CN115001985B

CN115001985B - Multi-service-oriented virtual network function high-availability deployment method

Info

Publication number: CN115001985B
Application number: CN202110200521.1A
Authority: CN
Inventors: 李华楠; 徐洪磊; 张雪; 王栋; 张鑫
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2021-02-23
Filing date: 2021-02-23
Publication date: 2024-04-09
Anticipated expiration: 2041-02-23
Also published as: CN115001985A

Abstract

The present disclosure relates to a high availability deployment method for a virtual network function oriented to multiple services, comprising the steps of: replacing an original Virtual Network Function (VNF) with an equivalent VNF, the equivalent VNF being a diverse set of sub-instances SubVNF1, subVNF2, … …, subVNFN comprising N sub-instances, wherein N is a diversity factor greater than or equal to 2, each of the N sub-instances comprising one or more sub-functions provided by a micro-service; calculating an equivalent VNF gain according to a gain coefficient defined for each micro service, the availability of each micro service and the resource requirement of each micro service; under constraint conditions, solving a diversity coefficient N to maximize the equivalent VNF gain and obtain the maximum granularity of the VNF sub-examples; setting the resource requirement of a single backup instance according to the maximum granularity of the VNF sub-instances, and setting one or more backup instances depending on the service requirement; and deploying the N sub-instances in the equivalent VNF on different physical nodes through distributed deployment.

Description

Multi-service-oriented virtual network function high-availability deployment method

Technical Field

The present disclosure relates generally to the field of telecommunications carrier network operation maintenance, and in particular to reliability issues for network services in 5G networks.

Background

The 3GPP all-round in 2018 6 months formally establishes the 5G NR independent group function through freezing the SA network architecture standard, has the capability of independent deployment, and develops a brand new end-to-end network architecture in front of the world, thereby generating new requirements of network reconstruction. SDN/NFV/slice technology needs to be synchronously used in cooperation with new network elements and interfaces in a new network architecture so as to meet the requirements of flexible service adaptation, flexible deployment and capability opening.

Under the new network architecture, the network logic control function is abstracted into network components which can be deployed independently, and the network components are placed and deployed on a reasonable position according to service requirements so as to realize the control of the network. Network function virtualization realizes the network function on the original special hardware through software deployed on a general server, and simultaneously decouples the network function from the special hardware, so that some challenges, such as reduction of network service reliability, are brought forward, corresponding solutions are required for high availability of virtual network functions, and services deployed on the general server are ensured to meet service requirements.

Disclosure of Invention

The following presents a simplified summary of the disclosure in order to provide a basic understanding of some aspects of the disclosure. However, it should be understood that this summary is not an exhaustive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure or to delineate the scope of the disclosure. Its purpose is to present some concepts related to the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

It is an object of the present disclosure to provide a network function high availability deployment method of functional diversity.

According to one aspect of the present disclosure, there is provided a method for deploying a high availability virtual network function for multiple services, including the steps of:

replacing an original Virtual Network Function (VNF) with an equivalent VNF, the equivalent VNF being a diverse set of sub-instances SubVNF1, subVNF2, … …, subVNFN comprising N sub-instances, wherein N is a diversity factor greater than or equal to 2, each of the N sub-instances comprising one or more sub-functions provided by a micro-service;

calculating an equivalent VNF gain according to a gain coefficient defined for each micro service, the availability of each micro service and the resource requirement of each micro service;

under constraint conditions, solving a diversity coefficient N to maximize the equivalent VNF gain and obtain the maximum granularity of the VNF sub-examples;

setting the resource requirement of a single backup instance according to the maximum granularity of the VNF sub-instances, and setting one or more backup instances depending on the service requirement; and

through distributed deployment, N sub-instances in the equivalent VNF are deployed on different physical nodes.

Other features of the present invention and its advantages will become apparent from the following detailed description of the preferred embodiments of the invention.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.

The disclosure may be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings in which:

fig. 1 shows a flow chart of a method for high availability deployment of multi-service oriented virtual network functions according to the present invention.

Fig. 2 illustrates an example of replacing an original Virtual Network Function (VNF) with an equivalent VNF.

Fig. 3 illustrates one embodiment of the present invention.

Fig. 4 schematically illustrates an exemplary configuration of a computing device capable of implementing embodiments in accordance with the present disclosure.

