acquired performance metric value data satisfies a predetermined first scaling out condition. Here, it may be determined whether the performance metric value indicated by the performance metric value data exceeds a threshold th1. This performance metric value may be a value indicating the level of network load, such as traffic volume (throughput), latency, or the like. Then, when it is determined that the first scaling out condition is satisfied (for example, when it is determined that the performance metric value exceeds the threshold th1), scaling out of the elements included in the communication system 1 may be executed.

[0170] Also, in this embodiment, for example, the performance evaluation process 106a determines whether it is necessary to predict the network load based on the acquired performance metric value data. Then, in this embodiment, for example, the platform system 30 starts predicting the network load in response to determining that it is necessary to predict the network load.

[0171] Also, for example, the performance evaluation process 106a may determine whether the acquired performance metric value data satisfies a predetermined prediction start condition. For example, it may be determined whether the performance metric value indicated by the performance metric value data exceeds a threshold value t2. Here, this threshold value th2 may be a value smaller than the above-described threshold value th1. That is, the above-described threshold value th1 may be a value larger than the threshold value th2. Then, in response to determining that the prediction start condition is satisfied (for example, determining that the performance metric value exceeds the threshold value th2), the prediction of the network load may be started.

[0172] As described above, in this embodiment, when the value indicating the level of the network load indicated by the performance metric value data exceeds a first threshold value (for example, the above-described threshold value th2), the performance evaluation process 106a may determine that it is necessary to predict the network load. Then, when the value indicating the level of the network load indicated by the performance metric value data exceeds a second threshold value (for example, the above-described threshold value th1) that is larger than the first threshold value, the performance evaluation process 106a may determine that scale-out is necessary.

[0173] For example, as shown in FIG. 8, assume that in the situation where the performance evaluation process 106a is operating, the performance evaluation process 106a determines that it is necessary to predict the network load. For example, assume that a predetermined prediction start condition is satisfied.

[0174] Then, the AI unit 70 generates an estimation process 108 associated with the performance determination process 106a, and the policy manager unit 90 generates a prediction determination process 106b associated with the performance determination process 106a. Here, for example, the estimation process 108 and the prediction determination process 106b may be activated. Also, at this time, instantiation of the learned machine learning model may be executed together. Then, the estimation process 108 may execute an estimation using the machine learning model instantiated in this way.

[0175] Then, the prediction determination process 106b may execute a predetermined determination process based on the estimation result data output by the estimation process 108 associated with the prediction determination process 106b. For example, the prediction determination process 106b may determine the necessity of scale-out based on the prediction result of the network load.

[0176] In this embodiment, for example, as shown in FIG. 9, in response to the performance index value data being enqueued in the queue 100 included in the first queue group 102a, the performance determination process 106a acquires the enqueued performance index value data, and the estimation process 108 acquires the performance index value data of the most recent predetermined number or the most recent predetermined period including at least the enqueued performance index value data among the performance index value data stored in the queue 100. In this way, in response to the performance index value data being enqueued in the queue 100, the enqueued performance index value data may be acquired by both the performance determination process 106a and the estimation process 108.

[0177] Then, the performance determination process 106a may determine the necessity of scale-out based on the acquired performance index value data.

[0178] Further, the estimation process 108 may generate estimation result data indicating a prediction result of network load based on the performance index value data to be acquired. Then, the estimation process 108 may output the generated estimation result data to the prediction determination process 106b. Then, the prediction determination process 106b may acquire the estimation result data.

[0179] Then, the prediction determination process 106b may determine whether scale - out is necessary based on the acquired estimation result data.

[0180] Note that it is not necessary for the AI unit 70 to generate the estimation process 108 and for the policy manager unit 90 to generate the prediction determination process 106b. For example, the performance determination process 106a may generate the estimation process 108 and the policy manager unit 90.

[0181] Then, in this embodiment, for example, the platform system 30 executes scale - out of the elements included in the communication system 1 in response to a determination that scale - out is necessary.

