Network management

According to an example aspect of the present invention, there is provided a system comprising a memory configured to store information characterizing network management actions that have occurred in the past, and at least one processing core configured to initiate a network management action based at least in part on the stored information, the network management action involving at least one virtualized network function.

PRIORITY

This application is a U.S. national application of PCT-application PCT/FI2015/050167 filed on Mar. 13, 2015, the contents of all of which are incorporated herein by reference.

FIELD OF INVENTION

The present invention relates to the field of network management, such as for example management of a communication network with virtualized functions.

BACKGROUND OF INVENTION

Communication networks, such as for example cellular communication networks, are comprised of network nodes. The network nodes of a network may be subdivided into different node types, for example, a cellular communication network may comprise base stations, base station controllers, switches, gateways and application functions. An internet protocol, IP, network may comprise routers and gateways.

When designing a network, planners may estimate loading situations in a coverage area of the network. For example, in busy sections of cities it may be estimated that communication occurs more often, and at a higher intensity, than in outlying areas. Therefore, in the case of a cellular network, cells in busier areas may be made smaller, and base stations assigned to control these smaller cells may be furnished with sufficient data processing capability to handle high peak loads. For example, the base stations may be equipped with several data processing cards. Likewise, network nodes tasked with conveying data to and from base stations with high anticipated peak loads may be dimensioned to be capable of handling these high loads.

When network nodes dimensioned to handle high loads experience lower loads, for example at night, their instantaneous load factors may be low. On the other hand, in case usage patterns of a network change, for example, if an area that previously experienced only low usage becomes more highly loaded, capacity may be added to base stations and, possibly, to other nodes serving that area of the network. Such adding may involve installing more data processing cards and/or transceivers into base stations to cope with the load. Peak loads may increase in previously lightly loaded areas as cities or other areas develop. For example, the opening of a new airport, train station or simply the construction of a new apartment building may modify usage patterns of communication networks that are active in the area.

Virtualization of network functions may be employed to simplify network maintenance. In a network where functions have been, at least in part, virtualized, virtualized network functions may be run as software entities on commodity server computers, which may be located in a datacentre, for example.

SUMMARY OF THE INVENTION

According to a first aspect of the present invention, there is provided a system comprising a memory configured to store information characterizing network management actions that have occurred in the past, and at least one processing core configured to initiate a network management action based at least in part on the stored information, the network management action involving at least one virtualized network function.

Various embodiments of the first aspect may comprise at least one feature from the following bulleted list:the network management action comprises at least one of the following: increasing resources allocated to a virtualized network function, decreasing resources allocated to a virtualized network function, starting a virtualized network function instance, and terminating a virtualized network function instancethe information characterizing network management actions that have occurred in the past comprises at least one of the following: information identifying when past network management actions have occurred, at least one pattern identified in past network management actions, and at least one propensity score associating a network management action with a network operating conditionthe at least one processing core is configured to initiate the network management action based jointly on the at least one propensity score and the network operating conditionthe at least one virtualized network function comprises at least one of the following: a virtualized base station function, a virtualized call session control function, CSCF, a virtualized switching centre, a virtualized mobility management entity, a virtualized home subscriber server, a virtualized equipment identity register and a virtualized gateway functionthe system is configured to override a first network management action request based at least in part on the stored informationthe first network management action request comprises a request to terminate a first virtualized network function and the stored information indicates that increased loading is likely in the near futurethe first network management action request comprises a request to increase resources allocated to a first virtual network function, and the at least one processing core is configured to, based at least in part on the stored information, increase resources allocated to a second virtualized network function and leave resources of the first virtualized network function unmodifiedthe memory and the at least one processing core are comprised in a virtualized cellular communication network management device and the system further comprises a network functions virtualization orchestrator function configured to, responsive to a trigger from the virtualized cellular communication network management device, cause the network management action to be implementedthe system comprises a first node configured to determine, based on the information identifying when past network management actions have occurred, at least one of the at least one pattern and the at least one propensity scorethe first node comprises the virtualized cellular communication network management device

According to a second aspect of the present invention, there is provided a method comprising storing information characterizing network management actions that have occurred in the past, and initiating a network management action based at least in part on the stored information, the network management action involving at least one virtualized network function.

Various embodiments of the second aspect may comprise at least one feature corresponding to a feature comprised in the preceding bulleted list laid out in connection with the first aspect.

