CN115361284A - Deployment adjustment method of virtual network function based on SDN - Google Patents
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Abstract
The invention discloses a deployment adjusting method of a virtual network function based on an SDN (software defined network), which comprises the following steps: after receiving a request for deploying a virtual network function, determining an initial deployment scheme after the requested virtual network function is deployed in a network; based on an initial deployment scheme, aiming at SFGs formed by all SFCs in a network, traversing physical nodes and links in the SFGs, and determining overloaded physical nodes and links; and carrying out deployment adjustment on virtual network functions aiming at the overloaded physical nodes and links. The invention can be applied to globally balance the load from the network.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a deployment adjustment method of a virtual network function based on an SDN.
Background
Currently, NFV has become a promising technology to efficiently deploy and manage various network functions. In the NFV architecture, these network functions in software are handled as Virtual Network Functions (VNFs) and can be managed by the NFV MANOs. NFV may provide services in the form of end-to-end Service Function Chains (SFC), which define specific sequences of VNFs and their logical connections, which may be embedded in a physical network in a flexible way. NFV utilizes commercial servers using standard hardware, as opposed to traditional middleware that relies on dedicated hardware. One major benefit from this evolution is that network functions can scale flexibly according to user traffic. For example, when traffic is bursty, a group of servers may be configured to run the same VNF to process packets.
In a cloud edge collaborative network based on a Software Defined Network (SDN), due to changes in network traffic, SFCs need to adjust deployment policies by scaling in time, and a conventional SFC scaling method is often based on a single SFC in a user request, which cannot effectively save cost and balance load from the network overall.
That is, the existing SFC scaling methods focus on scaling at a single SFC and cannot efficiently globally optimize network performance. For a base network with SFC service deployed, when a traffic burst is faced, scaling of network functions is performed from the perspective of a single SFC, which cannot efficiently ensure the utilization rate of the whole network resource and balance the load among network devices. Meanwhile, in the existing part of research, although the peak value of the flow is predicted and some optimization strategies are proposed, no specific scaling SFC deployment scheme is proposed. In addition, in a network environment with cloud edge combination, the scaling problem of the comprehensive SDN cloud edge network structure characteristics is not considered.
Disclosure of Invention
In view of this, the present invention is directed to a method for adjusting deployment of virtual network functions based on an SDN, which may globally balance loads from a network.
Based on the above purpose, the present invention provides a deployment adjustment method for virtual network functions based on SDN, which includes:
after receiving a request for deploying a virtual network function, determining an initial deployment scheme after the requested virtual network function is deployed in a network;
based on an initial deployment scheme, aiming at SFGs formed by all SFCs in a network, traversing physical nodes and links in the SFGs, and determining overloaded physical nodes and links;
and carrying out deployment adjustment of virtual network functions aiming at the overloaded physical nodes and links.
The deployment adjustment of the virtual network function for the overloaded physical node and the overloaded link specifically includes:
respectively forming an overloaded physical node and an overloaded link into a node set and a link set;
carrying out deployment adjustment of virtual network functions on each physical node in the node set in sequence according to the load rate;
and carrying out deployment adjustment of virtual network functions on all links in the link set according to the load rate.
Preferably, the sequentially performing deployment adjustment of the virtual network function on each physical node in the node set according to the load rate specifically includes:
sorting all physical nodes in the node set from large load rate to small load rate; and sequentially deploying and adjusting the virtual network functions of the sorted physical nodes:
determining a precursor node and a successor node of an SFC to which a physical node belongs for the physical node to be deployed and adjusted currently;
taking the precursor node as an initial node and taking the successor node as a termination node; taking a path between the starting node and the terminating node as a path to be adjusted;
and selecting a path of the server node which is consistent with the type of the physical node and meets the requirement of the load rate from a plurality of paths between the starting node and the terminating node by using a tabu search algorithm, and adjusting and deploying the virtual network function of the physical node to the server node of the selected path.
