CN116232921B - Deterministic network data set construction device and method based on hypergraph - Google Patents
Deterministic network data set construction device and method based on hypergraph Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/142—Network analysis or design using statistical or mathematical methods
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/147—Network analysis or design for predicting network behaviour
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
- H04L41/5061—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the interaction between service providers and their network customers, e.g. customer relationship management
- H04L41/5064—Customer relationship management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention relates to the technical field of computers, in particular to a device and a method for constructing a deterministic network data set based on hypergraph, wherein the device comprises a business willingness acquisition module, an infrastructure characteristic expression module and a modal alignment module, wherein the device comprises the following components: the service willingness acquisition module mainly completes capturing, willingness verification and willingness negotiation of the service willingness and decomposes the service willingness into a suitable scene, a main function and main performance; the infrastructure characteristic expression module mainly completes abstraction and virtualization of the equipment and comprises modes which can be displayed by the equipment, virtualized network functions and network resources which are required to be consumed by each virtualized network function; the modality alignment module is a corresponding association between a known traffic and a network slice. By introducing the hypergraph, the deterministic network business willingness, the infrastructure and the data set of modal alignment between the deterministic network business willingness and the infrastructure are established, the understanding capability of the business willingness is improved, and further, the business requirement is conveniently and rapidly provided for customers.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a device and a method for constructing a deterministic network data set based on hypergraph.
Background
Research on deterministic network architecture embodying business will has become a leading edge hotspot. ONF sets the standard draft of the intended network architecture. Gartner issues capability reports required for a willingness-based network system. Cisco also published ESG: advancing white books to willingness-based networks. However, the idea that the business will and the network slice belong to different layers is that the association mechanism and the influence mechanism of the business will and the network slice are not effectively revealed. The premise of researching the association mechanism of the business will and the network slice is to establish a data set of deterministic network business will, infrastructure and modal alignment between the deterministic network business will and the infrastructure.
Existing willingness acquisition methods all assume that there is a service profile (request graph, summary or virtualized network function) of deterministic network slicing, and the profile needs to include the service graph and additional service attributes, such as the edge constraints (link bandwidth, packet loss, etc.) of each middleware and each node link, such as firewall and load balancer. However, business applications, network operations, and infrastructure have evolved independently. The application is very different from the beginning to the end, the opening time of the emerging business is from the past year to the present day or hour or even minute, the business has multiple scenes, the same business has completely different characteristics in different scenes, and the business only knows the qualitative requirement of the application. These factors make business willingness difficult to understand. The slice provided by the on-demand network only comprises related network functions, attributes carried by each network function, network resources consumed by each attribute, dependency relations among each function, scenes served by the slice and the like; the infrastructure has multi-modal, and the same facility can be assembled into different virtualized network functions after virtualization. This results in a business intent that cannot be directly matched to the network slice. Meanwhile, the existing data set is mainly the description data aiming at a certain aspect of network performance, such as an ali cloud security malicious program detection, a task dialogue based on subslots, automatic outdoor position sensing correction of ALWAES, CIS fraud detection of IEEE, capturing and visualizing time sequence change of LAN network when connecting to Internet, and the like, and lacks a database for describing business will.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a device and a method for constructing a deterministic network data set based on hypergraph.
The technical scheme adopted for solving the technical problems is as follows: the device for constructing the deterministic network data set based on the hypergraph comprises a business willingness acquisition module, an infrastructure characteristic expression module and a modality alignment module, wherein:
the service willingness acquisition module mainly completes capturing, willingness verification and willingness negotiation of the service willingness and decomposes the service willingness into a suitable scene, a main function and main performance;
the infrastructure characteristic expression module mainly completes abstraction and virtualization of the equipment and comprises modes which can be displayed by the equipment, virtualized network functions and network resources which are required to be consumed by each virtualized network function;
the modality alignment module is a corresponding association between a known traffic and a network slice.