According to a third aspect of the present invention, there is provided a database or big data storage comprising a memory configured to store information characterizing network management actions involving at least one virtualized network function that have occurred in the past, and at least one pattern characterizing the information.

According to a fourth aspect of the present invention, there is provided an apparatus comprising at least one processing core and at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processing core, cause the apparatus at least to store information characterizing network management actions that have occurred in the past, and initiate a network management action based at least in part on the stored information, the network management action involving at least one virtualized network function.

According to a fifth aspect of the present invention, there is provided an apparatus comprising means for storing information characterizing network management actions that have occurred in the past, and means for initiating a network management action based at least in part on the stored information, the network management action involving at least one virtualized network function.

According to a sixth aspect of the present invention, there is provided a non-transitory computer readable medium having stored thereon a set of computer readable instructions that, when executed by at least one processor, cause an apparatus to at least store information characterizing network management actions that have occurred in the past, and initiate a network management action based at least in part on the stored information, the network management action involving at least one virtualized network function.

According to a seventh aspect of the present invention, there is provided a method, comprising predictively triggering, from a predictive entity, a network management action relating to a virtualized network function, responsive to the triggering, requesting implementation of the network management action by transmitting an instruction from a network function virtualization orchestrator function to a virtual infrastructure manager function, and responsive to a message from the virtual infrastructure manager function, requesting configuration relating to the network management action by transmitting a message from the network function virtualization orchestrator function to a virtualized network function manager function.

Various embodiments of the seventh aspect may comprise at least one of the following two features:the configuration is caused by causing at least one message to be transmitted from the virtualized network function manager to the virtualized network functionresponsive to the triggering, querying by the network function virtualization orchestrator function from the virtualized network function manager function what resources are needed for implementing the network management action, and wherein the implementation is requested based at least in part on a response to the query.

According to an eighth aspect of the present invention, there is provided a computer program configured to cause a method in accordance with at least one of the second or seventh aspects to be performed.

INDUSTRIAL APPLICABILITY

At least some embodiments of the present invention find industrial application in network management and use optimization.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

In networks employing virtualization of at least some functions thereof, preemptive network management actions may be triggered based on predictive methods to react more fluently to changes in network usage. Capacity may be adaptively increased and decreased, respectively, where usage is foreseen to increase or decrease, for example. A utility may thereby be obtained where computational capacity is used more efficiently and/or outage situations may be avoided as changes are initiated already before the usage changes that necessitate the changes occur.

FIG. 1Aillustrates an example system for explaining at least some embodiments of the present invention. In a cellular network example, mobile110is in communication with base station120via wireless link112. Mobile110may comprise, for example, a smartphone, a phablet, a cellular phone, a tablet computer or a laptop computer. Wireless link112may be arranged to operate in accordance with a wireless technology that both mobile110and base station120are configured to support. Suitable technologies include, for example, wideband code division multiple access, WCDMA, long term evolution, LTE, and CDMA2000.

Base station120is operably connected to core network node140. Core network node140may comprise, for example, a mobility management entity, MME, or a router. Core network node140is further operably connected to further core network node150. Further core network node150may comprise a gateway, for example, configured to provide access to further networks, such as the Internet, for example. Thus mobile110may obtain access to the further networks via base station120, core network node140and further core network node150. Base station120may be dimensioned to be capable of serving a maximum load in the location where the cell or cells it controls are located.

Base station130may be similar to base station120, and comprised in the same radio-access network as base station120. Like base station120, also base station130is operably connected to core network node140. The connections between the base stations and core network node140may be wire-line connections, or alternatively they may be at least in part wireless.

Although discussed in terms of a cellular network, a non-cellular network would equally serve to illustrate an example system. Examples of non-cellular technologies include wireless local area network, WLAN, also known as Wi-Fi, and worldwide interoperability for microwave access, WiMAX. Embodiments of the present invention may also be applied, in suitable form, to wire-line networks, such as IP networks, where wireless links do not occur.

