CN110166304A

CN110166304A - A kind of method of combination, device, electronic equipment and the storage medium of cross-domain SFC

Info

Publication number: CN110166304A
Application number: CN201910472411.3A
Authority: CN
Inventors: 王颖; 钟旭霞; 邱雪松; 芮兰兰; 樊娟
Original assignee: Beijing University of Posts and Telecommunications
Current assignee: Beijing University of Posts and Telecommunications
Priority date: 2019-05-31
Filing date: 2019-05-31
Publication date: 2019-08-23

Abstract

The embodiment of the invention provides the method for combination of cross-domain SFC a kind of, device, electronic equipment and storage medium, the described method includes: for a plurality of to layout SFC and multiple data center's nodes, generated VNF expense and bandwidth cost when being deployed in different data central node based on heterogeneous networks functional node, construct probability set, and construct transition probability matrix and output probability matrix, then, based on constructed probability set, transition probability matrix, and output probability matrix, construct hidden Markov model, utilize the probability in hidden Markov model, transition probability and output probability, calculate the hidden state probability that network function node deployment in SFC is corresponding in data center's node, and layout is carried out to each SFC in a plurality of SFC based on hidden state probability, obtain each S The corresponding hidden state subsequence of FC.The embodiment of the present invention can reduce the cross-domain bandwidth cost of SFC after layout.

Description

Cross-domain SFC arranging method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for arranging cross-domain SFC, an electronic device, and a storage medium.

Background

With the rapid development of communication technology, the network scale is continuously enlarged and the service is continuously expanded, in order to complete the corresponding service in the network, the flow in the network needs to be processed in sequence according to the network functions conforming to a specific sequence, and SFC (service function Chains) defines the sequence of processing the network functions. In practical application, a Network operator needs to direct traffic to a proprietary hardware Network function to complete a service corresponding to the SFC, but the Network function of a traditional proprietary device has obvious limitations in terms of Network flexibility, extensibility, manageability and operation efficiency, so that NFV (Network Functions Virtualization) is brought into operation. NFV is a new Network architecture, which decouples Network functions from proprietary hardware devices, running on generic devices in the form of VNFs (virtual Network functions).

With the widespread use of cloud computing and big data, multiple IDCNs (Inter-dcnetworks) distributed in different geographical locations have been widely deployed. By deploying the SFC based on the NFV in the multi-IDCN, a user can flexibly use computing resources, network resources, storage resources and the like of a data center, and economical and rapid user service deployment is realized. While deploying SFCs in multiple IDCNs has great advantages, orchestrating SFCs across data centers is a challenge for current SFC orchestration.

The current method for SFC orchestration is: aiming at all SFCs, traversing and integrating network function types of a plurality of SFCs to be arranged one by one based on the correlation of VNF types in the SFCs until all the SFCs to be arranged are integrated into one or more service function diagrams, wherein the service function diagrams comprise network function sequences requested by all the SFCs to be arranged, then selecting the service function diagram with the largest scale, carrying out topological sequencing on the service function diagrams, further selecting the network function sequence with the lowest bandwidth demand, and using a shortest path algorithm to sequentially deploy the network functions of the SFCs to be arranged into a bottom layer network.

However, in practical applications, SFCs typically need to be deployed on data centers distributed in different geographic locations to meet performance requirements or location constraints of the SFCs, e.g., "agent functions" and "caching functions" should be deployed on data centers near the enterprise network, "packet filters" should be deployed on data centers near the source of traffic, etc. Existing methods for SFC orchestration integrate SFCs to be orchestrated into one or more service function graphs, typically based on the correlation of VNF types in the SFCs, to implement the orchestration of the SFCs. However, when the number of SFCs to be arranged is large, in the integrated service function graph, a situation that a previous function node in the SFC to be arranged is not adjacent to a subsequent function node may occur, that is, a cross-domain situation may occur between the previous function node and the subsequent function node, which increases the cross-domain bandwidth overhead between the previous function node and the subsequent function node, and increases the cross-domain bandwidth overhead of the SFC after arrangement.

Disclosure of Invention

The embodiment of the invention aims to provide a cross-domain SFC arranging method, a cross-domain SFC arranging device, electronic equipment and a storage medium, so as to reduce cross-domain bandwidth overhead of the arranged SFC. The specific technical scheme is as follows:

in a first aspect, an embodiment of the present invention provides a method for arranging cross-domain SFCs, where the method includes:

acquiring a plurality of Service Function Chains (SFCs) to be arranged and a plurality of data center nodes, wherein each SFC comprises a plurality of network function nodes, each data center node is used for deploying the network function nodes in the SFC, and one network function node is deployed in one data center node;

determining initial probabilities of deploying different network function nodes in different data center nodes based on Virtual Network Function (VNF) overheads and bandwidth overheads generated when the different network function nodes are deployed in different data center nodes, and constructing an initial probability set based on the determined initial probabilities;

constructing a transition probability matrix based on the transition probability determined by the transition of the first state to the second state; the first state corresponds to a process of deploying a first network function node in the SFC in a first data center node, the second state corresponds to a process of deploying a second network function node in the SFC in a second data center node, and the second network function node is a neighboring node of the first network function node;

constructing an output probability matrix based on the output probability of the function type corresponding to the third network function node in the SFC under the third state; the third state is a state corresponding to a process of deploying a third network function node in the SFC in a third data center node;

constructing a hidden Markov model based on the constructed initial probability set, the transition probability matrix, and the output probability matrix;

calculating the hidden state probability corresponding to the network function nodes in the SFCs deployed in the data center nodes by using the initial probability, the transition probability and the output probability in the hidden Markov model, and arranging each SFC in the plurality of SFCs based on the hidden state probability to obtain hidden state subsequences corresponding to each SFC and obtain a plurality of hidden state subsequences corresponding to the plurality of SFCs; the hidden state subsequence comprises the following elements: and the data center node is deployed on the network function node in the SFC.

Optionally, determining an initial probability of deploying the different network function nodes in the different data center nodes by using a first preset expression;

determining the transition probability of the first state to the second state by using a second preset expression;

determining the output probability of the function type corresponding to the third network function node in the output SFC under the third state by using a third preset expression;

the first preset expression is as follows:

in the formula, pi_mRepresenting an initial probability of deploying the network function node to an mth data center node, M representing a number of data center nodes,representing in mth data centre nodeThe s-th instance of the VNF,represents the overhead incurred by deploying the first network function node to the s-th VNF instance in the m-th data center node,representing the bandwidth transfer overhead generated by deploying an initial network function node to an initial data center node, deploying a first network function node to an m-th data center node, 01 representing the state transfer from the initial network function node to the first network function node of the SFC, and σ m representing the transfer from the initial data center node to the m-th data center node;

the second preset expression is as follows:

in the formula, indicating a stateTransition to StateTransition probability, state ofRepresents the state corresponding to the process of deploying the (i-1) th network function node in the nth data center nodeMeans for deploying the ith network function node toThe state corresponding to the process in the mth data center node,represents the bandwidth overhead of the (i-1) th network function node deployed in the data center node n and the ith network function node deployed in the data center m, wherein (i-1) i represents the transfer from the (i-1) th network function node to the ith network function node, nm represents the transfer from the nth data center node to the mth data center node,representing the unit bandwidth cost from data center node N to data center node m, N representing the number of network topologies of data center nodes,representing the requested bandwidth amount between the (i-1) th network function node and the ith network function node of the p-th SFC;

the third preset expression is as follows:

in the formula,indicating a stateLower output network function typeProbability, state ofRepresents the state corresponding to the process of deploying the ith network function node in the mth data center node,indicates the function type of the i-th network function node of the p-th SFC,indicating a stateType of network function not outputThe probability of (a) of (b) being,represents the overhead incurred by deploying the ith network function node to the s-th VNF instance in the m-th data center node,indicating the reliability of the s-th VNF instance in the m-th data center node,indicating the function type of the s-th VNF instance in the m-th data center node.