[0182] For example, the prediction determination process 106b may determine whether the predicted value of the network load indicated by the estimation result data satisfies a predetermined second scale - out condition. For example, it may be determined whether the predicted value exceeds a threshold th3. Here, for example, it may be determined whether any of a plurality of predicted values from the current time to 20 minutes ahead of the current time exceeds the threshold th3. This predicted value may be a value indicating the level of network load, such as traffic volume (throughput), latency, etc. Then, in response to a determination that the second scale - out condition is satisfied, scale - out of the elements included in the communication system 1 may be executed. Note that the second scale - out condition may be the same as or different from the above - mentioned first scale - out condition.

[0183] As described above, in the present embodiment, after the prediction of the network load is started, the prediction determination process 106b determines whether or not scale-out is necessary based on the prediction result of the network load. On the other hand, the performance determination process 106a determines whether or not scale-out is necessary based on the performance index value data without depending on the prediction result of the network load.

[0184] Then, in response to the determination by the performance determination process 106a or the prediction determination process 106b that scale-out is necessary, the policy manager unit 90, the life cycle management unit 94, the container management unit 78, and the configuration management unit 76 execute scale-out of the elements included in the communication system 1.

[0185] Also, in the present embodiment, in response to satisfying a predetermined condition, the estimation process 108 ends the prediction of the network load. That is, the estimation process 108 and the prediction determination process 106b disappear or stop. In this way, according to the present embodiment, by not constantly predicting the network load (in other words, by ending periodic or temporary prediction), waste of computer resource capacity and power consumption can be suppressed. The end condition of the prediction of the network load may be appropriately determined according to the specifications of the communication system 1, the requirements of the users using the communication system 1, and the like.

[0186] For example, in response to the execution of scale-out of the element as described above, the estimation process 108 and the prediction determination process 106b associated with the element may end (process kill).

[0187] Further, it may be determined by the prediction determination process 106b whether or not a predicted value of the network load indicated by the estimation result data is less than a threshold value th4. Then, in response to determining that the predicted value of the network load indicated by the estimation result data is less than the threshold value th4, the prediction determination process 106b and the estimation process 108 associated with the prediction determination process 106b may be terminated (process killed).

[0188] Here, an example of the processing flow regarding the state determination of the communication system 1 by the actual result determination process 106a performed in the platform system 30 according to the present embodiment will be described with reference to the flowchart illustrated in FIG. 10.

[0189] In this processing example, the data bus unit 68 monitors that performance index value data is enqueued for each of the queues 100 included in the first queue group 102a (S101).

[0190] When enqueueing of the performance index value data for the queue 100 is detected, the actual result determination process 106a associated with the queue 100 acquires the performance index value data (S102).

[0191] Then, the actual result determination process 106a determines whether or not the performance index value indicated by the performance index value data acquired in the process shown in S102 exceeds a threshold value th1 (S103).

[0192] When it is determined that the performance index value exceeds the threshold value th1 (S103: Y), the policy manager unit 90, the life cycle management unit 94, the container management unit 78, and the configuration management unit 76 execute scale-out of the elements associated with the actual result determination process 106a (S104), and return to the process shown in S101.

[0193] When it is determined that the performance index value does not exceed the threshold value th1 (S103: N), the performance determination process 106a determines whether the estimation process 108 associated with the performance determination process 106a and the prediction determination process 106b have been generated, and whether the performance index value exceeds the threshold value th2 (S105). As described above, the threshold value th2 may be a value smaller than the above-described threshold value th1.

[0194] When it is determined that the estimation process 108 and the prediction determination process 106b have not been generated and the performance index value exceeds the threshold value th2 (S105: Y), the AI unit 70 generates the estimation process 108 associated with the performance determination process 106a, and the policy manager unit 90 generates the prediction determination process 106b associated with the performance determination process 106a (S106). Then, the process returns to the process shown in S101. In this way, the estimation process by the estimation process 108 and the determination process by the prediction determination process 106b are started.