FIG. 1Billustrates an example system capable of supporting at least some embodiments of the present invention. Like numbering inFIG. 1Bdenotes like structure as inFIG. 1A. In the system ofFIG. 1B, mobile110has a wireless link112with radio node122. Radio node122is a reduced version of base station120comprising radio hardware but less, or no, information processing functions. Radio node132likewise takes the place of base station130in the system ofFIG. 1B. Radio node122and radio node132are both separately connected with server1V1, which comprises a computer system configured with computing resources, such as processing cores and memory, arranged to be able to run the information processing capabilities of base stations ofFIG. 1Athat are absent in the radio nodes ofFIG. 1B. In other words, compared to the system ofFIG. 1A, at least part of the information processing functions of base stations120and130have been moved to server1V1in the system ofFIG. 1B.

The information processing functions relating to radio node122that take place in server1V1are denoted as virtualized base station120v. The information processing functions relating to radio node132that take place in server1V1are denoted as virtualized base station130v.

Server1V2is in the system ofFIG. 1Bconfigured to run virtualized versions of core network nodes. In the system ofFIG. 1B, server1V2runs a virtualized core network node140voffering the same functionality as core network node140inFIG. 1A, and a virtualized further core network node150V offering the same functionality as further core network node150inFIG. 1A. In use virtualized base station120vmay receive information from radio node122and perform processing operations on the received information. For example, virtualized base station120vmay forward information it has processed to virtualized core network node140vin server1V2.

Servers1V1and1V2may be based on generic computation technology, such as a set of x86-architecture multi-core processors or reduced instruction set computing, RISC, processors, for example. Server1V1need not be based on the same computation technology as server1V2.

In general, a virtualized network function may comprise a software entity on generic computing hardware that is configured to perform according to a same specification as a corresponding network function that has not been virtualized, that is, one that runs on dedicated hardware. In other words, a virtualized network function may comprise a software implementation of a logical network node of a communication network. This has the effect that in terms of other network elements, these other elements needn't know whether the network element has been virtualized or no. Therefore, a virtualized call session control function, CSCF, for example, can be sent the same kinds of messages as a non-virtualized CSCF.

The system ofFIG. 1Boffers advantages over the system ofFIG. 1A. In detail, virtualized base station120vmay be scaled according to need, whereas base station120must be dimensioned for the maximum expected load at all times. For example, when load is light, virtualized base station120vmay be run with a few, or only one, processing core in server1V1, while other processing cores of server1V1may be used for other processing tasks, such as grid computing, for example. As a response to increasing load via radio node122, virtualized base station120vmay be allocated more processing cores in a dynamic network management action.

The system ofFIG. 1Bmay record network management actions that are performed, each network management action involving at least one virtualized network function. Virtualized network functions may comprise, for example, virtualized base stations and/or virtualized core network nodes. The network management action may comprises at least one of the following: increasing resources allocated to a virtualized network function, decreasing resources allocated to a virtualized network function, starting a virtualized network function instance, and terminating a virtualized network function instance.

Increasing resources allocated to a virtualized network function may comprise allocating at least one more processing core and/or more memory to the virtualized network function. Alternatively to allocating an entire processing core, a timeshare of a processing core may be allocated to the virtualized network function. A timeshare may comprise, for example 25% of clock cycles or 50% of clock cycles of a processing core. Decreasing resources allocated to a virtualized network function may comprise de-allocating a processing core, a timeshare of a processing core and/or memory from the virtualized network function.

Starting a virtualized network function instance may comprise initializing, for example based at least in part on a template, a new virtualized network function. In terms ofFIG. 1B, this might comprise, for example, initializing a further virtualized core network node, “160v” in server1V2. The new virtualized network function, or node, may be allocated resources in terms of at least one processor core and memory. Terminating a virtualized network function may correspondingly comprise ending processor tasks that run the virtualized network function. Terminating may be smooth in nature, wherein, for example, any users served by a virtualized network function that is to be terminated may be handed over to another virtualized network function, to avoid broken connections.

Alternatively to two servers1V1and1V2, another number of servers may be employed in dependence of the embodiment and network implementation. For example, one, three or seven servers may be used.

The system ofFIG. 1Bmay perform network management actions responsive to load status, congestion or user intervention. When the system has access to recorded information concerning past network management actions, the system may use the recorded information to predict changes in network usage, to pre-emptively, or predictively, perform network management actions. For example, if the network has knowledge of a usage pattern according to which usage will increase in a certain segment of the network, it may re-allocate resources to virtualized network functions that serve that segment. Usage patterns may occur, for example, as users travel to and from work, for example to consume streaming media content at home in the evenings. Thus residential areas may see comparatively little traffic during the day, but relatively heavy data usage in the evenings.

