CN109714219B - Virtual network function rapid mapping method based on satellite network - Google Patents
Virtual network function rapid mapping method based on satellite network Download PDFInfo
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Abstract
The invention discloses a virtual network function fast mapping algorithm based on a satellite network, which comprises the following steps: designing a software defined network and network function virtualization collaborative deployment framework based on a satellite network; designing a dynamic mapping method of the virtualized network function. The software defined network and network function virtualization collaborative deployment framework designed by the invention can enable the network function to be decoupled in the hardware equipment, thereby improving the flexibility and survivability of the network. The dynamic mapping method of the virtualized network function is divided into two steps, wherein a feasible mapping path set in one operation period of the satellite network is calculated in a static step, and the feasible mapping paths are weighted and ordered according to the length of the mapping delay; and in the dynamic step, a graph model matching algorithm is adopted to match an optimal mapping path from the feasible mapping paths with different weights in the time slice, and an arrangement strategy is formulated at the same time. The invention can greatly reduce time delay and meet the requirement of high dynamic change of a satellite network topological structure.
Description
Technical Field
The invention relates to cooperative deployment of Software Defined Network (SDN) and Network Function Virtualization (NFV) in a satellite network and a mapping technology of Virtualized Network Function (VNF) to an underlying physical network in a framework model of the cooperative deployment, in particular to a rapid mapping method of the virtual network function based on the satellite network.
Background
As an indispensable part of a global communication system, a satellite network has wide coverage and is not limited by regions, and meanwhile, the satellite network is an important component of a future space-ground integrated network as a beneficial supplement of a ground traditional network. With the rapid development of network technology, people put higher demands on satellite networks, and need to bear more kinds of services, larger data scale, and more stability and flexibility. However, in the conventional satellite network, there are the following problems:
a) the satellite is functionally deficient. Satellites are limited in volume and weight and do not allow for the deployment of excessive physical equipment. The number and kind of payloads on the satellite are limited and the functionality on the satellite is lacking.
b) Network management is inefficient. In a conventional satellite network, a management architecture is decentralized, and a fixed link allocation and a static routing policy are predetermined, so that the network cannot support fine-grained resource management and constantly changing user demands.
c) Network functions cannot be deployed flexibly. The periodically operated high dynamic topology satellite network ensures that different network functions need to be scheduled when different satellites carry out information interaction with the ground station, namely the requirement of the satellite network function is variable; in the traditional network, the network functions are realized by combining software and hardware, and need to be deployed in advance before satellite transmission, so that the satellite network functions are redundant or insufficient, and flexible deployment cannot be realized. Therefore, there is a need to provide a new flexible and efficient satellite network architecture. In recent years, new network technologies such as software defined networking and network function virtualization provide a new approach to solving the problems of the conventional satellite network.
Network function virtualization is a novel network technology proposed by the trend of telecommunication IT with the rise of cloud computing in recent years. The hardware minimization reduces the dependence on hardware, and the essence is to strip the software function from special hardware equipment to realize the independence of the software and the hardware after the decoupling.
Because the software defined network control plane supports fine-flow defined packet forwarding, software defined networks are an ideal choice for dynamic management of network function virtualization. The software defined network can decouple the management function of the control plane and the forwarding function of the data plane, so that the control plane for centralized management is separated from the data plane for distributed data forwarding, and the management and configuration of the network are simplified. The combination of software defined networking and network function virtualization is a novel network architecture which is widely researched and has a wide application prospect.
With the deep development of the software-defined network technology, the research on applying the software-defined network technology to the satellite network for numerical control separation is more and more. The OpenSAN proposed by Bao et al is a software-defined satellite network architecture, which provides high-efficiency, fine-grained and flexible control for a satellite network, and improves the applicability of the software-defined network in the satellite network. Feng et al propose a software-defined Emergency response Space (ORS) satellite network scheme that can provide a flexible deployment scheme for emergency responses. However, neither OpenSAN nor software-defined ORS can flexibly invoke network functions according to service scenarios, so that network functions are limited in satellite networks. Bertaux et al investigated the advantages of using software defined networking/networking functionality virtualization in satellite networks to introduce network programmability and virtualization by optimizing the networking framework through networking functionality virtualization technology. Ferr us et al describe in detail the benefits that software defined networking/networking functionality virtualization technology can bring to 5G satellite communications through reasonable analysis of application scenarios. But Bertaux and Ferr-s were primarily studied for the terrestrial segment of the satellite, the spatial segment of which was only used as a transport communication and was not studied in depth.