Optionally, the plurality of SFCs to be orchestrated are a set of SFCs; calculating, by using the initial probability, the transition probability, and the output probability in the hidden markov model, a hidden state probability corresponding to a network function node in the SFC deployed in a data center node, and arranging each of the SFCs based on the hidden state probability to obtain a hidden state subsequence corresponding to each SFC, where the obtained multiple hidden state subsequences corresponding to the multiple SFCs are:

calculating the hidden state probability corresponding to the network function nodes in the SFCs deployed in the data center nodes by using the initial probability, the transition probability and the output probability in the hidden Markov model, and arranging each SFC in the plurality of SFCs based on the hidden state probability to obtain the hidden state subsequence corresponding to each SFC and obtain the hidden state sequence set corresponding to the SFC set.

Optionally, the step of calculating, by using the initial probability, the transition probability, and the output probability in the hidden markov model, a hidden state probability corresponding to a network function node in the SFC being deployed in a data center node, and arranging each of the SFCs based on the hidden state probability to obtain a hidden state subsequence corresponding to each SFC, so as to obtain a hidden state sequence set corresponding to the SFC set, includes:

judging whether the SFC set contains SFCs to be arranged or not;

if the SFC set comprises the SFCs to be programmed, selecting the longest SFC in the SFC set as the current SFC to be programmed;

calculating the hidden state probability corresponding to each network function node in the current layout SFC deployed in a data center node by using the initial probability, the transition probability and the output probability in the hidden Markov model, and arranging the current layout SFC based on the hidden state probability to obtain a hidden state subsequence corresponding to the current layout SFC;

and if the SFC set does not have the SFC to be arranged, outputting a hidden state sequence set corresponding to the SFC set.

Optionally, the step of calculating, by using the initial probability, the transition probability, and the output probability in the hidden markov model, a hidden state probability corresponding to deployment of each network function node in the currently deployed SFC into a data center node, and deploying the currently deployed SFC based on the hidden state probability to obtain a hidden state subsequence corresponding to the currently deployed SFC includes:

judging whether the current layout SFC is finished or not;

if the current layout SFC is not completely laid, judging whether the current layout network function node of the current layout SFC is the first network function node of the current layout SFC;

if the current arranging network function node of the current arranging SFC is the first network function node of the current arranging SFC, calculating the initial hidden state probability of arranging the current arranging network function node in each data center node based on the initial probability in the hidden Markov model;

if the current arranging network function node of the current arranging SFC is not the first network function node of the current arranging SFC, calculating the maximum hidden state probability of arranging the current arranging network function node in each data center node by using the transition probability and the output probability in the hidden Markov model, and recording the position of a preposed data center node corresponding to the maximum hidden state probability;

taking the next network function node of the current arranging SFC as the current arranging network function node of the current arranging SFC, and executing the step of judging whether the current arranging SFC is arranged;

if the current SFC is arranged completely, taking the last network function node of the current SFC as the current network function node, selecting a fourth data center node which corresponds to the maximum hidden state probability of the current network function node to deploy the current network function node, and storing the fourth data center node in a hidden state subsequence corresponding to the current SFC;

determining a pre-data center node which corresponds to the maximum hidden state probability of the current network function node as a fifth data center node corresponding to a network function node which is before the current network function node, deploying the network function node which is before the current network function node in the fifth data center node, storing the fifth data center node in a hidden state subsequence which corresponds to the current layout SFC, and taking the network function node which is before the current network function node as the current network function node;

judging whether a network function node before the current network function node is the first network function node of the current SFC;

if the previous network function node of the current network function node is not the first network function node of the current SFC, executing a step of determining a preposed data center node corresponding to the hidden state probability of the current network function node to be the maximum as a fifth data center node corresponding to the previous network function node of the current network function node;

deleting the current orchestrated SFC from the set of SFCs if a previous network function node to the current network function node is a first network function node of the current orchestrated SFC.

Optionally, the method further comprises:

and backing up the VNF based on the hidden state sequence set, the reliability value corresponding to each SFC after arrangement and the cost benefit value of the VNF corresponding to each network function node in the SFC corresponding to each hidden state subsequence in the hidden state sequence set.

Optionally, the step of backing up the VNF based on the hidden state sequence set, the reliability value corresponding to each SFC after being arranged, and the cost benefit value of the VNF corresponding to each network function node in the SFC corresponding to each hidden state subsequence in the hidden state sequence set includes:

traversing each hidden state subsequence in the hidden state sequence set, and taking the SFC corresponding to the hidden state subsequence as the current SFC;

calculating a reliability value of the current SFC;

judging whether the reliability value of the current SFC is smaller than a preset reliability value or not;

when the reliability value of the current SFC is smaller than a preset reliability value, placing the current SFC in a first set, and placing VNFs passing through the current SFC in a second set;

determining whether the first set is empty;

calculating a cost-benefit value for each VNF in the second set when the first set is not empty;

backing up the VNF corresponding to the maximum cost benefit value;

calculating the reliability value of each SFC in the first set after backup;

and if the reliability value of the SFC in the first set is not less than the preset reliability value after backup, deleting the SFC from the first set, and executing the step of judging whether the first set is empty or not.

In a second aspect, an embodiment of the present invention provides an apparatus for orchestrating cross-domain SFCs, where the apparatus includes:

the system comprises an acquisition module, a scheduling module and a scheduling module, wherein the acquisition module is used for acquiring a plurality of Service Function Chains (SFCs) to be scheduled and a plurality of data center nodes, each SFC comprises a plurality of network function nodes, each data center node is used for deploying the network function nodes in the SFC, and one network function node is deployed in one data center node;

the first construction module is used for determining initial probabilities of deploying different network function nodes in different data center nodes based on Virtual Network Function (VNF) overheads and bandwidth overheads generated when the different network function nodes are deployed in different data center nodes, and constructing an initial probability set based on the determined initial probabilities;

the second construction module is used for constructing a transition probability matrix based on the transition probability determined by the transition of the first state to the second state; the first state corresponds to a process of deploying a first network function node in the SFC in a first data center node, the second state corresponds to a process of deploying a second network function node in the SFC in a second data center node, and the second network function node is a neighboring node of the first network function node;

the third construction module is used for constructing an output probability matrix based on the output probability of the function type corresponding to the third network function node in the SFC under the third state; the third state is a state corresponding to a process of deploying a third network function node in the SFC in a third data center node;

a fourth construction module, configured to construct a hidden markov model based on the constructed initial probability set, the transition probability matrix, and the output probability matrix;

the arranging module is used for calculating the hidden state probability corresponding to the network function nodes in the SFCs deployed in the data center nodes by utilizing the initial probability, the transition probability and the output probability in the hidden Markov model, arranging each SFC in the plurality of SFCs based on the hidden state probability, obtaining the hidden state subsequences corresponding to the SFCs and obtaining a plurality of hidden state subsequences corresponding to the SFCs; the hidden state subsequence comprises the following elements: and the data center node is deployed on the network function node in the SFC.