Preferably, the sequentially performing deployment adjustment of the virtual network function on the physical links in the link set according to the load rate specifically includes:
sorting all physical links in the link set from large load rate to small load rate; and sequentially carrying out deployment adjustment of the virtual network function on the sequenced physical links:
carrying out deployment adjustment on the paths for m times for the physical link to be currently subjected to deployment adjustment; the deployment adjustment process of the kth path is as follows:
taking the 1 st physical node of the physical link as a starting node and the (k + 2) th physical node of the physical link as a terminating node; taking a path between the starting node and the terminating node as a path to be adjusted;
and selecting a path with a server node which is consistent with the type of the physical node and meets the requirement of the load rate from a plurality of paths between the starting node and the terminating node by using a tabu search algorithm, and adjusting and deploying the virtual network function of the (k + 1) th physical node to the server node of the selected path.
Preferably, the using tabu search algorithm selects a path having a server node that is consistent with the physical node type and meets the requirement for load rate from among the plurality of paths between the start node and the end node, and specifically includes:
based on the optimal comprehensive cost, selecting a path with a server node with the same type as the physical node from a plurality of paths between the starting node and the terminating node by using a tabu search algorithm as a candidate path;
aiming at each candidate path, calculating the comprehensive cost of the whole network after the virtual network function of the physical node is adjusted and deployed to the candidate path;
and finally adjusting and deploying the virtual network function of the physical node to the path with the minimum comprehensive cost of the network.
Preferably, the comprehensive cost specifically includes: load cost, traffic delay cost, and node turn-on cost.
Preferably, the network is a cloud edge collaborative network based on SDN.
The present invention also provides an electronic device comprising a central processing unit, a signal processing and storage unit, and a computer program stored on the signal processing and storage unit and executable on the central processing unit, wherein the central processing unit executes the deployment adjustment method for SDN-based virtual network functions as described above.
In the technical scheme of the invention, after a request for deploying the virtual network function is received, an initial deployment scheme after the requested virtual network function is deployed is determined in the SDN-based network; based on an initial deployment scheme, aiming at SFGs formed by all SFCs in the network, traversing physical nodes and links in the SFGs, and determining overloaded physical nodes and links; and carrying out deployment adjustment of virtual network functions aiming at the overloaded physical nodes and links. Therefore, the overloaded nodes and links in the network global can be redeployed and routed aiming at the SFG formed by combining all the deployed SFCs, and a global virtual network function deployment adjustment scheme is output; compared with the prior art of scaling network functions from the perspective of a single SFC, the technical scheme of the invention can efficiently ensure the utilization rate of the whole network resources and balance the load among network devices.
Further, the technical scheme of the invention also provides a comprehensive optimization and evaluation model of the scaling cost at the flow peak of the cloud edge collaborative network based on the SDN, and the comprehensive optimization and evaluation model is applied to the deployment and adjustment scheme of the virtual network function, so that the cost-load balance of the SFG can be realized, the comprehensive cost of the network after adjustment and deployment can be optimized, the service quality of the SFC can be ensured at the flow peak of the SDN network, and the load can be globally balanced from the network.
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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 diagram of a cloud edge collaborative network architecture based on an SDN according to an embodiment of the present invention;
fig. 2 is a flowchart of a deployment adjustment method for a virtual network function based on an SDN according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for sequentially performing deployment adjustment of virtual network functions on physical nodes according to an embodiment of the present invention;
fig. 4 is a flowchart of a specific method for performing deployment adjustment of a virtual network function on a current physical node according to an embodiment of the present invention;
fig. 5 is a flowchart of a method for sequentially performing deployment adjustment of virtual network functions on a physical link according to an embodiment of the present invention;
fig. 6 is a flowchart of a specific method for deployment adjustment of a primary path according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of an example of SFG deployment at peak traffic according to an embodiment of the present invention;
fig. 8, 9 and 10 are schematic diagrams illustrating comparison of experimental results of various deployment adjustment algorithms according to an embodiment of the present invention;
fig. 11 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
It is to be noted that technical terms or scientific terms used in the embodiments of the present invention should have the ordinary meanings as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
In order to solve the above problems, the inventor of the present invention provides a global SFG scaling method in a scenario of multiple SFCs, which considers an SFG composed of all SFCs deployed in a network, redeployes and routes overloaded nodes and links in the global network, and outputs a scaling scheme of the SFG, that is, a global deployment adjustment scheme of a virtual network function.