The network function virtualization technology virtualizes network functions, so that different virtual network functions can be deployed on each normalized hardware, the flexibility of satellite network management and configuration is improved, and a new solution thought is provided for the satellite deployment large-scale functions with limited loads.
Disclosure of Invention
The invention aims to design a rapid mapping method of a virtual network function based on a satellite network, so as to solve the problem of overlarge mapping delay caused by complicated conditions required by mapping the virtual network function to a bottom physical network.
In order to achieve the purpose, the technical scheme of the invention is as follows: a virtual network function fast mapping method based on a satellite network comprises the following steps:
A. software defined network and network function virtualization collaborative deployment framework based on satellite network
The software defined network and network function virtualization collaborative deployment framework based on the satellite network adopts a satellite network framework of three-layer traffic scheduling, the framework comprises an application plane, a control plane and a forwarding plane, a high-orbit satellite is used as the control plane of the framework, a medium-low orbit satellite is used as the forwarding plane of the framework, and a ground station is used as the application plane of the framework. And virtualizing a virtualized network function on a low-orbit satellite of a forwarding plane by adopting a network function virtualization technology.
A1, design application plane
The application plane includes ground stations and other users. The ground station is responsible for resource management, network security and policy making, and is a coordinator of the entire network. The ground station makes a strategy according to satellite state information collected by the geosynchronous orbit satellite, and sends the strategy to the geosynchronous orbit satellite through a special channel. The geosynchronous orbit satellite controls the work of the medium orbit satellite and the low orbit satellite by issuing a flow table.
A2, design control plane
The high orbit satellite serves as a control plane. The high orbit satellite is used as a controller which is responsible for collecting the link condition information between the satellites and sending the information to the ground station for processing. At the same time, the instructions of the ground station are sent to the medium orbit satellite through the geosynchronous orbit satellite. When the commands from the ground station arrive at the geosynchronous orbit satellite, they are issued to the medium orbit satellite by means of a flow table.
A3, design Forwarding plane
The forwarding plane is composed of medium orbit satellites and low orbit satellites and is dynamically configured by the controller. The medium orbit satellite retains only the forwarding function. When data from a low orbit satellite or other medium orbit satellite reaches a certain medium orbit satellite, only the flow table is searched for matching forwarding information, and then the data packet is forwarded to the next satellite node. The low earth orbit satellite not only reserves partial forwarding function, but also adds virtualization function, wherein the virtualization function comprises a virtual satellite gateway, a firewall, a performance enhancement agent, network address conversion and a virtual private network agent.
B. Dynamic mapping method for designing virtualized network function
The mapping problem of the virtualized network function needs to consider virtualizing one or more networks under the satellite network topology of the current time slice on the basis of meeting the forwarding requirementNetwork functions are instantiated on physical resources. The mapping process is represented as a graph PG (V, E). Wherein V is a set of satellite nodes representing servers hosting virtualized network functions; e is a set of edges representing network connections between nodes. When the virtualized network function request is mapped to the underlying physical network, not only are nodes satisfying the accommodation of the virtualized function screened out, but also edges supporting data transmission between the virtualized network functions are searched. The virtualized network function configuration work is to initiate a path search for each virtualized network function request over the entire underlying physical network, with the worst case time complexity of O (| V |)2|E|)。
Therefore, the following conditional constraints must be satisfied to complete the virtualized network function mapping in the satellite network:
one network function cannot be deployed to multiple satellite nodes, and the formula is as follows:
the satellite nodes bearing the mapping need to satisfy the resources of some kind of virtualized network function mapping and have enough remaining resources. Meanwhile, the connecting edge between the nodes has enough residual resources, and the formula is as follows:
considering the limitation of data flow, the whole service chain flow is less than a certain constant, and the formula is as follows:
the topological structure of the satellite network is highly dynamically changed, but the period of the satellite network is discretized into n time slices by taking the operating periodicity of the satellite into consideration, and a two-dimensional matrix B is usedtNetwork topology connection situation representing the current time slice, Bt(i, j) ═ 1 denotes the node viAnd vjConnected by links, Bt(i, j) ═ 0 denotes the node viAnd vjWithout links connected, the formula is as follows:
presence of (i, j) such that Bt(i,j)＝1
Mathematically, the network function configuration problem is expressed as an objective function satisfying the constraints of equations (1) - (4):
Φ(PG,s) (5)
where s is a set of service chains associated with the virtualized network function request, Φ (PG, s) represents the optimization function implemented by mapping the service chains s over a given underlying physical network PG.