[0195] In the process shown in S105, when it is determined that the estimation process 108 and the prediction determination process 106b have been generated, or when it is determined that the performance index value does not exceed the threshold value th2 (S105: N), the process returns to the process shown in S101.

[0196] Constantly predicting the network load for all elements included in the communication system 1 is wasteful of computer resource capacity and power consumption and is not desirable.

[0197] As described above, in the present embodiment, when it is determined that it is necessary to predict the network load based on the performance index value data, the prediction of the network load is started. Then, after the prediction of the network load is started, when it is determined that scale-out is necessary based on the prediction result of the network load, the scale-out of the elements included in the communication system 1 is executed. By doing so, according to the present embodiment, the prediction of the network load for executing the scale-out of the elements included in the communication system 1 is started at an appropriate timing.

[0198] Also, in the present embodiment, even when the prediction of the network load is not being performed, the necessity of scale-out is determined based on the performance index value data without depending on the prediction result of the network load. Then, when it is determined that scale-out is necessary, the scale-out of the elements included in the communication system 1 is executed.

[0199] For example, even when the prediction of the network load is not being performed, if the performance index value exceeds the threshold th1, the scale-out of the elements included in the communication system 1 is executed.

[0200] In this way, in the present embodiment, regardless of whether the prediction of the network load is being executed, when a situation occurs where the elements included in the communication system 1 should be scaled out, the elements will be accurately scaled out.

[0201] Next, another example of the processing flow regarding the state determination of the communication system 1 by the performance determination process 106a performed in the platform system 30 according to the present embodiment will be described with reference to the flowchart illustrated in FIG. 11.

[0202] The processes shown from S201 to S205 are the same as the processes shown from S101 to S105, and thus the description thereof will be omitted.

[0203] Assume that in the process shown in S205, the estimation process 108 and the prediction determination process 106b have not been generated, and it is determined that the performance index value exceeds the threshold th2 (S205: Y). In this case, the performance determination process 106a determines whether there is sufficient computer resource for the determination process 106 (S206).

[0204] Here, for example, it may be determined whether the ratio of the number of the determination processes 106 being executed to the maximum number of the predetermined determination processes 106 is equal to or greater than a predetermined ratio.

[0205] When it is determined that there is sufficient computer resource for the determination process 106 (S206: Y), the AI unit 70 generates the estimation process 108 associated with the performance determination process 106a, and the policy manager unit 90 generates the prediction determination process 106b associated with the performance determination process 106a (S207). Then, the process returns to the process shown in S201. In this way, the estimation process by the estimation process 108 and the determination process by the prediction determination process 106b are started.

[0206] When it is determined that there is not enough computer resource for the determination process 106 (S206: N), the process returns to the process shown in S201.

[0207] As shown in FIG. 11, in the present embodiment, the performance determination process 106a may determine whether prediction of network load is necessary based on the performance index value data and the usage amount of computer resources. Then, in response to the determination that prediction of network load is necessary, prediction of network load may be started. By doing so, it becomes possible to control whether to start prediction of network load according to the usage amount of computer resources. For example, it becomes possible not to start prediction of network load when the computer resources are in short supply.

[0208] Here, the indicator indicating the amount of computer resource usage is not limited to the ratio of the number of the determination processes 106 being executed to the maximum number of the determination processes 106 as described above. For example, based on the number of the determination processes 106 being executed, it may be determined whether or not it is necessary to predict the network load. Also, based on the ratio of the number of the prediction determination processes 106b being executed to the maximum number of the prediction determination processes 106b determined in advance, it may be determined whether or not it is necessary to predict the network load. Further, based on the number of the prediction determination processes 106b being executed, it may be determined whether or not it is necessary to predict the network load. Also, based on the ratio of the number of the estimation processes 108 being executed to the maximum number of the estimation processes 108 determined in advance, it may be determined whether or not it is necessary to predict the network load. Further, based on the number of the estimation processes 108 being executed, it may be determined whether or not it is necessary to predict the network load. Also, based on the CPU usage rate, the memory usage rate, the storage usage rate, etc., it may be determined whether or not it is necessary to predict the network load.