In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete mutual communication through the communication bus;

a memory for storing a computer program;

and the processor is used for realizing the cross-domain SFC arranging method in the first aspect when executing the program stored in the memory.

In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer is caused to execute the method for orchestrating cross-domain SFCs according to the first aspect.

In the cross-domain SFC arranging method, device, electronic equipment and storage medium provided by the embodiment of the invention, because hidden states of HMM can not be directly observed, but can be observed through observable sequences, each observable sequence is expressed into various states through probability density distribution, and each observable sequence is generated by a state sequence with corresponding probability density distribution, in the embodiment of the invention, the arranging of the cross-domain SFC is modeled into a hidden Markov model, then each SFC in a plurality of SFCs is arranged by utilizing the hidden Markov model to obtain hidden state subsequences corresponding to each SFC, because the hidden state probability corresponding to the network function nodes in the SFC which are deployed in data center nodes is obtained based on the calculation, the network function nodes of the SFC are deployed, the arranging of the SFC is realized, and the cross-domain bandwidth overhead of the arranged SFC can be reduced, thereby reducing the bandwidth resources consumed after the SFC is arranged.

Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flowchart of an arrangement method of a cross-domain SFC according to an embodiment of the present invention;

FIG. 2 is a flow chart illustrating an implementation of arranging SFCs according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart of an embodiment of S203 in FIG. 2;

FIG. 4 is a schematic structural diagram of an SFC according to an embodiment of the present invention;

fig. 5 is a schematic flowchart of an implementation of backing up a VNF according to an embodiment of the present invention;

FIG. 6a is a simulation diagram of a relationship between VNF overhead and SFC demand according to an embodiment of the present invention;

fig. 6b is a simulation diagram of a relationship between cross-domain bandwidth overhead and SFC demand according to an embodiment of the present invention;

FIG. 6c is a simulation diagram of the relationship between backup overhead and SFC demand according to an embodiment of the present invention;

FIG. 6d is a simulation diagram of the relationship between the total overhead and the SFC demand according to the embodiment of the present invention;

FIG. 7 is a schematic structural diagram of an editing apparatus for cross-domain SFC according to an embodiment of the present invention;

fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As shown in fig. 1, fig. 1 is a schematic flowchart of an orchestration method of a cross-domain SFC according to an embodiment of the present invention, where the method includes:

s101, acquiring a plurality of service function chains SFC to be arranged and a plurality of data center nodes.

In the embodiment of the invention, when the SFC is arranged, a plurality of SFCs to be arranged and a plurality of data center nodes can be obtained. Each SFC comprises a plurality of network function nodes, the data center nodes are used for deploying the network function nodes in the SFC, one network function node is deployed in one data center node, and one data center node represents one data center network.

S102, based on VNF expenses and bandwidth expenses generated when different network function nodes are deployed in different data center nodes, determining initial probabilities of deploying the different network function nodes in the different data center nodes, and constructing an initial probability set based on the determined initial probabilities.

In practical applications, when the network function node is deployed in the data center node, a VNF instance needs to be created, and the network function node is deployed on the created VNF instance, which may generate VNF overhead. Illustratively, the VNF instance may be a vFW (Virtualized Fire Wall), a vLB (Virtualized LoadBalance), etc. When data interaction is performed on networks between different data center nodes, corresponding bandwidth needs to be occupied, so that corresponding bandwidth overhead is generated when different network function nodes are deployed in different data center nodes.

Because the hidden Markov model can calculate the hidden state sequence with the maximum probability according to the known state sequence, and in the process of arranging the SFC, the sequence of the network function nodes in the SFC is known, and the position sequence where the network function nodes to be obtained are arranged is unknown, the arranging problem of the cross-domain SFC is modeled into the hidden Markov model in the embodiment of the invention. Based on VNF overhead and bandwidth overhead generated when different network function nodes are deployed in different data center nodes, an initial probability of deploying the different network function nodes in the different data center nodes is determined.

As an optional implementation manner of the embodiment of the present invention, an initial probability of deploying different network function nodes in different data center nodes may be determined by using a first preset expression, where the first preset expression may be:

in the formula, pi_mRepresenting an initial probability of deploying a network function node to the mth data center node, M representing the number of data center nodes,representing the s-th VNF instance in the m-th data center node,indicating the deployment of a first network function node inThe overhead that is generated is that of the overhead,the bandwidth cost of the initial network function node is represented by the bandwidth cost of the initial data center node, the first network function node is represented by the mth data center node, 01 represents the state transition from the initial network function node to the first network function node of the SFC, and σ m represents the transition from the initial data center node to the mth data center node.

After determining initial probabilities of deploying different network function nodes in different data center nodes, an initial set of probabilities can be constructed based on the determined plurality of initial probabilities. Illustratively, the constructed initial probability set may be expressed as: pi ═ pi₁,π₂,…π_MIn which, pi_MShowing a netAnd the initial probability that the network function node is deployed in the Mth data center node.

S103, constructing a transition probability matrix based on the transition probability determined by the transition from the first state to the second state.

In the embodiment of the invention, the transition probability of the first state to the second state can be determined, and then the transition probability matrix is constructed based on the determined transition probability. The first state corresponds to a process of deploying a first network function node in the SFC in a first data center node, the second state corresponds to a process of deploying a second network function node in the SFC in a second data center node, and the second network function node is a neighboring node of the first network function node. In the embodiment of the invention, the sequence of the network function nodes in the SFC is an observable sequence, and the data center nodes deployed with the network function nodes in the SFC cannot be directly observed, but can be represented into various states through intermediate conversion of the observable sequence. For example, a process of deploying a first network function node in an SFC in a first data center node may be translated into a state, which is represented as a first state.

As an optional implementation manner of the embodiment of the present invention, a second preset expression may be used to determine a transition probability of the first state transitioning to the second state, where the second preset expression may be:

in the formula, indicating a stateTransition to StateTransition probability, state ofRepresents the state corresponding to the process of deploying the (i-1) th network function node in the nth data center nodeRepresents the state corresponding to the process of deploying the ith network function node in the mth data center node,represents the bandwidth overhead of the (i-1) th network function node deployed in the data center node n and the ith network function node deployed in the data center m, wherein (i-1) i represents the transfer from the (i-1) th network function node to the ith network function node, nm represents the transfer from the nth data center node to the mth data center node,representing the unit bandwidth cost from data center node N to data center node m, N representing the number of network topologies of data center nodes,representing the amount of requested bandwidth between the (i-1) th network function node and the ith network function node of the pth SFC.

Illustratively, the constructed transition probability matrix may be represented as:

wherein A represents the constructed transition probability matrix,indicating a stateTransition to StateTransition probability, state ofRepresents the state corresponding to the process of deploying the (i-1) th network function node in the 1 st data center nodeAnd the state corresponding to the process of deploying the ith network function node in the Mth data center node is shown.

And S104, based on the output probability of the function type corresponding to the third network function node in the SFC output under the third state, constructing an output probability matrix.

In the embodiment of the invention, the output probability of the function type corresponding to the third network function node in the output SFC in the third state can be determined, and then the output probability matrix is constructed according to the determined output probability. The third state is a state corresponding to a process of deploying a third network function node in the SFC in a third data center node.