The objective function is that when mapping the virtualized network function to the underlying physical network, the total delay is the lowest, i.e. the following equation is satisfied:
Φ(PG,s)＝min∑s∈Sm(s) (6)
s in the above formula represents a virtualized network function service chain set, m (S) represents the mapping delay of a service chain S, and the problem solving complexity is NP-hard.
The method specifically comprises the following steps:
b1, static step
In the static step, the process of virtualizing the network function map is modeled. Because the context of the virtualized network functions in the service chain, the node virtualized network function mapping conditions, and the underlying network node resource capacity are observable, the node to which each virtualized network function in the service chain is specifically mapped is not observable. Therefore, the virtualized network function mapping problem has hidden Markov property, and the virtualized network function mapping problem is constructed into a hidden Markov model to be solved. In a service chain, the state at any time t only depends on the state at the previous time, and is irrelevant to the states and observations at other times, so that the hidden Markov model established in the mapping process is a homogeneous Markov model. The specific modeling process is as follows:
defining node resource information and software and hardware requirements required by mapping in an underlying network as an observation sequence in a hidden Markov chain, and recording as follows:
wherein each one
The state of the ith node of the underlying physical network at the time t, including the residual storage resources, CPU resources, memory resources and the function type of the virtualized network supporting mapping, is represented and recorded as
The observation sequence is obtained by analyzing the utilization condition of the underlying network resources by the software defined network controller.
Defining a service path of a virtualized network function logic link at the time t as a state sequence of a hidden Markov chain, and recording the state sequence as:
St＝{ft(m1),ft(m2),…,ft(mn)}
wherein f ist(mi) Denotes the m-th time at tiThe mapped nodes of the class virtualization network function.
The state transition probability matrix is represented as:
A＝[aij]N×N
wherein a isij＝P(ft+1(mj)＝vj|ft(mi)＝vi) Indicating f in the virtualized network function at time tt(mi) Mapping a node to viAt time t +1 virtualizes f in the network functiont+1(mj) Mapping a node to vjIn order for the mapping process to satisfy the constraints of nodes and edges, aijSatisfies the constraint conditional expressions (1) to (4), and when one of the conditions is not satisfied, aij0. At the same time for load balancingThe larger the resource remaining capacity is, the larger the state transition probability is. Defining:
B＝[bj(k)]N×M
to observe a probability matrix, wherein bj(k)＝P(Ot＝Qk|ft(mi)＝vj) Denotes that time t is at time ft(mi) Mapping to node vjUnder the condition of (2), the resource status of the generation node is QkThe probability of (c). QkIs the discretization value of the residual resource state of each node of the bottom layer physical network.