Detailed Description

The following detailed description is made with reference to the accompanying drawings and is provided to aid in a comprehensive understanding of various exemplary embodiments of the disclosure. The following description includes various details to aid in understanding, but these are to be considered merely examples and are not intended to limit the disclosure, which is defined by the appended claims and their equivalents. The words and phrases used in the following description are only intended to provide a clear and consistent understanding of the present disclosure. In addition, descriptions of well-known structures, functions and configurations may be omitted for clarity and conciseness. Those of ordinary skill in the art will recognize that various changes and modifications of the examples described herein can be made without departing from the spirit and scope of the present disclosure.

The VNF diverse sub-instance set deployment method is exemplarily described in detail below with reference to the accompanying drawings.

First, as shown in step S101, the original Virtual Network Function (VNF) is replaced with an equivalent VNF, which is a diversified sub-instance set (SubVNF 1, subVNF2, … …, subVNFN) containing N sub-instances, where N is a diversity factor greater than or equal to 2, each of the N sub-instances containing one or more sub-functions provided by the micro service.

More specifically, one VNF instance is composed of a plurality of micro services, and may have a plurality of functions. According to the resource and the loaded service required by each micro service, the proportion relation between the resource and the service can be obtained, according to the proportion relation, the micro service is subjected to resource refinement and can be split into a plurality of independent units, and each unit can finish the same service of the micro service. The equivalent diversity VNF sub-instance sets { SubVNF1, subVNF2, … … } that can satisfy different traffic models can be obtained by combining different micro-service sub-units. The sum of the traffic capabilities of the sub-instances is sufficient to match the original VNF. Ensuring that all scheduled traffic is not affected.

As a specific example, fig. 2 specifically illustrates an example in which an original Virtual Network Function (VNF) is replaced with an equivalent VNF.

For example, a Virtual Network Function (VNF) includes a micro service 1 and a micro service 2, the micro service 1 includes a sub-function 1 and a sub-function 2, and the micro service 2 includes a sub-function 3. And combining different micro-service subunits to obtain the equivalent diversity VNF sub-instance set which can meet different service models as shown in the figure.

Then, as shown in step S102, an equivalent VNF gain is calculated according to the gain coefficient defined for each micro service, the availability of each micro service, and the resource requirement of each micro service.

Specific examples of the implementation of the steps are given below in detail as non-limiting examples. It will be appreciated by those skilled in the art that the present invention is not limited to the non-limiting examples detailed below, and in particular to the specific manner in which gain coefficients are defined for each micro-service, the specific manner in which availability of each micro-service is indicated, the specific manner in which resource requirements for each micro-service are indicated, and the specific manner in which equivalent VNF gains are calculated.

For example, in case the original VNF contains micro services S1, S2, …, SN, the availability of the original VNF may be represented by the following matrix.

Where a (VNF) represents availability of the original VNF, a (S1) represents availability of the micro service S1, a (S2) represents availability of the micro service S2, and so on, a (SN) represents availability of the micro service SN. In the representation method of representing the availability of VNFs by a matrix, only the elements of the main diagonal are not zero, and the availability of N micro-services is represented by a matrix of N, for example, the availability of the original VNF containing three micro-services may be represented by a 3*3 matrix.

Also, each micro-service may contain one or more sub-functions. For example, the micro-service Si may contain sub-functions Fi1, fi2, …, fiM, and so on. Further, the availability a (Si) of the micro service Si may be further expressed as a product of the availability a (Fi 1), a (Fi 2), …, a (FiM) of the respective sub-functions Fi1, fi2, …, fiM. That is, a (Si) =a (Fi 1) a (Fi 2) … a (FiM).

For the example specifically illustrated above, the original VNF contains micro service 1 and micro service 2. Micro-service 1 comprises sub-function 1 and sub-function 2; the micro service 2 comprises a sub-function 3.

For the example specifically illustrated above, the availability of the original VNF is expressed as follows:

also, since the micro service 1 includes the sub-function 1 and the sub-function 2, the availability of the micro service 1 may be further expressed as a (S1) =a (F1) a (F2) by the availability of the sub-function 1, where a (F1) represents the availability of the sub-function 1, a (F2) represents the availability of the sub-function 2, and a (F1) a (F2) means a (F1) ×a (F2). Similarly, a (S2) =a (F3), where a (F3) represents the availability of subfunction 3.