Where the network can act predictively, as opposed to responsive to already developing overload, high load or low load situations, the network can reallocate resources in a smoother way. In some cases, without predictive operation outage might occur in case load picks up rapidly in a segment of the network and the network cannot adapt to the changing load fast enough. Using a predictive approach, resources may be freed from other uses before the need to allocate them to virtualized network functions with increasing load occurs. In other words, predictive resource allocation increases the flexibility at which a network operator can, for example, rent out unused capacity to grid computing. The network can smoothly run down or hibernate grid computing tasks, for example, and begin re-allocating resources, such as processing cores and/or memory, based on a predicted need.

A database or big data storage such as Hadoop may be arranged to store information characterizing network management actions involving at least one virtualized network function that have occurred in the past, and at least one pattern characterizing the information. A management node may comprise such a database, or big data storage system, to predictively trigger network management actions.

In some embodiments, a network may use a predictive approach to override network management action requests. For example, where a virtualized network function reports such a low load factor as to normally cause a termination of the virtualized network function, the network may decide to retain the virtualized network function in case the network predictively expects traffic to increase for the virtualized network function concerned in the near future. As another example, where a virtualized network function requests additional resources to be allocated to it due to load, the network may decide that in light of expected development in usage of the network, a more appropriate change is to increase the resources of another virtualized network function. In that case, the network may leave the requesting virtualized network function with unchanged resources, but increase resources allocated to another virtualized network function to enable the network to flexibly cope with dynamically developing usage. A request for additional resources may be implicit, for example, a virtualized network function may report a high load, responsive to which the network would normally allocate additional resources to reduce the load factor of the affected virtualized network function.

FIG. 2illustrates an example network architecture in accordance with at least some embodiments of the present invention. InFIG. 2, VNF210comprises a virtualized network function, such as for example a virtualized network function as described above in connection withFIG. 1B. VNF210has an interface with VNF manager230, wherein VNF manager230may be configured to initiate network management actions, for example, responsive to changes in a loading level of virtualized network functions. VNF manager230has an interface with virtualized infrastructure manager, VIM,220. VIM220may implement a monitoring function to detect virtualized network functions that cross loading or other predefined thresholds, to responsively trigger network management actions. For example, where a loading level exceeds a first threshold, more resources may be allocated to the virtualized network function, and/or where the loading level decreases below a second threshold, resources may be allocated from the virtualized network function to other uses. NFVO270and/or another node may be configured to respond to predefined or machine-learned propensity scores, wherein a propensity score associates a network management action with at least one operating condition of the network. The architecture may comprise a plurality of VNFs210, VIMs220and/or a plurality of VNF managers230.

Both VNF210and VIM220have interfaces to network functions virtualization infrastructure, NFVI,240. NFVI240may provide a set of hardware and software components that build up the environment in which VNFs are deployed. VNF210further has an interface with element manager, EM,250. EM250may provide end-user functions for management of a set of related types of network elements which may include network elements with virtualized network functions or non-virtualized network functions, or both. These functions may be divided into two main categories: Element Management Functions and Sub-Network Management Functions. In some embodiments, EM250may be configured to take decisions concerning network management actions, and to cause the decisions to be implemented by signalling concerning them to VNF manager230, for example. EM250has an interface with operational support systems and/or business support systems, OSS/BSS260. OSS/BSS260may be configured to support end-to-end telecommunication services. OSS/BSS260may implement load monitoring. OSS/BSS260in turn has an interface with NFV Orchestrator, NFVO,270. NFVO270may comprise a functional block that manages network service, NS, lifecycles and coordinates the management of the NS lifecycles, VNF lifecycles and NFVI240resources to ensure an optimized allocation of resources and connectivity. NFVO270has interfaces with each of NS catalogue272, VNF catalogue274, network functions virtualization, NFV, instances276and NFVI resources278. VIM220may further have an interface with NFVO270. VNF manager230may likewise have an interface with NFVO270.