As an optional implementation manner of the embodiment of the present invention, a third preset expression may be used to determine an output probability of a function type corresponding to a third network function node in the output SFC in a third state, where the third preset expression may be:

in the formula,indicating a stateLower output network function typeProbability, state ofRepresents the state corresponding to the process of deploying the ith network function node in the mth data center node,indicates the function type of the i-th network function node of the p-th SFC,indicating a stateType of network function not outputThe probability of (a) of (b) being,representing the s-th VNF instance in the m-th data center node,means for deploying the ith network function node toThe overhead that is generated is that of the overhead,to representThe reliability value of (a) of (b),to representThe type of function of (2).

For example, the constructed output probability matrix can be expressed as:

wherein B represents the constructed output probability matrix,indicating a stateLower output network function typeProbability, state ofRepresents the state corresponding to the process of deploying the ith network function node in the Mth data center node,and the functional type corresponding to the ith network functional node of the p-th SFC is shown.

And S105, constructing a hidden Markov model based on the constructed initial probability set, the transition probability matrix and the output probability matrix.

As an alternative implementation manner of the embodiment of the present invention, a triplet model of a hidden markov model may be constructed based on the constructed initial probability set Π, transition probability matrix a, and output probability matrix B, where the triplet model may be represented as (Π, a, B).

S106, calculating the hidden state probability corresponding to the network function nodes in the SFCs deployed in the data center nodes by using the initial probability, the transition probability and the output probability in the hidden Markov model, and arranging each SFC in the multiple SFCs based on the hidden state probability to obtain the hidden state subsequences corresponding to the SFCs and obtain the multiple hidden state subsequences corresponding to the multiple SFCs.

In the embodiment of the present invention, the order of the network function nodes in the SFC is an observable sequence, and the data center nodes deployed by the network function nodes in the SFC cannot be directly observed, but can be observed through the observable sequence, each observable sequence is represented as various states through probability density distribution, and each observable sequence is generated by a state sequence with corresponding probability density distribution. In the embodiment of the invention, the hidden state probability corresponding to the network function node in the SFC deployed in the data center node is calculated, and each SFC in a plurality of SFCs can be arranged based on the hidden state probability. Wherein, the elements contained in the obtained hidden-state subsequence may be: and the data center node is deployed on the network function node in the SFC.

As an alternative implementation manner of the embodiment of the present invention, a plurality of SFCs to be arranged may be represented as an SFC set. The step S106 may specifically be:

calculating the hidden state probability corresponding to the network function nodes in the SFCs deployed in the data center nodes by using the initial probability, the transition probability and the output probability in the hidden Markov model, arranging each SFC in a plurality of SFCs based on the hidden state probability, obtaining the hidden state subsequence corresponding to each SFC, and obtaining the hidden state sequence set corresponding to the SFC set.

Illustratively, the set of SFCs may be represented as: SFC ═ S¹,S²,…S^qThe hidden state sequence set corresponding to the SFC set can be represented as: q ═ Q¹,Q²,…Q^qIn which S is^qDenotes the Q-th SFC, Q^qThe q-th SFC is shown corresponding to the hidden state subsequence.

As an optional implementation manner of the embodiment of the present invention, a hidden state probability corresponding to a network function node in an SFC deployed in a data center node is calculated by using an initial probability, a transition probability, and an output probability in a hidden markov model, and each SFC in a plurality of SFCs is arranged based on the hidden state probability to obtain a hidden state subsequence corresponding to each SFC, and an implementation manner of obtaining a hidden state sequence set corresponding to an SFC set may refer to fig. 2, where the implementation manner may include:

s201, judging whether the SFC set has the SFC to be arranged.

Judging whether the SFC set to be arranged has the SFC to be arranged or not according to the SFC set to be arranged, if so, indicating that the SFC to be arranged is being arranged or about to start to be arranged, and executing the step S202; if the SFC to be programmed does not exist in the SFC set, it indicates that the SFC to be programmed is programmed completely, then the step of S204 is executed.

S202, if the SFC to be programmed exists in the SFC set, selecting the longest SFC in the SFC set as the current SFC to be programmed.

As an optional implementation manner of the embodiment of the present invention, if there is an SFC to be arranged in the SFC set, in order to simplify complexity of arranging the SFC, a longest SFC in the SFC set is selected as a current arranging SFC, where the longest SFC may be: including the SFC corresponding to the maximum number of network function nodes. Illustratively, the longest SFC may be represented as:wherein,representing the kth network function node of the p SFC.

S203, calculating the hidden state probability corresponding to each network function node in the current layout SFC deployed in the data center node by using the initial probability, the transition probability and the output probability in the hidden Markov model, and arranging the current layout SFC based on the hidden state probability to obtain the hidden state subsequence corresponding to the current layout SFC.

After determining the current layout SFC, the current layout SFC is laid out, and a specific implementation process thereof is described in detail below.

S204, if the SFC set does not have the SFC to be arranged, outputting a hidden state sequence set corresponding to the SFC set.

And if the SFC set does not have the SFC to be arranged, indicating that the arrangement of the SFC to be arranged is finished, and outputting a hidden state sequence set corresponding to the SFC set at the moment.

As an optional implementation manner of the embodiment of the present invention, referring to fig. 3, a specific implementation manner of the step S203 may include:

s2031, judging whether the current layout SFC is finished.

For the current layout SFC, it may be determined whether the current layout SFC is completely laid out, and if so, the step of S2032 is performed; if not, the step of S2036 is performed.

S2032, if the current SFC is not programmed, determining whether the current programming network function node of the current SFC is the first network function node of the current SFC.

When the current SFC is not programmed, it may be determined whether the current scheduling network function node of the current SFC is the first network function node of the current SFC, if so, the step of S2033 is executed; if not, the step of S2034 is performed.

S2033, if the current orchestration network function node of the currently orchestrated SFC is the first network function node of the currently orchestrated SFC, calculating an initial hidden-state probability of deploying the current orchestration network function node in each data center node based on the initial probability in the hidden markov model.

As an optional implementation manner of the embodiment of the present invention, a fourth preset expression may be used to calculate an initial hidden state probability of deploying the current orchestration network function node in each data center node based on the initial probability in the hidden markov model, and since the current orchestration network function node is a first network function node of the current orchestration SFC, the calculated initial hidden state probability corresponding to the first network function node may also be a maximum hidden state probability thereof. The fourth preset expression may be:

in the formula,the initial hidden state probability, pi, of the first network function node representing the current layout SFC being deployed in the data center node m_mRepresenting an initial probability of deploying a network function node to the mth data center node,indicating a stateLower output network function typeProbability, state ofRepresents the state corresponding to the process of deploying the first network function node in the mth data center node,indicating the function type of the first network function node of the p-th SFC.

S2034, if the current arranging network function node of the current arranging SFC is not the first network function node of the current arranging SFC, calculating the maximum hidden state probability of arranging the current arranging network function node in each data center node by using the transition probability and the output probability in the hidden Markov model, and recording the position of the preposed data center node corresponding to the obtained maximum hidden state probability.

As an optional implementation manner of the embodiment of the present invention, when the current scheduling network function node of the current scheduling SFC is not the first network function node of the current scheduling SFC, the fifth preset expression may be used to calculate the maximum hidden state probability of deploying the current scheduling network function node in each data center node, and record the position of the pre-data center node corresponding to the obtained maximum hidden state probability. The fifth preset expression may be:

in the formula,representing the maximum hidden state probability of deploying the ith network function node of the current orchestrated SFC in data center node m,represents the maximum hidden state probability of deploying the i-1 network function node currently arranging the SFC in the data center node x,indicating a stateTransition to StateTransition probability, state ofRepresents the state corresponding to the process of deploying the (i-1) th network function node in the (x) th data center nodeRepresents the state corresponding to the process of deploying the ith network function node in the mth data center node,indicating a stateLower output network function typeProbability, state ofRepresents the state corresponding to the process of deploying the ith network function node in the mth data center node,indicating the function type of the i-th network function node of the p-th SFC.