And after the modeling is finished, analyzing the hidden Markov model through a Viterbi algorithm, calculating a mapping path meeting constraint conditions, and providing selection for calculating an optimal mapping path in subsequent dynamic steps. The Viterbi algorithm starts to introduce two variables of sigma and psi, wherein sigma represents the residual situation of the resources of the underlying physical network node in the observation sequence at the time t and is Ot＝QkUnder the condition of (1), the mapping path i with the maximum probability in the state sequence1,i2,…,it+1(ii) a And psi is used for recording the condition of the selected node in the node sequence of the mapping path with the highest probability at the current moment, and the last virtualized network function maps the selected node, so that the complete mapping path is traced back after the algorithm is finished. Therefore, the mapping path with the maximum probability at time t is:
recursion formula for variable σ:
the former virtualized network function mapping of the probabilistic maximum mapping path is:
the viterbi algorithm only finds an optimal solution with the highest probability, and a set of feasible path elements needs to be obtained in the dynamic step. Therefore, after the algorithm finishes one iteration to screen out the optimal solution, the iteration is continued to generate more suboptimal solutions. And (4) setting the iteration number as m, generating m feasible paths as alternative samples in the dynamic step, and storing the alternative samples calculated in the static step in P'.
B2, dynamic step:
in the satellite network, a matched model is made according to the service request and the network topology of the current time slice, and a mapping path with the highest score is matched from P' by adopting a graph model matching algorithm to complete the mapping and arrangement of the virtualized network function.
When the dynamic matching of the functions of the virtualized network of the satellite network is realized by the graph model matching algorithm, a matching model is firstly formulated according to the service requirements of the satellite, the topological state of the current satellite and the software hardware capacity of the current satellite network node and edge, and is recorded as M. And in the P' output in the static step, screening out a proper selectable path set according to the topology condition of the current network. And returning the candidate set which finally meets the condition, and recording the candidate set as G.
And then, scoring the mapping path in the G through graph simulation by adopting a connection-based method in graph model matching. And weighting the matching similarity and the mapping delay as a final score. And finally, selecting the optimal mapping path according to the scores to complete mapping.
Compared with the prior art, the invention has the following beneficial effects:
1. the software defined network and network function virtualization collaborative deployment framework designed by the invention can enable the network function to be decoupled in the hardware equipment, thereby improving the flexibility and survivability of the network.
2. The dynamic mapping method (VG-DPA algorithm) for the virtualized network function is divided into two steps, namely a static step and a dynamic step. In the static step, a hidden Markov chain model is established for the mapping problem in a pre-calculation mode, a feasible mapping path set in one operation period of the satellite network is calculated by adopting an improved Viterbi algorithm, and the feasible mapping paths are weighted and sequenced according to the length of mapping delay; in the dynamic step, a graph matching model is formulated according to the service requirement and the current topology of the network, an optimal mapping path is matched from feasible mapping paths with different weights in the time slice by adopting a graph model matching algorithm, and an arrangement strategy is formulated at the same time. The method combining static configuration and dynamic configuration can ensure that whether the paths in the feasible mapping path set can complete mapping or how to map is not required to be considered when the virtualized network function request is dynamically processed, and only the optimal mapping path is selected. Therefore, the time delay can be greatly reduced, and the requirement of high dynamic change of a satellite network topological structure is met.
Drawings
Fig. 1 is a diagram of a satellite network software defined network and network function virtualization co-deployment framework.
Fig. 2 is a diagram of a scenario to be implemented in a simulation experiment.
Figure 3 is a diagram of a hidden markov mapping model.
Detailed Description
The experimental simulation is to simulate a software-defined network and network function virtualization collaborative deployment framework based on a satellite network in a real environment, as shown in fig. 1. The experimental purpose is to realize the scenario pattern as shown in fig. 2, which is based on the software defined network and network function virtualization model framework designed by the present invention, USER 1(USER1) is at some point on the ground, and needs to transmit data to another ground station or USER 2(USER2), and the communication path thereof will be USER1-LEO1-MEO1-MEO 2-LEO-TSER 2. Before link reconfiguration, LEOs comprise LEO2, LEO4, LEO5 and LEO6, and then network functions such as a satellite gateway, a firewall, a performance enhancement agent, network address conversion, a virtual private network agent and the like are simulated on the LEOs in sequence. During the current time slice, the MEO2 transmits data to the USER2 or ground station via the four connected low-orbit satellites, which deploy the corresponding virtualized network functions. Due to the high dynamics of the satellite network topology and the exposure of wireless links, LEO2 may be out of coverage of MEO2 or the link may be disrupted from communicating with it during the next time slice. At the moment, the ground station makes a decision instruction according to the stored satellite orbit parameters and the information fed back by the GEO and sends the decision instruction to the GEO. The command is issued to the MEO2 in a flow table form, the MEO2 searches for an LEO3 within its own control range, and simultaneously, the LEO3 virtualizes a lost satellite gateway function quickly and establishes a functional service chain with the other three low-earth orbit satellites, so that normal communication of a satellite network is ensured.