The present invention equivalently replaces the original VNF with a diverse sub-instance set { SubVNF1, subVNF2, … …, subVNFN } comprising N sub-instances, called equivalent VNF, by defining a diversity factor N.

Each of the N sub-instances includes one or more sub-functions provided by the micro-service. Each micro-service is capable of defining one or more sub-functions.

The availability of each SubVNF may be expressed as:

wherein Xn, yn, zn respectively represent the number of different functional units, A1i, A2i, A3i respectively represent the availability of each functional unit, and pi represents the operation of the continuous multiplication.

The availability matrix of the equivalent VNF is denoted as matrix K, which can be expressed as:

wherein "E" in the above formula represents an identity matrix, A (subVNF _n ) Representing sub-instance subVNF _n E-a (SubVNF) _n ) Representing sub-instance subVNF _n Is not available.

Replacing the original VNF with the sub-instance set increases the consumption of resources, such as load balancing of traffic, and therefore, according to the diversity factor N, the resources required to diversify the sub-instance set are expressed as:

when n=1, R (N) is the resource required by the original VNF.

Depending on the role of the different micro services in the traffic, micro service gain coefficients α, β, …, etc. are defined. Taking the specific example described above involving two micro-services as an example, the gain of the equivalent VNF with respect to the diversity factor can be expressed as:

wherein R1 and R2 respectively represent resources required by two micro services, K (1, 1) represents elements of the 1 st row and 1 st column of the matrix K, and K (2, 2) represents elements of the 2 nd row and 2 nd column of the matrix K. If the embodiment involves 3 micro services, the above expression is a similar 3-term addition; if the embodiment involves 4 micro services, the above expression is a similar 4-term addition; and so on.

Then, as shown in step S103, under constraint conditions, the diversity factor N is solved to maximize the equivalent VNF gain, and the maximum granularity of the VNF sub-cases is obtained.

Specifically, the invention combines a mixed integer linear programming algorithm to construct an analytical model.

Based on the availability analysis and the diversity model, the problem of solving the diversity coefficient is established as a mixed integer linear programming problem.

Specifically, the optimization objective is as follows: in case the constraint is met, the equivalent VNF gain is maximized, which according to the diversity model can be expressed as:

and, the constraint conditions are as follows:

(1) Ensuring that resources used by the VNF instance do not exceed total available resources;

(2) Ensuring that each VNF sub-instance has certain availability, and the difference of the availability is within a certain range;

(3) Ensuring that the resources allocated to the set of VNF sub-instances can meet the expected performance (consistent with the original VNF instance);

(4) Ensuring the positioning and anti-affinity deployment of the VNF sub-embodiments;

(5) Ensuring that each VNF sub-instance can only be deployed on one physical node;

(6) Ensuring VNF sub-instance set load sharing.

By the above-described processing, the following results can be obtained: the optimal solution N meeting the constraint condition can be obtained through a mixed integer linear programming algorithm; under the condition that the diversity factor is N, according to the availability requirement of each VNF sub-instance in the constraint condition, the maximum granularity of the VNF sub-instance can be obtained, namely the deployment scale of any VNF sub-instance cannot exceed the granularity.

Then, as shown in step S104, the resource requirements of the single backup instance are set according to the maximum granularity of the VNF sub-instances, and one or more backup instances depending on the service requirements are set. Fig. 2 of the specification specifically illustrates an example of maximum granularity of VNF sub-instances and a backup instance with maximum granularity.

Finally, as shown in step S105, N sub-instances in the equivalent VNF are deployed on different physical nodes by distributed deployment.

As exemplified by the above-described embodiments of the present invention, the present invention has the following advantages and effects over the prior art.

First, a diversified deployment of VNFs is achieved. According to the VNF diversification sub-instance method, the VNF can be integrated into zero on the basis of meeting the original service requirement, distributed parallel deployment is carried out with smaller granularity, and weak correlation among each sub-instance is achieved, so that complete interruption of service is not caused when part of sub-instances fail, and continuity and stability of the service are guaranteed to a greater extent.