S2035, using the next network function node of the current arranging SFC as the current arranging network function node of the current arranging SFC, and executing the step of determining whether the current arranging SFC is arranged completely.

After the maximum hidden state probability of deploying the current arranging network function node in each data center node is calculated, and the position of the preposed data center node corresponding to the obtained maximum hidden state probability is recorded, the next network function node of the current arranging SFC is used as the current arranging network function node of the current arranging SFC, and then the step of S2031 is executed until the current arranging SFC is arranged completely.

S2036, if the current SFC arrangement is finished, taking the last network function node of the current SFC arrangement as the current network function node, selecting the fourth data center node corresponding to the maximum hidden state probability of the current network function node to deploy the current network function node, and storing the fourth data center node in the hidden state subsequence corresponding to the current SFC arrangement.

When the current SFC is arranged, taking the last network function node of the current arranged SFC, namely the last network function node of the current arranged SFC as the current network function node, selecting a fourth data center node which corresponds to the maximum hidden state probability of the current network function node to be arranged in the current network function node, and storing the fourth data center node in a hidden state subsequence corresponding to the current arranged SFC. Illustratively, the current SFC is the pth SFC, and the last network function node of the current SFC isEnabling current network function nodesD is the fourth data center node corresponding to the maximum hidden state probability_mD is mixing_mAnd storing the hidden state subsequence corresponding to the current layout SFC.

S2037, determining the pre-data center node corresponding to the maximum hidden-state probability of the current network function node as a fifth data center node corresponding to a network function node before the current network function node, deploying the network function node before the current network function node in the fifth data center node, storing the fifth data center node in the hidden-state subsequence corresponding to the currently deployed SFC, and using the network function node before the current network function node as the current network function node.

Exemplary, with current network function nodeD is the fourth data center node corresponding to the maximum hidden state probability_mMake the current network function nodeThe prepositive data center node corresponding to the maximum hidden state probability, namely the previous network function node of the current network function nodeCorresponding fifth data center node d_xAt d_xIn deploymentWill d_xStoring in the hidden state subsequence corresponding to the current layout SFC and storing in the hidden state subsequence corresponding to the current layout SFCAs the current network function node.

S2038, determine whether a previous network function node of the current network function node is a first network function node where the SFC is currently arranged.

And judging whether the previous network function node of the current network function node is the first network function node of the current SFC, if so, indicating that the network function node of the current SFC is completely deployed, and obtaining a hidden state subsequence corresponding to the current SFC, executing the step S2039, otherwise, indicating that the network function node of the current SFC is not completely deployed, and returning to the step S2037. Illustratively, the current orchestration SFC is S^pThe resulting hidden-state subsequence corresponding to the pre-arranged SFC may be represented as:wherein,indicating pre-layout S^pDeployed by the Kth network function node inData center node d of_K。

S2039, if the previous network function node of the current network function node is the first network function node of the current SFC, deleting the current SFC from the SFC set.

And if the previous network function node of the current network function node is the first network function node of the current SFC, the network function node of the current SFC is completely deployed, and the hidden state subsequence corresponding to the current SFC is obtained, deleting the current SFC from the SFC set, and continuously arranging the next SFC to be arranged.

The cross-domain SFC arranging method provided by the embodiment of the invention has the advantages that because the hidden state of the HMM can not be directly observed, it can be observed that each observable sequence is represented by a probability density distribution as a variety of states, each observable sequence being generated by a sequence of states having a corresponding probability density distribution, in embodiments of the invention, modeling the layout of the cross-domain SFC into a hidden Markov model, and then utilizing the hidden Markov model, arranging each SFC in the plurality of SFCs to obtain a hidden state subsequence corresponding to each SFC, wherein the hidden state subsequence is based on the hidden state probability corresponding to the network function node in the SFC which is obtained by calculation and is deployed in the data center node, the network function nodes of the SFC are deployed, the arrangement of the SFC is realized, the cross-domain bandwidth overhead of the SFC after arrangement can be reduced, and the bandwidth resources consumed after the SFC is arranged are further reduced.

As an optional implementation manner of the embodiment of the present invention, after step S106, the arranging of the cross-domain SFC according to the embodiment of the present invention may further include: and backing up the VNF based on the hidden state sequence set, the reliability value corresponding to each SFC after arrangement and the cost benefit value of the VNF corresponding to each network function node in the SFC corresponding to each hidden state subsequence in the hidden state sequence set.

In practice, the user will have a reliability requirement for each SFC. Illustratively, as shown in FIG. 4, there are twoSFCs, service function chain 1 and service function chain 2, respectively. The service function chain 1 starts at the client a, and a service flow needs to sequentially pass through FW (Fire Wall), LB (load balance, traffic equalizer) and NAT (Network address translator), and finally flows to the client C. The service function chain 2 starts at client B and the traffic flows sequentially through FW, LB and GW (Gateway) and finally to client D. Wherein, the reliability requirement of the service function chain 1 is R₁The reliability requirement of the service function chain 2 is R80%₂＝85％。

Illustratively, there are 5 data center nodes, which are: DC1, DC2, DC3, DC4 and DC 5. After the service function chain 1 and the service function chain 2 are arranged, the arrangement result is: the service function chain 1 service flow sequentially passes through the vFW deployed at DC1, the vLB deployed at DC3 and the vNAT (virtual Network Address translator) deployed at DC4, and reaches the client C. Wherein, if the reliability value of deploying vFW to DC1 is 0.92, the reliability value of deploying vLB to DC3 is 0.82, and the reliability value of deploying vNAT to DC4 is 0.93, the reliability value of deploying the service function chain 1 is: 0.92X 0.82X 0.93%<R₁＝80％。

The service function chain 2 service flow sequentially passes through the vFW deployed at DC2, the vLB deployed at DC3 and the vGW (virtual Gateway) deployed at DC5, and finally reaches the client D. Wherein the reliability value of deploying vFW to DC2 is 0.97, the reliability value of deploying vLB to DC3 is 0.82, the reliability value of deploying vGW to DC5 is 0.74, and the reliability value of deploying service function chain 2 is 0.97 × 0.82 × 0.74 ═ 58.86%<R₂＝85％。

Therefore, in practical application, after the SFCs are arranged, the reliability requirements of the SFCs may not necessarily meet the requirements of users, and therefore, in the embodiment of the present invention, the VNFs may be backed up based on the hidden state sequence set, the reliability values corresponding to the arranged SFCs, and the cost benefit value of the VNF corresponding to each network function node in the SFCs corresponding to each hidden state subsequence in the hidden state sequence set, so as to improve the reliability values of the arranged SFCs after the VNFs are backed up.

As an optional implementation manner of the embodiment of the present invention, as shown in fig. 5, the step of backing up the VNF may specifically include:

s301, traversing each hidden state subsequence in the hidden state sequence set, and taking the SFC corresponding to the hidden state subsequence as the current SFC.