Preferably, the technical scheme of the invention further provides a scaling cost comprehensive optimization evaluation model for the SDN cloud edge cooperative network flow peak, and the scaling cost comprehensive optimization evaluation model is applied to the scaling scheme of the global SFG, so that a cost-load balancing scaling deployment algorithm of the SFG can be realized, the comprehensive cost of the network after the adjustment and deployment is optimized, the SDN network can guarantee the service quality of the SFC at the flow peak, and the load is globally balanced from the network.
The technical solution of the embodiments of the present invention is described in detail below with reference to the accompanying drawings.
An SDN-based cloud edge collaborative network architecture is shown in fig. 1 and includes a cloud layer, an edge layer, and a terminal layer. The Cloud Layer (Cloud Layer) comprises a Cloud Server node and a Router node, the Edge Layer (Edge Layer) comprises an Edge Server node and a Router node, and the Terminal Layer (Terminal Layer) comprises an access of an IoT device and a user Terminal. The user request generated by the terminal layer and the cloud-edge layer network basic information are used as the input of the NFV management orchestrator, a plurality of requests can form an SFG shared based on an example, and the NFV MANO outputs the deployment strategy of the corresponding SFG. In this way, the deployment of the network service can be completed by allocating corresponding computing and communication resources to the network service.
In the technical scheme of the invention, a network model, an SFG model, a flow change evaluation model, a time delay model and an optimization problem model in an SFC scene are established.
The network model reflects the information of the physical network and is defined as follows:
defining a base network of a cloud edge collaborative network based on SDN as an undirected graph G (N, E), N being a set of physical nodes, N = { N = { N = SV ,N RT }＝{n 1 ,n 2 ,...,n |N| E is the physical link set, E = { E = { E } 1 ,e 2 ,...,e |E| }。
Wherein N is SV ＝{Ν E ,N C },Ν E For edge server nodesCollection, N C A set of cloud server nodes.
N is the ith physical node N i Available tuples
Is shown in which
Is the total computational resource of the physical node,
indicating the remaining computational resources, pr, of the node i Indicating the capability of the node to process data packets, act i Indicating the turn-on cost for enabling the current node. type i Indicating the type of the node.
When n is i ∈N RT When being a routing node, type i =0, meaning that the routing node has no server,
pr i =0, and therefore does not carry VNF.
When n is i Type when edge server node i ＝1，n i ∈N E ∈N SV 。
When n is i Type when cloud server node is present i ＝2，n i ∈N C ∈N SV 。
Compared with the edge server node, the cloud server node has more computing resources and data processing capacity.
E＝{E EE ,E EC ,E CC Denotes the set of links between nodes of the entire network, E EE Representing a set of links between edge server nodes, E EC Representing a set of links between a cloud server node and an edge server node, E CC Representing a set of links between cloud server nodes.
Tuple for each physical link
Is shown in which
Representing the total bandwidth resources of the physical link,
representing the remaining bandwidth resources of the link, d j Represents a link e j The propagation delay of (2). e.g. of the type j ∈E EE When the link is between the edge nodes, the link bandwidth is small, and the propagation delay is small; e.g. of a cylinder j ∈E EC When the cloud server node is connected with the edge node, the link is connected with the cloud server node, and the link has larger bandwidth and larger propagation delay; e.g. of a cylinder j ∈E CC In time, the link is connected with different cloud server nodes, and the link has large bandwidth and small propagation delay. Note, e j Can be written as
Wherein i 1 ,i 2 Represents a link e j Sequence numbers of two physical nodes connected. In the same way, the method for preparing the composite material,
in combination with the network model, two variables nlr and elr are defined to describe the load rates of the physical nodes and the physical links, respectively, as shown in equations 1 and 2, so as to evaluate the load condition of the whole network.
Further, the load ratio of the whole network is shown in formula 3:
the SFG model reflects the information of the virtual network, defined as follows:
describing SFC traffic as a directed acyclic graph, G V (N V ,E V Snum, RD), the graph consists of several SFCs.