In some embodiments, a predictive entity280is configured to act predictively and trigger network management actions in the system illustrated inFIG. 2. For example, predictive entity280may instruct NFVO270to implement a predictively determined network management action in the network ofFIG. 2. Predictive entity280has an interface, such as for example a standard interface, with NFVO270. To enable this, predictive entity may be configured to receive and collect information on network management actions performed in the network ofFIG. 2and determine patterns therein. Predictive entity280may be arranged to receive information from at least one VNF210, and/or at least one VIM220and/or at least one NFVI240. Alternatively, predictive entity280may be configured with information describing patterns it is to use in predictively triggering network management actions.

Predictive entity280may comprise an interface to NFV210and/or NFVI240, and a node configured to collect data therefrom. Predictive entity280may comprise a mediation service, a database or a big data storage for storing the collected data, a node configured to analyse the data, for example in real time, and to create a predictive analysis based on the data, and a node/interface configured to inform NFVO270directly or through OSS/BSS260. Predictive entity280can be run as a virtual service.

In some embodiments, to implement a predictively determined network management resource scaling action, NFVO270instructs VIM220to give additional resources for at least one VNF210. When VIM220acknowledges the additional resources, the NFVO270may inform the VNFM230to scale resources. As an option the NFVO270may first request the VNFM230, if the VNF's are allowed to scale and what resources are needed to scale. In case of scaling down or in, NFVO270may inform VNFM230to scale VNF's, VNFM230may scale the resources and inform NFVO270about it, then NFVO270may inform the VIM220these resources are no longer used and the VIM220may then do so. It can also inform the NFVO270that the resources are no longer available.

In various embodiments, at least two entities illustrated inFIG. 2comprise software entities arranged to run on the same hardware resource.

WhileFIG. 2illustrates one example architecture, other architectures are possible in different embodiments of the invention. For example, where the network is an internet protocol, IP, network, the architecture may be simpler than that illustrated inFIG. 2.

FIG. 3illustrates an example system capable of supporting at least some embodiments of the present invention. Illustrated is device300, which may comprise, for example, a control node configured to control, at least in part, the operation of the network illustrated inFIG. 1BorFIG. 2. Device300may comprise one of the entities illustrated in the architecture ofFIG. 2that may perform monitoring and/or triggering of network management actions. Alternatively, device300may be comprised in a node not illustrated inFIG. 2, wherein device300may be configured to provide instructions to at least one node comprised inFIG. 2. Comprised in device300is processor310, which may comprise, for example, a single- or multi-core processor wherein a single-core processor comprises one processing core and a multi-core processor comprises more than one processing core. Processor310may comprise more than one processor. A processing core may comprise, for example, a Cortex-A8 processing core manufactured by ARM Holdings or a Steamroller processing core produced by Advanced Micro Devices Corporation. Processor310may comprise at least one Intel Core, Intel Xeon, AMD Opteron and/or Intel Atom processor. Processor310may comprise at least one application-specific integrated circuit, ASIC. Processor310may comprise at least one field-programmable gate array, FPGA. Processor310may be means for performing method steps in device300. Processor310may be configured, at least in part by computer instructions, to perform actions.

Device300may comprise memory320. Memory320may comprise random-access memory and/or permanent memory. Memory320may comprise at least one RAM chip. Memory320may comprise solid-state, magnetic, optical and/or holographic memory, for example. Memory320may be at least in part accessible to processor310. Memory320may be means for storing information. Memory320may comprise computer instructions that processor310is configured to execute. When computer instructions configured to cause processor310to perform certain actions are stored in memory320, and device300overall is configured to run under the direction of processor310using computer instructions from memory320, processor310and/or its at least one processing core may be considered to be configured to perform said certain actions. Memory320may be at least in part comprised in processor310. Memory320may be at least in part external to device300but accessible to device300.

Device300may comprise a transmitter330. Device300may comprise a receiver340. Transmitter330and receiver340may be configured to transmit and receive, respectively, information in accordance with at least one communication standard. Transmitter330may comprise more than one transmitter. Receiver340may comprise more than one receiver. Transmitter330and/or receiver340may be configured to operate in accordance with Ethernet and/or Diameter communication standards, for example.

Device300may comprise user interface, UI,360. UI360may comprise at least one of a display, a keyboard, a touchscreen, a vibrator arranged to signal to a user by causing device300to vibrate, a speaker and a microphone. A user may be able to operate device300via UI360, for example to initiate network management actions or configure loading thresholds.