And traversing each hidden state subsequence in the hidden state sequence set aiming at each SFC which is already arranged, and taking the SFC corresponding to the hidden state subsequence as the current SFC.

S302, calculating the reliability value of the current SFC.

As an optional implementation manner of the embodiment of the present invention, for each current SFC, calculating the reliability value of the current SFC may be: and calculating the reliability value of the current SFC by using a sixth preset expression. The sixth preset expression may be:

in the formula, R^pThe reliability value of the p-th SFC is shown, K represents the number of network function nodes of the p-th SFC,representing the reliability value of the ith network function node of the p SFC. Wherein,the position of the data center node deployed by the ith network function node in the p-th SFC orchestration process and the function type of the ith network function node are determined, that is, when the SFC orchestration is completed, the ith network function node is deployed in the data center node, the reliability value of the VNF in the data center node is determined by the function type of the VNF, that is, when the SFC orchestration is completed,is determined.

S303, judging whether the reliability value of the current SFC is smaller than a preset reliability value.

After the calculation of the reliability values of all the programmed SFCs is completed, for each SFC, it may be determined whether the reliability value of the current SFC is less than a preset reliability value. The preset reliability value is the reliability requirement value of the user for the SFC.

S304, when the reliability value of the current SFC is smaller than the preset reliability value, the current SFC is placed in the first set, and VNFs passing through the current SFC are placed in the second set.

And when the reliability value of the current SFC is smaller than the preset reliability value, the reliability value of the current SFC after being arranged does not meet the requirement of a user, the current SFC is placed in the first set, and the VNF passing through the current SFC is placed in the second set.

S305, judging whether the first set is empty or not.

And placing the SFCs with the reliability values which do not meet the requirements of the user in the first set, wherein if the first set is empty, the reliability values of all the SFCs after the SFCs are arranged meet the requirements of the user, and if the first set is not empty, the reliability values of the SFCs after the SFCs are arranged do not meet the requirements of the user.

S306, when the first set is not empty, calculating a cost benefit value of each VNF in the second set.

The first set is not empty, which indicates that the reliability value of the SFC does not meet the requirement of the user after the SFC is arranged, and at this time, the reliability value of each SFC in the first set after the VNF is backed up may be calculated by using the sixth preset expression for each VNF in the second set. Then, based on the reliability value of each SFC in the first set after the VNF is backed up, the first preset reliability value, the processing capacity demand of the backup VNF, and the unit processing capacity cost of the backup VNF, a cost benefit value of each VNF in the second set is calculated using a seventh preset expression. The seventh preset expression may be:

wherein,

in the formula,representing the s-th VNF instance in the m-th data center node,representing backupsP denotes the p-th SFC, q denotes the number of SFCs,representing backupsImprovement value of p-th SFC reliability, R^pRepresenting backupsReliability value of the last p-th SFC, phi^pRepresenting a first preset reliability value, which may be a user set reliability value,representing backupsTo the extent that the reliability of all SFCs is improved,to representCost incurred, α_mRepresenting the cost per unit of processing capacity of the backup VNF,to representThe amount of processing power required of (a),to representThe type of function of (2).

When backing upReliability value R of last p-th SFC^pIf the reliability value is larger than the first preset reliability value, the method willTake the result to be 1, so that backup is performedResulting in higher reliability enhancement values and lower costHas higher cost benefit value.

And S307, backing up the VNF corresponding to the maximum cost benefit value.

After calculating the cost-benefit value of each VNF in the second set, the VNF corresponding to the largest cost-benefit value may be backed up. In practical applications, one VNF corresponding to the maximum cost benefit value may be backed up, and multiple VNFs corresponding to the maximum cost benefit value may also be backed up, and a specific embodiment of the present invention is not limited herein.

S308, calculating the reliability value of each SFC in the first set after backup.

In this embodiment of the present invention, after the VNF corresponding to the maximum cost benefit value is backed up, the reliability value of each SFC in the first set after backup is calculated, specifically, the reliability value of each SFC in the first set after backup is calculated by referring to the calculation manner of the reliability value of the current SFC in step S302, which is not described herein again in this embodiment of the present invention.

S309, if the reliability value of the SFC in the first set after backup is not less than the preset reliability value, deleting the SFC from the first set, and executing the step of judging whether the first set is empty or not.

After the reliability value of each SFC in the first set after backup is calculated, the reliability value of the SFC may be determined, and whether it is not less than a preset reliability value is determined, if the reliability value of the SFC in the first set after backup is not less than the preset reliability value, it is indicated that the reliability value of the SFC at this time meets the requirement of the user, the SFC is deleted from the first set, and the step of S305 is returned to be executed until the reliability values of all the SFCs in the first set meet the requirement of the user.

The cross-domain SFC arranging method provided by the embodiment of the invention has the advantages that because the hidden state of the HMM can not be directly observed, it can be observed that each observable sequence is represented by a probability density distribution as a variety of states, each observable sequence being generated by a sequence of states having a corresponding probability density distribution, in embodiments of the invention, modeling the layout of the cross-domain SFC into a hidden Markov model, and then utilizing the hidden Markov model, arranging each SFC in the plurality of SFCs to obtain a hidden state subsequence corresponding to each SFC, wherein the hidden state subsequence is based on the hidden state probability corresponding to the network function node in the SFC which is obtained by calculation and is deployed in the data center node, the network function nodes of the SFC are deployed, the arrangement of the SFC is realized, the cross-domain bandwidth overhead of the SFC after arrangement can be reduced, and the bandwidth resources consumed after the SFC is arranged are further reduced. In addition, the VNF is backed up, and the reliability value of the SFC after backup is improved, so that the reliability value of each SFC after backup can meet the requirements of users.

Exemplarily, different layout methods are used for layout of the SFC in the embodiment of the present invention, and the obtained simulation graphs are respectively shown in fig. 6a to fig. 6 d. Where prior art method 1 is the prior art described in the background, prior art method 2 is: the specific implementation process of the scheduling problem of the SFC is realized by referring to the implementation in the prior art, and details of the implementation process of the scheduling problem of the SFC are not described herein. In the embodiment of the invention, a multi-data center network is selected from a large-scale accurate network topology structure topology-zoo, the bandwidth across the data center is set to be 200Gbps, the unit cost across the data center bandwidth is a random value which is uniformly distributed between 0.01,0.02] $/Mbps, the unit IT resource cost of the data center is 0.05,0.10] $/unit, IT resources requested by different virtual network function nodes are randomly selected between [1 and 3], the reliability value of the virtual network function node is set to be [0.8 and 0.99], the length of an SFC is uniformly distributed between [2 and 6], the requested bandwidth of the SFC meets the uniform distribution of [10 and 100] Mbps, and the reliability value of the SFC is randomly selected between [0.95,0.98,0.99,0.995 and 0.999 ].

Fig. 6a is a simulation diagram of a VNF overhead and SFC demand relationship provided by the embodiment of the present invention, and when the number of requests of an SFC is less than 400, the prior art method 1 uses a smaller VNF instance to process an incoming SFC request. However, when the number of requests of SFC is greater than 600, VNF overhead of the prior art method 1 may increase significantly. The embodiment of the invention uses more VNFs under the condition of small-scale SFC requests, because the embodiment of the invention processes the requests according to the descending order of the lengths of the SFCs, neglects other relevance of the SFCs to reduce the complexity of the problem and causes more VNF expenses. When a large-scale SFC request is processed, the VNF spending of the embodiment of the invention is not much different from the VNF spending of the method 2 in the prior art, because the embodiment of the invention converts the SFC arrangement into the decoding problem of the hidden Markov model, the using efficiency of the VNF is fully considered, and the VNF spending with lower cost is realized.