Secondly, the invention realizes customized redundant backup. In the method for customizing redundant backup, provided by the invention, the resource requirement is adjusted according to the granularity of the sub-instance, the inherent resource requirement of the backup is reduced, the efficient utilization of the backup resource is ensured, and meanwhile, the redundant backup of the unused level can be customized according to the unused service requirement, so that the backup resource is flexibly allocated.

An exemplary further embodiment of the present invention, namely the diversified deployment of the 5G core network function AUSF, is described in detail below with reference to fig. 3 of the specification.

For the network function AUSF, as shown in fig. 3, the network function AUSF consists of 2 micro services, which are authentication and roaming information protection, respectively, wherein the authentication function can provide authentication for access in a 3GPP mode and authentication for access in a non-3 GPP mode. According to the resource and the loaded service required by each micro service, the proportion relation between the resource and the service can be obtained, according to the proportion relation, the micro service is subjected to resource refinement and can be split into a plurality of independent units, and each unit can finish the same service volume of the micro service. The equivalent diversity VNF sub-instance set { sub ausf1, sub ausf2, … … } can be obtained by combining different micro-service sub-units. As shown in the diversified sub-examples in fig. 3, the diversified sub-examples have the following functions {3GPP authentication+roaming information protection, non-3 GPP authentication+roaming information protection, 3GPP authentication+non-3 gpp+roaming information protection, 3GPP authentication+non-3 GPP authentication }, and traffic of different services may be directed to corresponding sub-examples for processing. The sum of the service capabilities of the sub-instances is sufficient to match the original AUSF. Ensuring that all scheduled traffic is not affected.

Each sub-instance is deployed on different physical nodes through distributed deployment, so that the correlation between each sub-instance is reduced. The sub-instances run in parallel, so that when a certain sub-instance fails, the service operation of other sub-instances cannot be influenced, the fault range is reduced, the influence of the fault on the service is reduced, and the availability is improved. As shown in fig. 3, it is assumed that when the physical node where the sub ausf1 is located fails, the sub ausf1 cannot continue to provide services for the current service, and needs to be guided to the backup instance processing. The method has no influence on other sub AUSF, and can ensure the service stability of other sub AUSF.

The original AUSF is equivalently replaced by a sub-instance set with smaller granularity, redundancy can be correspondingly contracted, all resources required by the original AUSF are not required to be covered, and a single redundant instance is enough to cover the sub-instance with the maximum granularity. According to the actual service demand, the number of backup instances can be flexibly selected, and the utilization rate of backup resources is effectively improved.

For the present embodiment, the diversity demand analysis based on availability can be performed as follows.

First, the original AUSF availability is defined as:

wherein A1, A2 represent the availability of 3GPP authentication and non-3 GPP authentication, respectively, and A3 represents the availability of roaming information protection.

Then, a diversity factor N is defined, i.e., the original AUSF is equivalently replaced with a diversity sub-instance set { sub AUSF1, sub AUSF2, … …, sub AUSF } containing N sub-instances, referred to as equivalent AUSF. The availability of each sub ausf may be expressed as:

wherein Xn, yn, zn respectively represent the number of different functional units, A1i, A2i, A3i respectively represent the availability of each functional unit.

Also, the availability matrix of equivalent AUSF is denoted as matrix K, which may be expressed as:

in the case of replacing the original AUSF with the sub-instance set, in order to ensure a perfect match with the original AUSF function, additional resource consumption is required, such as load balancing of the service. The resources required to diversify the sub-instance set are expressed as:

when n=1, R (N) is the resource required by the original AUSF.

For example, according to the roles in the service, the gain coefficients of the authentication and roaming information protection are defined as α and β, respectively, and the gain of the equivalent AUSF with respect to the diversity coefficient is expressed as:

r1 and R2 represent resources required for the authentication function and the roaming information protection function, respectively.

Then, a mixed integer linear programming algorithm is combined to construct an analytical model.

and, the constraint conditions are as follows:

(2) Ensuring that each AUSF sub-instance has a certain availability, and the difference between the availability is within a certain range;

(3) Ensuring that the resources allocated to the set of AUSF sub-instances can meet the expected performance (consistent with the original AUSF instance);

(4) Ensuring the AUSF sub-instance to be placed in different places, and performing anti-affinity deployment;

(5) Ensuring that each AUSF sub-instance can only be deployed on one physical node;

(6) Ensuring load sharing of AUSF sub-instance sets.