In the invention, GEO represents a geostationary satellite, MEO represents a medium orbit satellite, and LEO represents a low orbit satellite.
The specific implementation is implemented by running on a PC configured as a 64-bit Ubuntu operating system, an Intel Core i7-4790 CPU @3.60GHz processor, and a 16GB memory, wherein the used programming language is Python3.6, and the programming platform is JetBrains Pycharm Community Edition 2017.2.4x 64.
The OpenSAND emulator is used to emulate the underlying physical network of the satellite network. It can simulate the communication process of a satellite network and can generate network functions such as gateways, firewalls, and the like. The satellite network topology adopts a GEO/MEO/LEO three-layer satellite network. The height of the GEO orbit is 35786km, 3 satellites are all positioned in a static orbit, and the longitude is 00, 1200 th longitude and 1200 th longitude respectively; the MEO orbit height is 12000km, 4 orbit surfaces are adopted, and a Walk Delta constellation is formed by 4 satellites on each orbit surface; LEO adopts a constellation similar to iridium, and has 66 satellites and 6 orbital planes. In order to establish a virtualization-capable NFV infrastructure, OpenStack is used as a bearer platform to deploy and manage the entire lifecycle of VNFs on virtual machines. Assuming that the service mapping requests obey a poisson distribution with a strength of [0,500], the number of VNFs included in each mapping request traffic is variable, but obeys a uniform distribution of [1,10 ]. The OpenDaylight network controller is used for controlling the network end to end, and Openflow and the like are used for formulating the strategy of each flow in the intermediate network node, so that the flow is guided to pass through the VNF deployed on the virtual machine and finally reach the destination node. The cost of the function mapping comes from the cost of function implementation and the cost of utilizing nodes and links in the underlying physical network. In order to minimize the mapping cost, in the static step, the feasible paths are pre-computed by the viterbi algorithm and sorted by time delay. And then matching an optimal path in a dynamic step by adopting a graph model in the feasible path.
Experiment in order to map the virtual network function to the underlying physical network, a hidden markov model (as shown in fig. 3) is established, a path to be mapped by each node is modeled as a state sequence, and then the state sequence is solved by a viterbi algorithm, and finally a mapping path is obtained.
Claims (1)
1. A virtual network function fast mapping method based on a satellite network is characterized in that: the method comprises the following steps:
A. software defined network and network function virtualization collaborative deployment framework based on satellite network
The software defined network and network function virtualization collaborative deployment framework based on the satellite network adopts a satellite network framework of three-layer flow scheduling, wherein the framework comprises an application plane, a control plane and a forwarding plane, a high-orbit satellite is used as the control plane of the framework, a medium-low orbit satellite is used as the forwarding plane of the framework, and a ground station is used as the application plane of the framework; virtualizing a virtualized network function on a low orbit satellite of a forwarding plane by adopting a network function virtualization technology;
a1, design application plane
The application plane comprises a ground station and other users; the ground station is responsible for resource management, network security and strategy formulation and is a coordinator of the whole network; the ground station establishes a strategy according to satellite state information collected by the geosynchronous orbit satellite and sends the strategy to the geosynchronous orbit satellite through a special channel; the geosynchronous orbit satellite controls the work of the medium orbit satellite and the low orbit satellite by issuing a flow table;
a2, design control plane
The high orbit satellite is used as a control plane; the high orbit satellite is used as a controller which is responsible for collecting the link condition information between the satellites and sending the link condition information to the ground station for processing; meanwhile, the command of the ground station is sent to the medium orbit satellite through the geosynchronous orbit satellite; when the instructions from the ground station reach the geosynchronous orbit satellite, the instructions are issued to the medium orbit satellite in a flow table mode;
a3, design Forwarding plane