Fig. 6b is a simulation diagram of a relationship between cross-domain bandwidth overhead and SFC demand provided by the embodiment of the present invention, and when the same number of SFCs are arranged, the prior art method 1 uses more cross-domain bandwidth overhead, because the prior art method 1 merges the SFCs into the graph to reduce the use of VNF instances, but this may result in higher bandwidth consumption. The embodiment of the invention considers the bandwidth overhead and the VNF overhead at the same time, and compared with the method 1 in the prior art, the used bandwidth cost is reduced by about 26.2%. When the number of SFC requests is less than 400, the bandwidth cost of the embodiment of the present invention is comparable to prior art method 2. When the number is greater than 600, compared with the method 2 in the prior art, the bandwidth cost between the data center nodes used in the embodiment of the present invention is increased by about 11.3%, because the output probability in the hidden markov model of the embodiment of the present invention considers both the VNF overhead and the VNF reliability. Therefore, the embodiment of the invention sacrifices partial cross-domain bandwidth overhead to obtain the high-reliability SFC arrangement result.

FIG. 6c is a simulation graph of backup overhead versus SFC demand provided by an embodiment of the present invention that uses backup overhead comparable to prior art method 2 when the number of SFC requests is less than 600. When the number of the SFC requests is greater than 800, the backup overhead used in the embodiment of the present invention is reduced by 13.2%, because the embodiment of the present invention considers the reliability requirement when the SFC is programmed using the hidden markov model, and the embodiment of the present invention sacrifices the deployment of the cross-domain bandwidth to realize the SFC programming with higher reliability.

Fig. 6d is a simulation diagram of a relationship between total overhead and SFC demand provided in the embodiment of the present invention, where the total overhead includes three parts: VNF overhead (as shown in fig. 6 a), cross-domain bandwidth overhead (as shown in fig. 6 b), and VNF backup overhead (as shown in fig. 6 c). It is clear that the prior art method 1 consumes more overhead than the results obtained with the inventive example and the method of prior art method 2. The cost of prior art method 1 is about 20.4% and 15.6% higher, respectively, than the results obtained with prior art method 2 and the method of the present example. When the number of SFCs is greater than 600, the total overhead of the embodiment of the present invention is about 11.4% higher than that of the prior art method 2.

Although the cost of the embodiment of the invention is relatively high, the backup of the VNF is carried out after the SFC is arranged in the embodiment of the invention, so that the reliability of the SFC can be improved, and the requirements of users can be better met.

Corresponding to the foregoing method embodiment, an embodiment of the present invention provides an apparatus for arranging cross-domain SFCs, as shown in fig. 7, the apparatus may include:

the acquiring module 401 is configured to acquire a plurality of service function chains SFCs to be arranged and a plurality of data center nodes, where each SFC includes a plurality of network function nodes, each data center node is configured to deploy a network function node in the SFC, and each network function node is deployed in one data center node.

A first constructing module 402, configured to determine initial probabilities of deploying different network function nodes in different data center nodes based on virtual network function VNF overheads and bandwidth overheads generated when the different network function nodes are deployed in different data center nodes, and construct an initial probability set based on the determined multiple initial probabilities.

A second construction module 403, configured to construct a transition probability matrix based on the transition probability determined by the transition from the first state to the second state; the first state corresponds to a process of deploying a first network function node in the SFC in a first data center node, the second state corresponds to a process of deploying a second network function node in the SFC in a second data center node, and the second network function node is a neighboring node of the first network function node.

A third constructing module 404, configured to construct an output probability matrix based on an output probability of a function type corresponding to a third network function node in the SFC output in a third state; the third state is a state corresponding to a process of deploying a third network function node in the SFC in a third data center node.

A fourth construction module 405, configured to construct a hidden markov model based on the constructed initial probability set, transition probability matrix, and output probability matrix.

The arranging module 406 is configured to calculate a hidden state probability corresponding to a network function node in the SFC being deployed in a data center node by using an initial probability, a transition probability and an output probability in the hidden markov model, and arrange each of the SFCs based on the hidden state probability to obtain a hidden state subsequence corresponding to each SFC, so as to obtain a plurality of hidden state subsequences corresponding to the SFCs; the hidden state subsequence comprises the following elements: and the data center node is deployed on the network function node in the SFC.

It should be noted that the apparatus according to the embodiment of the present invention is an apparatus corresponding to the method for organizing cross-domain SFCs shown in fig. 1, and all embodiments of the method for organizing cross-domain SFCs shown in fig. 1 are applicable to the apparatus and can achieve the same or similar beneficial effects.

The cross-domain SFC arranging device provided by the embodiment of the invention has the advantages that because the hidden state of the HMM can not be directly observed, it can be observed that each observable sequence is represented by a probability density distribution as a variety of states, each observable sequence being generated by a sequence of states having a corresponding probability density distribution, in embodiments of the invention, modeling the layout of the cross-domain SFC into a hidden Markov model, and then utilizing the hidden Markov model, arranging each SFC in the plurality of SFCs to obtain a hidden state subsequence corresponding to each SFC, wherein the hidden state subsequence is based on the hidden state probability corresponding to the network function node in the SFC which is obtained by calculation and is deployed in the data center node, the network function nodes of the SFC are deployed, the arrangement of the SFC is realized, the cross-domain bandwidth overhead of the SFC after arrangement can be reduced, and the bandwidth resources consumed after the SFC is arranged are further reduced.

Optionally, an initial probability of deploying different network function nodes in different data center nodes is determined by using a first preset expression.

And determining the transition probability of the first state to the second state by using a second preset expression.

And determining the output probability of the function type corresponding to the third network function node in the output SFC in the third state by using a third preset expression.

The first preset expression is:

in the formula, pi_mRepresenting an initial probability of deploying a network function node to the mth data center node, M representing the number of data center nodes,representing the s-th VNF instance in the m-th data center node,represents the overhead incurred by deploying the first network function node to the s-th VNF instance in the m-th data center node,the bandwidth cost of the initial network function node is represented by the bandwidth cost of the initial data center node, the first network function node is represented by the mth data center node, 01 represents the state transition from the initial network function node to the first network function node of the SFC, and σ m represents the transition from the initial data center node to the mth data center node.

The second preset expression is:

The third preset expression is:

Optionally, the plurality of SFCs to be programmed are a set of SFCs; calculating the hidden state probability corresponding to the network function nodes in the SFCs deployed in the data center nodes by utilizing the initial probability, the transition probability and the output probability in the hidden Markov model, arranging each SFC in a plurality of SFCs based on the hidden state probability to obtain the hidden state subsequences corresponding to each SFC, and obtaining a plurality of hidden state subsequences corresponding to the plurality of SFCs as follows:

Optionally, the orchestration module 406 comprises:

and the judging submodule is used for judging whether the SFC to be arranged exists in the SFC set.

And the selection submodule is used for selecting the longest SFC in the SFC set as the current layout SFC when the SFC to be laid exists in the SFC set.

And the arranging submodule is used for calculating the hidden state probability corresponding to each network function node in the current arranging SFC deployed in the data center node by utilizing the initial probability, the transition probability and the output probability in the hidden Markov model, and arranging the current arranging SFC based on the hidden state probability to obtain the hidden state subsequence corresponding to the current arranging SFC.