The method for constructing the deterministic network data set based on the hypergraph is characterized by comprising the following steps of:
step S1: analyzing the scene, the function and the performance of the service through a service willingness acquisition module to obtain a service hypergraph;
step S2: analyzing network equipment, network functions and network resources required to be consumed by each function of the network infrastructure through an infrastructure characteristic expression module to obtain a network slicing mode hypergraph;
step S3: aligning the service hypergraph with the network slice mode hypergraph through a mode alignment module;
step S4: a hypergraph-based deterministic network dataset is constructed.
Specifically, the specific flow of step S4 is as follows:
step S41: decomposing service characteristics;
step S42: decomposing network slice characteristics;
step S43: rule correspondence analysis;
step S44: establishing a data set, preprocessing and standardizing corresponding data of business and network facilities, and then constructing and updating periodically on the basis, wherein:
preprocessing includes, but is not limited to, data alignment, summarization, de-redundancy, complementation, and error correction;
normalization refers to the unified representation of the names of the same business or slice in different databases and documents.
Specifically, the step S41 is specifically a service characteristic decomposition: let θ= { θ 1 ,θ 2 … } represents a business scenario, α= { α 1 ,α 2 … } represents traffic, τ= { τ 1 ,τ 2 … } represents the function of the traffic, σ= { σ 1 ,σ 2 … indicates the performance that a certain function of the service needs to embody.
Specifically, the step S42 is specifically a network slice characteristic decomposition: let θ= { θ 1 ,θ 2 … represents a certain type of device of the network, β= { β 1 ,β 2 … } represents a device, γ= { γ 1 ,γ 2 … } represents the network function of the device, μ= { μ 1 ,μ 2 … represents the network resources that the device needs to consume for a certain network function.
Specifically, the step S43 is specifically a rule correspondence analysis:
the willingness of the scene thetai service alpha j is denoted as x, and the probability that x belongs to the scene thetai can be expressed as:
f(p 1 (x,θ 1 )p 1 (θ 1 )+p 2 (x,θ 2 )p 2 (θ 2 )+…)＝∑p(x,θ)＝p(f) (1)
wherein p is 1 (θ 1 ) Represented in scene theta 1 Probability of existence, p 1 (x,θ 1 )p 1 (θ 1 ) Representing willingness x in scene θ 1 Probability of existence;
in a heterogeneous network, a batch of devices related to willingness x need to be selected from different types of devices to cooperate to complete tasks, and then the probability of selecting the devices capable of completing the willingness x in the network facility can be expressed as:
g(p A (x,θ 1 )p B (x,θ 1 )p A (x,θ 2 )p B (x,θ 2 )…)＝∏p(x,θ)＝p(g) (2)
from equations (1) and (2), the application correlation model p (f, g) of the willingness of the service and the network slice characteristics is expressed, the function of the service is expressed by a service function chain of the network slice, and the performance of the service is realized by the type of resources and the quantity of resources required to be consumed by each function of the service function chain of the network slice. And establishing a prior probability model between the service and the network slice inside the service and the network slice by adopting a Bayesian model.
Specifically, a certain service theta of a certain scene is calculated based on a clustering algorithm i .α j Is a desire of (1).
Specifically, a Bayesian model is adopted to build a prior probability model between the service and the network slice inside the service and the network slice.
The invention has the beneficial effects that:
1. representing a multi-element relationship of a multi-element group containing a plurality of entities by using a superside; the integrity of the multi-element relation with different dimensions can be fully considered, and the learning and the prediction of the target can be completed from the combination of different granularities;
2. the service characteristics of different scenes and the network slice characteristics of different modes can be described through the superside, and the importance degree of each characteristic can be described through the attention mechanism;
3. by introducing the hypergraph, the deterministic network business willingness, the infrastructure and the data set of modal alignment between the deterministic network business willingness and the infrastructure are established, the understanding capability of the business willingness is improved, and further, the business requirement is conveniently and rapidly provided for customers.