Processor310may be furnished with a transmitter arranged to output information from processor310, via electrical leads internal to device300, to other devices comprised in device300. Such a transmitter may comprise a serial bus transmitter arranged to, for example, output information via at least one electrical lead to memory320for storage therein. Alternatively to a serial bus, the transmitter may comprise a parallel bus transmitter. Likewise processor310may comprise a receiver arranged to receive information in processor310, via electrical leads internal to device300, from other devices comprised in device300. Such a receiver may comprise a serial bus receiver arranged to, for example, receive information via at least one electrical lead from receiver340for processing in processor310. Alternatively to a serial bus, the receiver may comprise a parallel bus receiver.

Device300may comprise further devices not illustrated inFIG. 3. For example, device300may comprise a fingerprint scanner to authenticate users. In some embodiments, device300lacks at least one device described above.

Processor310, memory320, transmitter330and/or receiver340may be interconnected by electrical leads internal to device300in a multitude of different ways. For example, each of the aforementioned devices may be separately connected to a master bus internal to device300, to allow for the devices to exchange information. However, as the skilled person will appreciate, this is only one example and depending on the embodiment various ways of interconnecting at least two of the aforementioned devices may be selected without departing from the scope of the present invention.

FIG. 4illustrates signalling in accordance with at least some embodiments of the present invention. On the vertical axes are disposed, from left to right, a set of virtualized network functions, VNFs, on the left, a control node300, on the right. Control node300may correspond to the device300ofFIG. 3, or predictive entity280ofFIG. 2for example. In the lower part ofFIG. 4there is a further vertical axis labelled VNF_x. The set of VNFs may change as time goes by, and the figure should not be understood so that the number of VNFs in the set should always be three, or any constant number. Control node300is in the illustrated example architecture tasked with managing network management actions with regard to VNFs, however the invention is by no means limited thereto, rather, in different embodiments other functions, or sets of functions may perform at least part of the actions attributed to control node300inFIG. 4. Time advances from the top toward the bottom. Messaging between control node300and the VNFs may be conducted via at least one intermediate node, such as for example an NFVO and/or VIM as described in connection withFIG. 2.

In collective phase410, the VNFs provide indications to control node300. The indications of phase410may comprise load indications advising control node300of load situations in individual VNFs, for example. In phase420, control node300may analyse the indications received in phase410and determine to take actions based, at least in part, on these indications. In phase420, control node300may store information concerning the indications of phase410. The stored information may describe, for example, timestamps of received indications that enable determination of patterns in the indications of phase410. Patterns may comprise temporal patterns, for example indicating at what times of day certain sections of the network experience high and/or low load conditions. Determination of patterns in the indications may be performed by control node300, or by another node with which control node300shares the indications, at least in part. Another example of a pattern that may be derived based on the indications of phase410is a propensity score. A propensity score may indicate, for example, a likelihood that increasing or decreasing resources in a specific VNF will be appropriate if certain network conditions exist. For example, a propensity score may indicate a statistical probability that increasing resources will become necessary within 10 minutes, in case a load factor of a specific VNF increases by 30% within a previous 10 minutes.

In phase430, control node300may initiate at least one network management action with regard to the set of VNFs, at least in part responsive to the indications received in phase410. The network management action may comprise, for example, a network management action of a type described above. Phases410,420and430may overlap in time, such that control node300may initiate a network management action responsive to an individual indication received from one VNF comprised in the set of VNFs. Phases410,420and430may be seen as a reactive stage of operation, where the network reacts to indications received from VNFs and also gathers information enabling it to begin acting, at least partly, in a predictive way later on.

Phase440comprises control node300acting predictively based at least in part on the information stored in phase420. For example, where control node300determines that virtualized base station resources usually need to be increased in a certain section of the network at 18:00 in the evening, control node300may increase the resources already at 17:55. Thus as usage increases at 18:00, a load factor of a virtualized base station does not increase as much as it would have, had the resources not been already increased five minutes earlier. Phase450illustrates an example instruction from control node300to increase resources in a VNF. In phase460, control node300predictively starts a new virtualized network function instance based at least in part on the information stored in phase420. The new virtualized network function, VNF_x, may comprise a virtualized call session control function, for example. VNF_x may be initialized in a same physical computer or computer set as the other VNFs ofFIG. 4, for example. In at least some embodiments, addition to increasing resources of a VNF and starting a new VNF, control node300may instruct resources of a VNF to be reduced, or that a VNF is to be terminated, as discussed above.