Is a set of virtual nodes, representing network functional components,
the virtual link set represents a service traffic path, and the snum represents the number of SFCs contained in the SFG. Set RD = { RD = 1 ,rd 2 ,...,rd snum Record G V Maximum tolerated delay per SFC. Similarly, the s-th SFCs can be expressed as
Ith virtual node by tuple
Description of wherein v i A VNF type representing the virtual node,
representing the computational resources required by the virtual node, P i Indicating the size of the packet to be processed by the virtual node,
represent the virtual node
The set of SFCs to which it belongs.
Jth virtual link route tuple
Therein is described
Respectively represent
A source virtual node and a target virtual node.
To represent
The bandwidth resources that are required for the communication,
indicating the flow through
The set of SFCs of (1). Similar to a physical link, a virtual link may also be represented by nodes at both ends thereof, and thus
In addition, two binary variables are defined
The mapping of the virtual network to the physical network is described.
Representing virtual nodes
Deployed at a physical node n i' The above step (1);
representing virtual links
Deployed on physical link e j' The above. Assume a 1-to-1 mapping of virtual nodes to server nodesThe virtual link to physical link is an m to n mapping.
Combining SFC information corresponding to nodes and links
And deployment information
Deployment information for each SFC may be obtained. The SFCs are deployed by a physical node set
Set of physical links as
To pair
The following equation 4 is satisfied:
the packet size sequence is shown in equation 5:
routing node does not handle VNF, N s The size of the data packet carried by the physical node in (1) is shown in equation 6:
the traffic change estimation model may reflect load information of traffic peaks, defined as follows:
when defining the peak value of the flow, the virtual node
Is requested as
Virtual link
The bandwidth resource request is
Is provided with
Then the virtual node
The flow rate variation of (d) is shown in equation 7:
virtual link
The flow rate variation of (c) is shown in equation 8:
when in use
In time, the currently deployed server has the capacity of bearing a new VNF instance required by burst traffic, vertical scaling of the VNF instance is selected, otherwise, only horizontal scaling can be performed, and n is deployed at a new server node 1 Instantiates and allocates a path. In the same way, the method for preparing the composite material,
and if not, carrying out horizontal scaling on the route.
At the peak of the traffic, the node load rate is as shown in equation 9:
at the peak of the traffic, the link load rate is as shown in equation 10:
at the time of the peak of the traffic, the load rate of the whole network is as shown in formula 11:
wherein,
when the flow peak value is expressed, the node load rate of the whole network is represented;
when the flow peak value is expressed, the link load rate of the whole network is represented; | N SV L represents the number of server nodes of the entire network; | E | represents the number of physical links of the entire network.
The total cost for starting the servers with deployed virtual nodes, i.e. deployed virtual network functions, in the network is shown in formula 12:
wherein act c i Represents the turn-on cost of the ith server,
representing virtual nodes
Article withPhysical node n i The mapping relationship of (a) to (b),
to represent
Deployed in physical node n i ；
Represent
Not deployed in physical node n i ；
The delay model is defined as follows:
the end-to-end delay of the SFC includes three components, propagation delay, transmission delay, queuing and processing delay.
1. Propagation delay: in relation to the length of the physical link, denoted by d j And (6) determining. The sum of the propagation delays of the SFCs is shown in equation 13:
2. transmission delay: the transmission delay of SFCs refers to the transmission time of the data packet from the server to the output link, and is related to the size of the data packet and the requested bandwidth, and the calculation formula is shown in equation 14:
3. queuing and processing delay: the processing delay of the VNF of SFCs is related to the packet size, the node processing capacity and the requested computing resources, and can be calculated according to equation 15:
wherein the virtual node
Deployed at physical nodes
The above.