Detailed Description
The invention is further described in connection with the following detailed description in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
The invention discloses a hypergraph-based deterministic network data set construction device which comprises a business willingness acquisition module, an infrastructure characteristic expression module and a modality alignment module. The business willingness acquisition module mainly completes capturing, willingness verification and willingness negotiation of business willingness and decomposes the business willingness into suitable scenes, main functions and main performances. The infrastructure characteristic expression module mainly completes abstraction and virtualization of the equipment and comprises modes which can be displayed by the equipment, virtualized network functions and network resources which are required to be consumed by each virtualized network function. The modality alignment module is then a corresponding association between the known traffic and the network slice.
A deterministic network data set construction method based on hypergraph, through business willingness acquisition module to scene, function and performance of business analyze, get the hypergraph of business; analyzing network equipment, network functions and network resources required to be consumed by each function of the network infrastructure through the infrastructure characteristic expression module to obtain a modal hypergraph of the network slice; finally, aligning the service hypergraph with the network slice mode hypergraph through a mode alignment module; thereby constructing a deterministic network data set based on hypergraph, comprising the following specific steps:
s1: service characteristic decomposition: let θ= { θ 1 ,θ 2 … } represents a business scenario, α= { α 1 ,α 2 … } represents traffic, τ= { τ 1 ,τ 2 … } represents the function of the traffic, σ= { σ 1 ,σ 2 … indicates the performance that a certain function of the service needs to embody. Based on clustering algorithm, a certain service theta of a certain scene can be calculated i .α j Is a desire of (1).
S2: network slice characteristic decomposition: let θ= { θ 1 ,θ 2 … represents a certain type of device of the network, β= { β 1 ,β 2 … } represents a device, γ= { γ 1 ,γ 2 … } represents the network function of the device, μ= { μ 1 ,μ 2 … represents the network resources that the device needs to consume for a certain network function. When the equipment leaves the factory, the network functions, the number of network resources required to be consumed by each function and the total number of resources of the equipment are fixed, and only the physical attribute and the logical attribute of the equipment are required to be marked when the network slice characteristic is decomposed.
S3: rule correspondence analysis: scene θ i Service alpha j Expressed as x, the probability that x belongs to the scene θ can be expressed as:
f(p 1 (x,θ 1 )p 1 (θ 1 )+p 2 (x,θ 2 )p 2 (θ 2 )+…)＝∑p(x,θ)＝p(f) (1)
wherein p is 1 (θ 1 ) Represented in scene theta 1 Probability of existence, p 1 (x,θ 1 )p 1 (θ 1 ) Representing willingness x in scene θ 1 Probability of existence. In a heterogeneous network, a batch of devices related to willingness x need to be selected from different types of devices to cooperate to complete tasks, and then the probability of selecting the devices capable of completing the willingness x in the network facility can be expressed as:
g(p A (x,θ 1 )p B (x,θ 1 )p A (x,θ 2 )p B (x,θ 2 )…)＝Πp(x,θ)＝p(g) (2)
from equations (1) and (2), the correspondence between the willingness of the service and the network slice characteristics can be represented by a correlation model p (f, g), the functions of the service can be represented by a service function chain of the network slice, and the performance of the service can be realized by the type of resources and the quantity of resources required to be consumed by each function of the service function chain of the network slice. Based on the above analysis, a bayesian model may be employed to build a prior probability model between traffic and network slices inside the traffic and network slices.