And the output submodule is used for outputting the hidden state sequence set corresponding to the SFC set when the SFC set to be arranged does not exist.

Optionally, the programming submodule is specifically configured to:

judging whether the current layout SFC is finished or not;

if the current arranging network function node of the current arranging SFC is not the first network function node of the current arranging SFC, calculating the maximum hidden state probability of arranging the current arranging network function node in each data center node by using the transition probability and the output probability in the hidden Markov model, and recording the position of the preposed data center node corresponding to the obtained maximum hidden state probability;

if the current SFC arrangement is finished, taking the last network function node of the current SFC arrangement as the current network function node, selecting a fourth data center node which corresponds to the maximum hidden state probability of the current network function node to deploy the current network function node, and storing the fourth data center node in a hidden state subsequence corresponding to the current SFC arrangement;

determining a preposed data center node which corresponds to the maximum hidden state probability of the current network function node as a fifth data center node corresponding to a network function node before the current network function node, deploying the network function node before the current network function node in the fifth data center node, storing the fifth data center node in a hidden state subsequence corresponding to the current layout SFC, and taking the network function node before the current network function node as the current network function node;

judging whether a previous network function node of the current network function node is a first network function node of the current SFC;

if the previous network function node of the current network function node is not the first network function node of the current layout SFC, executing a step of determining a preposed data center node which corresponds to the hidden state probability of the current network function node to be the fifth data center node corresponding to the previous network function node of the current network function node;

the current orchestrated SFC is deleted from the set of SFCs if a previous network function node to the current network function node is the first network function node to currently orchestrate the SFC.

Optionally, the apparatus in the embodiment of the present invention further includes: and the backup module is used for backing up the VNF based on the hidden state sequence set, the reliability value corresponding to each SFC after arrangement and the cost benefit value of the VNF corresponding to each network function node in the SFC corresponding to each hidden state subsequence in the hidden state sequence set.

Optionally, the backup module is specifically configured to:

calculating the reliability value of the current SFC;

when the reliability value of the current SFC is smaller than the preset reliability value, placing the current SFC in a first set, and placing VNFs passing through the current SFC in a second set;

judging whether the first set is empty;

backing up the VNF corresponding to the maximum cost benefit value;

calculating the reliability value of each SFC in the first set after backup;

An embodiment of the present invention further provides an electronic device, as shown in fig. 8, which includes a processor 501, a communication interface 502, a memory 503 and a communication bus 504, where the processor 501, the communication interface 502 and the memory 503 complete mutual communication through the communication bus 504,

a memory 503 for storing a computer program;

the processor 501 is configured to implement the cross-domain SFC arrangement provided by the embodiment of the present invention when executing the program stored in the memory 503.

In the electronic device provided by the embodiment of the invention, because the hidden state of the HMM cannot be directly observed, it can be observed that each observable sequence is represented by a probability density distribution as a variety of states, each observable sequence being generated by a sequence of states having a corresponding probability density distribution, in embodiments of the invention, modeling the layout of the cross-domain SFC into a hidden Markov model, and then utilizing the hidden Markov model, arranging each SFC in the plurality of SFCs to obtain a hidden state subsequence corresponding to each SFC, wherein the hidden state subsequence is based on the hidden state probability corresponding to the network function node in the SFC which is obtained by calculation and is deployed in the data center node, the network function nodes of the SFC are deployed, the arrangement of the SFC is realized, the cross-domain bandwidth overhead of the SFC after arrangement can be reduced, and the bandwidth resources consumed after the SFC is arranged are further reduced.

The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.

The communication interface is used for communication between the electronic equipment and other equipment.

The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.

The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.

In yet another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the above cross-domain SFC orchestration methods.

In yet another embodiment, a computer program product containing instructions is provided, which when run on a computer causes the computer to perform any of the above-described cross-domain SFC orchestration methods.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device/electronic apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and for the relevant points, reference may be made to some descriptions of the method embodiments.

The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for orchestrating cross-domain SFCs, the method comprising:

2. The method of claim 1, wherein an initial probability of deploying the different network function nodes in the different data center nodes is determined using a first preset expression;

the first preset expression is as follows:

in the formula, pi_mRepresenting an initial probability of deploying the network function node to an mth data center node, M representing a number of data center nodes,representing the s-th VNF instance in the m-th data center node,represents the overhead incurred by deploying the first network function node to the s-th VNF instance in the m-th data center node,representing the bandwidth transfer overhead generated by deploying an initial network function node to an initial data center node, deploying a first network function node to an m-th data center node, 01 representing the state transfer from the initial network function node to the first network function node of the SFC, and σ m representing the transfer from the initial data center node to the m-th data center node;

the second preset expression is as follows:

in the formula, presentation formState of the artTransition to StateTransition probability, state ofRepresents the state corresponding to the process of deploying the (i-1) th network function node in the nth data center nodeRepresents the state corresponding to the process of deploying the ith network function node in the mth data center node,represents the bandwidth overhead of the (i-1) th network function node deployed in the data center node n and the ith network function node deployed in the data center m, wherein (i-1) i represents the transfer from the (i-1) th network function node to the ith network function node, nm represents the transfer from the nth data center node to the mth data center node,representing the unit bandwidth cost from data center node N to data center node m, N representing the number of network topologies of data center nodes,representing the requested bandwidth amount between the (i-1) th network function node and the ith network function node of the p-th SFC;

the third preset expression is as follows:

3. The method of claim 2, wherein the plurality of SFCs to be orchestrated are a set of SFCs; calculating, by using the initial probability, the transition probability, and the output probability in the hidden markov model, a hidden state probability corresponding to a network function node in the SFC deployed in a data center node, and arranging each of the SFCs based on the hidden state probability to obtain a hidden state subsequence corresponding to each SFC, where the obtained multiple hidden state subsequences corresponding to the multiple SFCs are:

4. The method according to claim 3, wherein the step of calculating a hidden state probability corresponding to a network function node in the SFC deployed in a data center node by using the initial probability, the transition probability and the output probability in the hidden markov model, and arranging each SFC in the SFCs based on the hidden state probability to obtain a hidden state subsequence corresponding to each SFC, and obtain a hidden state sequence set corresponding to the SFC set, comprises:

judging whether the SFC set contains SFCs to be arranged or not;

5. The method according to claim 4, wherein the step of calculating a hidden state probability corresponding to each network function node deployed in a data center node in the currently deployed SFC by using the initial probability, the transition probability and the output probability in the hidden Markov model, and deploying the currently deployed SFC based on the hidden state probabilities to obtain a hidden state subsequence corresponding to the currently deployed SFC comprises:

judging whether the current layout SFC is finished or not;

6. The method of claim 3, further comprising:

7. The method of claim 6, wherein the step of backing up the VNF based on the set of hidden-state sequences, the reliability value corresponding to each SFC after the arranging, and the cost-benefit value of the VNF corresponding to each network function node in the SFC corresponding to each hidden-state subsequence in the set of hidden-state sequences comprises:

calculating a reliability value of the current SFC;

determining whether the first set is empty;

backing up the VNF corresponding to the maximum cost benefit value;

calculating the reliability value of each SFC in the first set after backup;

8. An apparatus for orchestration of cross-domain SFCs, the apparatus comprising:

9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;

a memory for storing a computer program;

a processor for implementing the method steps of any of claims 1 to 7 when executing a program stored in the memory.

10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.