[0209] Also, in the present embodiment, the policy manager unit 90 may determine whether or not to execute the determination of whether or not it is necessary to predict the network load based on the amount of computer resource usage according to the importance of the element that is the execution target of the scale-out.

[0210] For example, the process shown in FIG. 10 may be a process corresponding to the first type of workflow, and the process shown in FIG. 11 may be a process corresponding to the second type of workflow.

[0211] And, for example, in a scenario where the performance evaluation process 106a is generated, the policy manager unit 90 may check the importance of elements associated with the generated performance evaluation process 106a based on the inventory data. Then, a performance evaluation process 106a with a workflow set according to the confirmed importance may be generated. For example, for elements associated with an important area flag or an important service flag in the inventory data, a performance evaluation process 106a with a first type of workflow set may be generated. And, for elements not associated with either an important area flag or an important service flag in the inventory data, a performance evaluation process 106a with a second type of workflow set may be generated.

[0212] In this case, in the performance evaluation process 106a with the first type of workflow set, a determination of whether it is necessary to predict the network load based on the usage amount of computer resources is executed. Also, in the performance evaluation process 106a with the second type of workflow set, a determination of whether it is necessary to predict the network load based on the usage amount of computer resources is executed.

[0213] In this way, it is possible to switch whether to consider the usage amount of computer resources in the determination of whether to start predicting the network load according to the importance of the elements to be the execution target of scale-out. Therefore, for example, for important elements, it becomes possible to start predicting the network load even in a situation where computer resources are tight.

[0214] Also, in this processing example, the performance evaluation process 106a may determine that it is necessary to predict the network load with a probability according to the usage amount of computer resources when the actual performance value of the performance index value indicated by the performance index value data satisfies a predetermined condition.

[0215] For example, in the process shown in S206, when it is determined that there are sufficient computer resources for the determination process 106, the process shown in S207 may be executed with the probability shown in FIG. 12.

[0216] For example, when the ratio of the number of the ongoing determination processes 106 to the maximum number of the predetermined determination processes 106 is less than 50%, the execution probability of the process shown in S207 may be 1. That is, the process shown in S207 may be executed.

[0217] Also, when the ratio of the number of the ongoing determination processes 106 to the maximum number of the predetermined determination processes 106 is 50% or more and less than 80%, the execution probability of the process shown in S207 may be 0.5. Here, for example, it may be determined whether to execute the process shown in S207 by using pseudo-random numbers.

[0218] Also, when the ratio of the number of the ongoing determination processes 106 to the maximum number of the predetermined determination processes 106 is 80% or more, the execution probability of the process shown in S207 may be 0. That is, the process shown in S207 may not be executed.

[0219] By doing so as described above, it becomes possible to appropriately control the number of processes generated according to the usage amount of computer resources.

[0220] Next, an example of the processing flow regarding the state determination of the communication system 1 by the prediction determination process 106b performed in the platform system 30 according to the present embodiment will be described with reference to the flowchart illustrated in FIG. 13.

[0221] In this processing example, the prediction determination process 106b monitors the output of the estimation result data from the estimation process 108 associated with the prediction determination process 106b (S301).

[0222] Then, when the output of the estimation result data from the estimation process 108 is detected, the prediction determination process 106b acquires the prediction result data (S302).

[0223] Then, the prediction determination process 106b determines whether or not a predicted value indicating the level of network load indicated by the prediction result data obtained in the process shown in S302 exceeds a threshold value th3 (S303).

[0224] If it is determined that the predicted value does not exceed the threshold value th3 (S303: N), the process returns to the process shown in S301.