FIG. 5is a first flow chart of a first method in accordance with at least some embodiments of the present invention. The phases of the illustrated method may take place in a VNFM or other network management node comprised in a network that employs, at least in part, network function virtualization.

Phase510comprises storing information characterizing network management actions that have occurred in the past. Phase520comprises initiating a network management action based at least in part on the stored information, the network management action involving at least one virtualized network function. Optional phase530comprises, responsive to an acknowledgement concerning the network management action, causing configuration of the at least one virtualized network function.

FIG. 6illustrates signalling in accordance with at least some embodiments of the present invention. On the vertical axes are disposed, from left to right, an NFVO, a VNF Manager VNFM, a VNF, a VIM, a mediation function MED, a machine learning function LRN, an analytics engine ANL and, finally, a sender SND. Mediation function MED, machine learning function LRN, sender SND and analytics engine ANL may be functions comprised in a predictive entity such as predictive entity280ofFIG. 2, for example. Sender SND may be seen, in general, as a function of predictive entity280that is tasked with communicating with the NFVO and, optionally, other nodes in the network. Sender SND may be, at least in part, comprised in at least one of mediation function MED, machine learning function LRN and/or analytics engine ANL.

Phase610comprises the mediation function MED receiving, from the VIM and/or VNFI, information concerning VNF utilisation of resources. Phase620may comprise the mediation function MED receiving from at least one VNF information on operation of the VNF, for example, in the form of systems logs and parameters. Application status information may be provided in phase620. The messaging of phases610and620may be initiated by the VNF and VNFI respectively, or the information may be requested, and responsively received, by mediation function MED.

Phase630may comprise the mediation function MED providing information, such as for example enhanced information determined based on the information received in mediation node MED during phases610and/or620, to machine learning function LRN, which may be configured to apply at least one machine learning principle to the information to thereby determine at least one pattern and/or propensity score. The information provided by mediation function MED may be filtered and/or enriched with further information from third parties or databases. In some embodiments, the information provided by mediation function MED is simply formatted into a format that machine learning function LRN can process it.

In phase640, machine learning function LRN makes the determined at least one pattern and/or propensity score, or other determined characteristic, available to analytics engine ANL.

In phase650, analytics engine ANL predictively initiates a network management action, as described above. In the example ofFIG. 6, the network management action comprises a scale-out action where a VNF is instantiated pre-emptively, without waiting for an indication the system is highly loaded. A high-load indication could be delivered, for example, by notifying the VNFM. The triggering signal of phase650is fed to the NFVO in phase660to thereby cause its implementation.

Responsive to phase660, the NFVO may validate the request, for example by checking a credential comprised in the message of phase660. In phase670, the NFVO signals to the VNFM to instantiate the VNF. The VNFM may validate the request of phase670, and return, in phase680, a resource allocation instruction to the NFVO.

Phase670may optionally further comprise the NFVO informing the VNFM of need to scale. If phase670comprises this optional phase, phase680may further comprise the VNFM informing the NFVO of resources needed for scaling. Also NFVO may check from the VNFM, whether the VNF is allowed to scale.

In phase690, a resource allocation and/or interconnection setup message is transmitted from the NFVO to the VIM. This message may relate to allocation of computational, storage and/or networking resources to be allocated, for example. Responsively, the VIM may allocate the resources and virtual machines, and attach them to the network in accordance with the interconnection setup. Once the allocation is complete, the VIM may acknowledge completion of the allocation requested in phase690by transmitting a message, which is illustrated inFIG. 6as phase6100. The NFVO may acknowledge the resource allocation to the VNFM, phase6110.

In some embodiments, the VNFM may notify an element manager, EM, of successful VNF instantiation as a response to the message of phase6110. This is not illustrated inFIG. 6. The EM may responsively configure, at least in part, the VNF by signalling to the VNF. This configuration may relate to application specific parameters, for example.

In phase6120, the VNFM configures, at least in part, the newly instantiated VNF. This configuration may relate to deployment specific parameters, for example. In phase6130, the VNFM may acknowledge VNF instantiation to the NFVO, and in phase6140the NFVO may acknowledge VNF instantiation to the predictive entity280.