The forwarding plane consists of a medium orbit satellite and a low orbit satellite and is dynamically configured by the controller; the medium orbit satellite only keeps the forwarding function; when data from a low orbit satellite or other medium orbit satellites reaches a certain medium orbit satellite, only a flow table is searched to find out matched forwarding information, and then a data packet is forwarded to a next satellite node; the low earth orbit satellite not only reserves partial forwarding function, but also adds virtualization function, the virtualization function includes virtual satellite gateway, firewall, performance enhancement agent, network address conversion and virtual private network agent;
B. dynamic mapping method for designing virtualized network function
The mapping problem of the virtualized network function needs to consider instantiating one or more virtualized network functions on physical resources on the basis of meeting the forwarding requirement under the satellite network topology of the current time slice; representing the mapping process as a graph PG (V, E); wherein V is a set of satellite nodes representing servers hosting virtualized network functions; e is an edge set and represents network connection between nodes; when the virtualized network function request is mapped to the bottom physical network, not only nodes meeting the requirement of accommodating the virtual function are screened in a traversing way, but also edges supporting data transmission among the virtualized network functions are searched; the virtualized network function configuration work is to initiate a path search for each virtualized network function request over the entire underlying physical network, with the worst case time complexity of O (| V |)2|E|)；
Therefore, the following conditional constraints must be satisfied to complete the virtualized network function mapping in the satellite network:
one network function cannot be deployed to multiple satellite nodes, and the formula is as follows:
where i is the ith virtual network function, and a (i, j) ═ 1 indicates that virtual network function i is mapped to node vjThe above step (1);
the satellite node bearing the mapping needs to meet the resource mapped by a certain type of virtual network function and has enough residual resources; meanwhile, the connecting edge between the nodes has enough residual resources, and the formula is as follows:
wherein, cfiRepresenting a node viA set of virtual network functions supporting the mapping; z is a radical ofmNode resource requirements for network function f (m) to join the service chain; c. CiIs a node viThe residual resource vector of (2); bmnBandwidth resource requirements between service chain functions f (m) and f (n); lijIs a node viAnd vjBandwidth surplus resources of the connection edge;
considering the limitation of data flow, the whole service chain flow is less than a certain constant, and the formula is as follows:
wherein, aiFlow through node v for service chainiThe sum of the flow rates of (a); i is the flow through node viData traffic limit of (1);
the topological structure of the satellite network is highly dynamically changed, but the period of the satellite network is discretized into n time slices by taking the operating periodicity of the satellite into consideration, and a two-dimensional matrix B is usedtNetwork topology connection situation representing the current time slice, Bt(i, j) ═ 1 denotes the node viAnd vjConnected by links, Bt(i, j) ═ 0 denotes the node viAnd vjWithout links connected, the formula is as follows:
presence of (i, j) such that Bt(i,j)＝1
T is one period of the satellite network; t istAs a satellite networkThe t-th time slice in a period;
mathematically, the network function configuration problem is expressed as an objective function satisfying the constraints of equations (1) - (4):
Φ(PG,s) (5)
where s is a set of service chains associated with the virtualized network function request, Φ (PG, s) represents an optimization function implemented by mapping service chains s over a given underlying physical network PG;
the objective function is that when mapping the virtualized network function to the underlying physical network, the total delay is the lowest, i.e. the following equation is satisfied:
Φ(PG,s)＝min∑s∈Sm(s) (6)
s in the above formula represents a virtualized network function service chain set, m (S) represents the mapping delay of a service chain S, and the problem solving complexity is NP-hard;
the method specifically comprises the following steps:
b1, static step
In the static step, modeling the process of mapping the functions of the virtualized network; because the context of the virtualized network function in the service chain, the mapping condition of the node virtualized network function and the resource capacity of the underlying network node are observable, the node to which each virtualized network function in the service chain is specifically mapped is not observable; therefore, the virtualized network function mapping problem has hidden Markov property, and the virtualized network function mapping problem is constructed into a hidden Markov model to be solved; in a service chain, the state of any time t only depends on the state of the previous time, and is irrelevant to the states and observations of other times, so that the hidden Markov model established in the mapping process is a homogeneous Markov model; the specific modeling process is as follows:
defining node resource information and software and hardware requirements required by mapping in an underlying network as an observation sequence in a hidden Markov chain, and recording as follows:
wherein each one
The state of the ith node of the underlying physical network at the time t, including the residual storage resources, CPU resources, memory resources and the function type of the virtualized network supporting mapping, is represented and recorded as
The observation sequence is obtained by analyzing the utilization condition of the underlying network resources by the software defined network controller;
will be provided withtThe service path of the time virtualization network function logical link is defined as a state sequence of a hidden Markov chain, and is recorded as:
St＝{ft(m1),ft(m2),…,ft(mn)}
wherein f ist(mi) Denotes the m-th time at tiA node to which the class virtualization network function is mapped;
the state transition probability matrix is represented as:
A＝[aij]N×N
wherein a isij＝P(ft+1(mj)＝vj|ft(mi)＝vi) Indicating f in the virtualized network function at time tt(mi) Mapping a node to viAt time t +1 virtualizes f in the network functiont+1(mj) Mapping a node to vjIn order for the mapping process to satisfy the constraints of nodes and edges, aijSatisfies the constraint conditional expressions (1) to (4), and when one of the conditions is not satisfied, aij0; meanwhile, for load balancing, the larger the resource residual capacity is, the larger the state transition probability is; defining:
B＝[bj(k)]N×M
to observe a probability matrix, wherein bj(k)＝P(Ot＝Qk|ft(mi)＝vj) Denotes that time t is at time ft(mi) Mapping to node vjUnder the condition of (2), the resource status of the generation node is QkThe probability of (d); qkThe value is the discretization of the residual resource condition of each node of the bottom physical network;
after modeling is completed, analyzing the hidden Markov model through a Viterbi algorithm, calculating a mapping path meeting constraint conditions, and providing selection for calculating an optimal mapping path in subsequent dynamic steps; the Viterbi algorithm starts to introduce two variables of sigma and psi, wherein sigma represents the residual situation of the resources of the underlying physical network node in the observation sequence at the time t and is Ot＝QkUnder the condition of (1), the mapping path i with the maximum probability in the state sequence1,i2,…,it+1(ii) a The psi is used for recording the condition of the selected node mapped by the last virtualized network function in the node sequence of the mapping path with the maximum probability at the current moment so as to trace back the complete mapping path after the algorithm is finished; therefore, the mapping path with the maximum probability at time t is:
recursion formula for variable σ:
the former virtualized network function mapping of the probabilistic maximum mapping path is:
the Viterbi algorithm only obtains an optimal solution with the maximum probability, and a feasible path element set needs to be obtained in the dynamic step; therefore, after the algorithm finishes one iteration to screen out the optimal solution, the iteration is continued to generate more suboptimal solutions; setting the iteration number as m, generating m feasible paths as alternative samples in the dynamic step, and storing the alternative samples calculated in the static step in P';
b2, dynamic step:
in a satellite network, a matched model is made according to a service request and the network topology of the current time slice, and a mapping path with the highest score is matched from P' by adopting a graph model matching algorithm to complete the mapping and arrangement of the virtualized network function;
when the dynamic matching of the functions of the virtualized network of the satellite network is realized by the graph model matching algorithm, firstly, a matching model is formulated according to the service requirements of the satellite, the topological state of the current satellite and the software hardware capacity of the current satellite network node and edge, and the model is marked as M; in P' output in the static step, screening out a proper selectable path set according to the topology condition of the current network; returning the candidate set which finally meets the conditions, and recording the candidate set as G;
then, scoring the mapping path in G through graph simulation by adopting a connection-based method in graph model matching; weighting the matching similarity and the mapping time delay to serve as final scores; and finally, selecting the optimal mapping path according to the scores to complete mapping.
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