[0225] If it is determined that the predicted value exceeds the threshold value th3 (S303: Y), the policy manager unit 90, the life cycle management unit 94, the container management unit 78, and the configuration management unit 76 execute horizontal scaling of the elements associated with the performance determination process 106a (S304). Then, the prediction determination process 106b ends (kills) the estimation process 108 and its own process associated with the prediction determination process 106b (S305), and the process shown in this processing example ends.

[0226] In addition, in the processing examples shown in FIGS. 10 and 11, when the estimation process 108 and the prediction determination process 106b have been generated, the performance determination process 106a may determine whether or not the performance index value indicated by the acquired performance index value data is below a threshold value th4. Then, when it is determined that the value is below the threshold value th4, the performance determination process 106a may end (kill) the estimation process 108 and the prediction determination process 106b associated with the performance determination process 106a.

[0227] Note that the present invention is not limited to the above-described embodiments.

[0228] For example, in this embodiment, instead of scaling out elements of RAN 32 such as gNB, scaling out of elements of the core network system 34 may be performed. For example, scaling out of AMF 46, SMF 48, or UPF 50 may be performed. Also, in this case, performance metric value data related to elements of the core network system 34 may be used for determining whether to perform scaling out. Alternatively, performance metric value data related to elements of RAN 32 and elements of the core network system 34 may be used for such determination.

[0229] Also, similarly, transport scaling out may be performed.

[0230] Also, the functional unit according to this embodiment is not limited to that shown in FIG. 3.

[0231] Also, the functional unit according to this embodiment does not need to be an NF in 5G. For example, the functional unit according to this embodiment may be a network node in 4G such as eNodeB, vDU, vCU, P-GW (Packet Data Network Gateway), S-GW (Serving Gateway), MME (Mobility Management Entity), HSS (Home Subscriber Server), etc.

[0232] Also, the functional unit according to this embodiment may be realized using virtualization technologies other than container-type virtualization technologies, such as hypervisor-type or host-type virtualization technologies. Also, the functional unit according to this embodiment does not need to be implemented by software and may be implemented by hardware such as an electronic circuit. Also, the functional unit according to this embodiment may be implemented by a combination of an electronic circuit and software.

[0233] The technology described in this disclosure can also be expressed as follows. [1] Performance index value data acquisition means for acquiring performance index value data indicating the actual value of the performance index related to the communication system, first determination means for determining whether prediction of network load is necessary based on the performance index value data, prediction start means for starting prediction of network load in response to a determination that prediction of network load is necessary, second determination means for determining whether scale-out is necessary based on the prediction result of network load after the prediction of network load is started, scale-out execution means for executing scale-out of elements included in the communication system in response to a determination that scale-out is necessary, A scale-out execution system, characterized by including the above. [2] Further including third determination means for determining whether scale-out is necessary based on the performance index value data without depending on the prediction result of network load, The scale-out execution means executes scale-out of elements included in the communication system in response to a determination that scale-out is necessary by the second determination means or the third determination means. The scale-out execution system according to [1], characterized by the above. [3] The first determination means determines that prediction of network load is necessary when a value indicating the level of network load indicated by the performance index value data exceeds a first threshold value, The third determination means determines that scale-out is necessary when a value indicating the level of network load indicated by the performance index value data exceeds a second threshold value greater than the first threshold value. The scale-out execution system according to [2], characterized by the above. [4] In response to the performance index value data being enqueued in the queue where the performance index value data is stored, the third determination means determines whether scale-out is necessary based on the enqueued performance index value data, and the second determination means determines whether scale-out is necessary based on a prediction result of network load based on the performance index value data of a predetermined number of most recent or a predetermined period most recently including at least the enqueued performance index value data among the performance index value data stored in the queue. The scale-out execution system according to [2] or [3], characterized in that. [5] The first determination means determines whether it is necessary to predict network load based on the performance index value data and the usage amount of computer resources. The scale-out execution system according to any one of [1] to [4], characterized in that. [6] Further comprising execution determination means for determining whether to execute determination of whether it is necessary to predict network load based on the usage amount of computer resources according to the importance of the element to be the execution target of scale-out. The scale-out execution system according to [5], characterized in that. [7] When the actual value of the performance index indicated by the performance index value data satisfies a predetermined condition, the first determination means determines that it is necessary to predict network load with a probability according to the usage amount of computer resources. The scale-out execution system according to [5] or [6], characterized in that. [8] Further comprising prediction end means for ending the prediction of network load in response to satisfying a predetermined condition. The scale-out execution system according to any one of [1] to [7], characterized in that. [9] Obtaining performance index value data indicating the actual value of the performance index related to the communication system, Determining whether it is necessary to predict network load based on the performance index value data, In response to determining that prediction of network load is necessary, start predicting the network load, and After the prediction of the network load is started, determine whether scale-out is necessary based on the prediction result of the network load, and In response to determining that scale-out is necessary, execute scale-out of elements included in the communication system, and A scale-out execution method characterized by including the above.