By the above-described processing, the following results can be obtained: the optimal solution N meeting the constraint condition can be obtained through a mixed integer linear programming algorithm. Under the condition that the diversity factor is N, according to the availability requirement of each AUSF sub-instance in the constraint condition, the maximum granularity of the AUSF sub-instance can be obtained, namely the deployment scale of any AUSF sub-instance cannot exceed the granularity.

For this particular embodiment, the deployment results are as follows.

In this embodiment, a total of 12 physical nodes are available.

(1) According to the ratio of service to resource, the resource needed by the authentication function can be divided into 8 units, and the resource needed by the roaming information protection function can be divided into 4 units.

(2) According to the demand model and the analytical model, n=5 when the equivalent AUSF gain is maximum.

(3) The maximum granularity of the AUSF sub-instance is 'authentication unit x 2 and roaming information protection unit x 1' according to the constraint condition.

(4) According to the parameters, combining with service requirements, replacing the original AUSF by sub-instance sets equivalently, and deploying the sub-instance sets according to the following modes:

{ subauxf1=3 GPP authentication 1+roaming information protection 1,

sub ausf2=non-3 GPP authentication 1+ roaming information protection 1,

subauxf3=3 GPP authentication x 2+ roaming information protection x 1,

subauxf4=3 GPP authentication 1+non-3 GPP 1+roaming information protection 1,

SubAUSF5 = 3GPP authentication 1+ non-3 GPP authentication 1}

(5) Since the maximum granularity of the AUSF sub-instance is "authentication unit x 2", roaming information protection unit x 1", the granularity of a single backup AUSF is also" authentication unit x 2 ", and roaming information protection unit x 1" can cover any AUSF sub-instance. And deploying 2-3 backup AUSF instances according to service requirements.

The invention provides a network function high availability deployment method with function diversity, which can reduce the fault influence range by using a low-coupling diversified sub-instance set to replace the original network function, thereby avoiding complete service interruption and improving the reliability and availability of the network function. Because each sub-instance is deployed on different physical nodes through distributed deployment, the correlation between each sub-instance is reduced. The sub-instances run in parallel, so that when a certain sub-instance fails, the service operation of other sub-instances cannot be influenced, the fault range is reduced, the influence of the fault on the service is reduced, and the availability of the VNF is improved.

In addition, the invention provides a customized redundant backup scheme which can change the inherent resource requirement of the original network function backup, customize the set backup and ensure the efficient utilization of the backup resources. Because the original VNF is equivalently replaced by a sub-instance set with smaller granularity, redundancy can also be correspondingly scaled, all resources required by the original VNF are not required to be covered, and a single redundancy instance is enough to cover the sub-instance with the largest granularity. According to the actual service demand, the number of backup instances can be flexibly selected, and the utilization rate of backup resources is effectively improved.

Fig. 4 illustrates an exemplary configuration of a computing device 400 capable of implementing embodiments in accordance with the present disclosure.

Computing device 400 is an example of a hardware device that can employ the above aspects of the present disclosure. Computing device 400 may be any machine configured to perform processing and/or computing. Computing device 400 may be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a Personal Data Assistant (PDA), a smart phone, an in-vehicle computer, or a combination thereof.

As shown in fig. 4, computing device 400 may include one or more elements that may be connected to or in communication with bus 402 via one or more interfaces. Bus 402 can include, but is not limited to, an industry standard architecture (Industry Standard Architecture, ISA) bus, a micro channel architecture (Micro Channel Architecture, MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus. Computing device 400 may include, for example, one or more processors 404, one or more input devices 406, and one or more output devices 408. The one or more processors 404 may be any kind of processor and may include, but is not limited to, one or more general purpose processors or special purpose processors (such as special purpose processing chips). The processor 404 may be configured to perform the methods shown in fig. 2 or 3, for example. Input device 406 may be any type of input device capable of inputting information to a computing device and may include, but is not limited to, a mouse, keyboard, touch screen, microphone, and/or remote controller. Output device 408 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers.