S4: establishing a data set: the corresponding data of the business and the network facilities are preprocessed (including data comparison, summarization, redundancy removal, completion, error correction and the like) and standardized (including unified representation of the same business or slice names in different databases and documents and the like), and then are constructed and updated periodically on the basis.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the foregoing examples, and that the foregoing description and description are merely illustrative of the principles of this invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. The method for constructing the deterministic network data set based on the hypergraph is characterized by comprising the following steps of:
step S1: analyzing the scene, the function and the performance of the service through a service willingness acquisition module to obtain a service hypergraph;
step S2: analyzing network equipment, network functions and network resources required to be consumed by each function of the network infrastructure through an infrastructure characteristic expression module to obtain a network slicing mode hypergraph;
step S3: aligning the service hypergraph with the network slice mode hypergraph through a mode alignment module;
step S4: constructing a deterministic network data set based on hypergraph;
the specific flow of step S4 is as follows:
step S41: decomposing service characteristics;
step S42: decomposing network slice characteristics;
step S43: rule correspondence analysis;
step S44: establishing a data set, preprocessing and standardizing corresponding data of business and network facilities, and then constructing and updating periodically on the basis, wherein:
preprocessing includes, but is not limited to, data alignment, summarization, de-redundancy, complementation, and error correction;
standardization refers to the unified representation of the names of the same kind of business or slices in different databases and documents;
the step S43 specifically includes rule correspondence analysis:
scene θ i Service alpha j Expressed as x, the probability that x belongs to the scene θ can be expressed as:
f(p 1 (x,θ 1 )p 1 (θ 1 )+p 2 (x,θ 2 )p 2 (θ 2 )+…)＝∑p(x,θ)＝p(f) (1)
wherein p is 1 (θ 1 ) Represented in scene theta 1 Probability of existence, p 1 (x,θ 1 )p 1 (θ 1 ) Representing willingness x in scene θ 1 Probability of existence;
in a heterogeneous network, a batch of devices related to willingness x need to be selected from different types of devices to cooperate to complete tasks, and then the probability of selecting the devices capable of completing the willingness x in the network facility can be expressed as:
g(p A (x,θ 1 )p B (x,θ 1 )…p A (x,θ 2 )p B (x,θ 2 ))＝Πp(x,θ)＝p(g) (2)
from equations (1) and (2), it can be known that the willingness of the service and the application association model p (f, g) of the network slice characteristics are expressed, the function of the service is expressed by a service function chain of the network slice, the performance of the service is implemented by the type of resources and the number of resources required to be consumed by each function of the service function chain of the network slice, and a Bayesian model is used to build a prior probability model between the service and the network slice inside the service and the network slice.
2. The hypergraph-based deterministic network dataset construction method as set forth in claim 1, wherein: the step S41 specifically includes decomposing service characteristics: let θ= { θ 1 ,θ 2 … } represents a business scenario, α= { α 1 ,α 2 … } represents traffic, τ= { τ 1 ,τ 2 … } represents the function of the traffic, σ= { σ 1 ,σ 2 … indicates the performance that a certain function of the service needs to embody.
3. The hypergraph-based deterministic network dataset construction method as set forth in claim 1, wherein: the step S42 specifically includes decomposing the network slice characteristics: let θ= { θ 1 ,θ 2 … represents a certain type of device of the network, β= { β 1 ,β 2 … } represents a device, γ= { γ 1 ,γ 2 ,…The network function of the device is denoted μ= { μ } 1 ,μ 2 … represents the network resources that the device needs to consume for a certain network function.
4. The hypergraph-based deterministic network dataset construction method as set forth in claim 1, wherein: specifically, calculating a certain service theta of a certain scene based on a clustering algorithm i .α j Is a desire of (1).
5. The hypergraph-based deterministic network dataset construction method as set forth in claim 1, wherein: and establishing a prior probability model between the service and the network slice inside the service and the network slice by adopting a Bayesian model.
6. An apparatus for a hypergraph-based deterministic network dataset construction method as recited in any of claims 1-5, wherein: the system comprises a business willingness acquisition module, an infrastructure characteristic expression module and a modality alignment module, wherein:
the service willingness acquisition module mainly completes capturing, willingness verification and willingness negotiation of the service willingness and decomposes the service willingness into a suitable scene, a main function and main performance;
the infrastructure characteristic expression module mainly completes abstraction and virtualization of the equipment and comprises modes which can be displayed by the equipment, virtualized network functions and network resources which are required to be consumed by each virtualized network function;
the modality alignment module is a corresponding association between a known traffic and a network slice.
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