Claims

[

1. ] A performance index value data acquisition process for acquiring performance index value data indicating an actual value of a performance index value related to a communication system, a first determination process for determining whether prediction of network load is necessary based on the performance index value data, a prediction start process for starting prediction of network load in response to a determination that prediction of network load is necessary, a second determination process for determining whether scale-out is necessary based on a prediction result of network load after the prediction of network load is started, and a scale-out execution process for executing scale-out of elements included in the communication system in response to a determination that scale-out is necessary, and in the first determination process, when the actual value of the performance index value indicated by the performance index value data satisfies a predetermined condition, it is determined with a probability corresponding to the usage amount of computer resources that prediction of network load is necessary. A scale-out execution system. [

2. ] Further executing a third determination process for determining whether scale-out is necessary based on the performance index value data without depending on a prediction result of network load, and in the scale-out execution process, scale-out of elements included in the communication system is executed in response to a determination that scale-out is necessary by the second determination process or the third determination process. The scale-out execution system according to claim 1. [

3. ] The predetermined condition is a condition that a value indicating the height of the network load indicated by the performance index value data exceeds a first threshold, and in the third determination process, when a value indicating the height of the network load indicated by the performance index value data exceeds a second threshold greater than the first threshold, it is determined that scale-out is necessary. The scale-out execution system according to claim 2. [

4. ] In response to the performance index value data being enqueued in a queue in which the performance index value data is stored, in the third determination process, based on the enqueued performance index value data, it is determined whether scale-out is necessary, and in the second determination process, based on a prediction result of network load based on the performance index value data of a predetermined number of most recent or a predetermined period most recent including at least the enqueued performance index value data among the performance index value data stored in the queue, it is determined whether scale-out is necessary. The scale-out execution system according to claim 2.

5. An execution determination process for determining whether to execute a determination of the necessity of predicting a network load based on the amount of computer resource usage according to the importance of an element to be subjected to scale-out is further executed. The scale-out execution system according to claim 1.

6. A prediction end process for ending the prediction of the network load is further executed in response to satisfying a predetermined condition. The scale-out execution system according to claim 1.

7. Obtaining performance index value data indicating an actual value of a performance index value related to a communication system; Determining the necessity of predicting a network load based on the performance index value data; Starting the prediction of the network load in response to being determined that the prediction of the network load is necessary; After the prediction of the network load is started, determining the necessity of scale-out based on the prediction result of the network load; Including executing scale-out of an element included in the communication system in response to being determined that scale-out is necessary, In the determination of the necessity of predicting the network load, when the actual value of the performance index value indicated by the performance index value data satisfies a predetermined condition, it is determined that the prediction of the network load is necessary with a probability according to the amount of computer resource usage. A scale-out execution method executed by one or more computers.

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