Computing device 400 may also include or be connected to a non-transitory storage device 414, which non-transitory storage device 414 may be any storage device that is non-transitory and that may enable data storage, and may include, but is not limited to, disk drives, optical storage devices, solid state memory, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic medium, compact disk or any other optical medium, cache memory and/or any other memory chip or module, and/or any other medium from which a computer may read data, instructions, and/or code. Computing device 400 may also include Random Access Memory (RAM) 410 and Read Only Memory (ROM) 412. The ROM 412 may store programs, utilities or processes to be executed in a non-volatile manner. RAM 410 may provide volatile data storage and store instructions related to the operation of computing device 400. Computing device 400 may also include a network/bus interface 416 coupled to a data link 418. The network/bus interface 416 may be any kind of device or system capable of enabling communication with external equipment and/or a network and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication devices, and/or chipsets (such as bluetooth) ^TM Devices, 802.11 devices, wiFi devices, wiMax devices, cellular communication facilities, etc.).

The present disclosure may be implemented as any combination of apparatuses, systems, integrated circuits, and computer programs on a non-transitory computer readable medium. One or more processors may be implemented as an Integrated Circuit (IC), application Specific Integrated Circuit (ASIC), or large scale integrated circuit (LSI), system LSI, super LSI, or ultra LSI assembly that performs some or all of the functions described in this disclosure.

The present disclosure includes the use of software, applications, computer programs, or algorithms. The software, application, computer program or algorithm may be stored on a non-transitory computer readable medium to cause a computer, such as one or more processors, to perform the steps described above and depicted in the drawings. For example, one or more memories may store software or algorithms in executable instructions and one or more processors may associate a set of instructions to execute the software or algorithms to provide various functions in accordance with the embodiments described in this disclosure.

The software and computer programs (which may also be referred to as programs, software applications, components, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural, object-oriented, functional, logical, or assembly or machine language. The term "computer-readable medium" refers to any computer program product, apparatus or device, such as magnetic disks, optical disks, solid state memory devices, memory, and Programmable Logic Devices (PLDs), for providing machine instructions or data to a programmable data processor, including computer-readable media that receives machine instructions as a computer-readable signal.

By way of example, computer-readable media can comprise Dynamic Random Access Memory (DRAM), random Access Memory (RAM), read Only Memory (ROM), electrically erasable read only memory (EEPROM), compact disk read only memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired computer-readable program code in the form of instructions or data structures and that can be accessed by a general purpose or special purpose computer or general purpose or special purpose processor. Disk or disc, as used herein, includes Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media.

The subject matter of the present disclosure is provided as examples of apparatuses, systems, methods, and programs for performing the features described in the present disclosure. However, other features or variations are contemplated in addition to the features described above. It is contemplated that the implementation of the components and functions of the present disclosure may be accomplished with any emerging technology that may replace any of the above-described implementation technologies.

In addition, the foregoing description provides examples without limiting the scope, applicability, or configuration set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the spirit and scope of the disclosure. Various embodiments may omit, replace, or add various procedures or components as appropriate. For example, features described with respect to certain embodiments may be combined in other embodiments.

Similarly, although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous.

Claims

1. A high availability deployment method of virtual network function facing to multiple services includes the steps:

solving a diversity factor N under constraint conditions to maximize an equivalent VNF gain and obtain a maximum granularity of a VNF sub-instance, the constraint conditions comprising:

ensuring that the resources used by the VNF instance do not exceed the total available resources,

ensuring availability of each VNF sub-instance and a certain range of differences in availability,

ensuring that the resources allocated to the subset of VNF instances meet the expected performance, which is consistent with the performance of the original VNF instance,

ensuring the positioning and anti-affinity deployment of the VNF sub-entities,

ensuring that each VNF sub-instance can only be deployed on one physical node, an

Ensuring load sharing of the VNF sub-instance set;

2. The method of claim 1, wherein each micro-service is capable of defining one or more sub-functions.

3. The method of claim 1, wherein the diversified coefficients N are solved by establishing a problem of solving the diversified coefficients N as a mixed integer linear programming problem.

4. A method according to claim 3, wherein the diversification factor N is solved under constraints.

5. The method of claim 1, wherein the backup instance does not cover all resources required by the original VNF.

6. A multi-service oriented virtual network function high availability deployment apparatus comprising:

a memory having instructions stored thereon; and

a processor configured to execute instructions stored on the memory to perform the method of any one of claims 1 to 5.

7. A computer-readable storage medium comprising computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any of claims 1-5.

8. A computer program product comprising computer-executable instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any of claims 1 to 5.