The queuing delay of the VNF is equal to the waiting time after the arrival of the pending packet. Modeling a server of an access point as an M/M/1 queue to reach the server
The task of performing the calculation follows the poisson process, with an arrival rate of λ. Thus the server
Average latency to complete a VNF
(including queuing and service times) can be expressed as shown in equation 16:
wherein,
therefore, the queuing and processing delays of SFCs can be expressed as equation 17:
in summary, the total delay of the SFCs is calculated as equation 18:
delay s ＝ppd s +trd s +qpd s (formula 18)
The optimization problem model is defined as follows:
the optimization objective here is to minimize the combined cost of load cost, traffic latency cost and node turn-on cost in the network after deployment adjustment (SFC scaling), which is expressed as equation 19:
wherein, ω is 1 ，ω 2 ，ω 3 Is a custom parameter, ω 1 +ω 2 +ω 3 ＝1。
When the flow is at the peak value, the load rate of the whole network is increased; rd for s Representing the maximum tolerated delay of the SFCs;
representing the delay rate of the SFCs;
represents the sum of the delay rates of all SFCs; snum represents the number of SFCs contained in the SFG; actc represents the total startup cost of the servers with the deployed virtual network functions in the network;
representing a total cost of opening of all servers in the network; ρ is an acceptance rate penalty value, and is caused by traffic non-deployment due to a traffic peak value, and can be calculated according to the following formula 20:
wherein, total _ SFC represents the number of all SFCs, and accepted _ SFC represents the number of successfully deployed SFCs.
In order to solve the scaling problem of VNF instances, i.e. the deployment adjustment problem of virtual network functions, some constraints need to be met. First, on any one physical node or physical link, the sum of the resources required by the deployed virtual node or link does not exceed its total resource, as shown in equations 21 and 22:
then, the mapping of the server node and the virtual node is 1 to 1, as shown in equations 23, 24:
in addition, the delay of all SFCs cannot exceed the maximum tolerated delay, as shown in equation 25:
in summary, the optimization problem model herein can be summarized as:
s.t.:
the invention provides a deployment adjustment method of a virtual network function based on an SDN, the flow is shown in figure 2, and the method comprises the following steps:
step S201: after receiving a request for deploying a virtual network function, determining an initial deployment scheme after the requested virtual network function is deployed in the network.
Specifically, after a request for deploying a virtual network function sent by user equipment is received, information of a source node and a plurality of virtual network functions to be deployed is determined from the request; and determining a deployment scheme after the multiple virtual network functions of the request are deployed in the SDN-based network as an initial deployment scheme by adopting the conventional method.
Step S202: and based on the initial deployment scheme, aiming at the SFG consisting of all the SFCs in the network, traversing the physical nodes and the physical links in the SFG, and determining the overloaded physical nodes and physical links.
Specifically, based on an initial deployment scheme, traversing physical nodes and physical links in an SFG composed of all SFCs in a network, and determining overloaded physical nodes and physical links;
for example, a physical node with a load rate exceeding a node load degree threshold value alpha is determined as an overloaded physical node; and determining the physical link with the load rate exceeding the link load degree threshold value beta as the overloaded physical link. Where α and β can be set empirically by a person skilled in the art, such as setting α = β =0.7.
And forming the overloaded physical nodes into a node set, and forming the overloaded physical links into a link set.
Step S203: and carrying out deployment adjustment of the virtual network function aiming at the overloaded physical nodes and physical links.
In the step, the deployment adjustment of the virtual network function is carried out on each physical node in the node set according to the load rate; and carrying out deployment adjustment of virtual network functions on each physical link in the link set according to the load rate.
Specifically, the process of the method for sequentially performing deployment adjustment of the virtual network function on each physical node in the node set according to the load factor is shown in fig. 3, and includes the following steps:
step S301: sorting all physical nodes in the node set from large to small according to load rates;
step S302: and sequentially carrying out deployment adjustment on the virtual network functions on the sequenced physical nodes.
In the step, the sorted physical nodes are sequentially subjected to deployment adjustment of virtual network functions according to the sequence of the load rate from large to small; as shown in fig. 4, a specific method for performing deployment adjustment of a virtual network function on a physical node to be currently subjected to deployment adjustment includes the following sub-steps:
substep S401: determining a precursor node and a successor node of the SFC to which the physical node belongs;
substep S402: taking the precursor node as an initial node and taking the successor node as a termination node; taking a path between the starting node and the terminating node as a path to be adjusted;
substep S403: and selecting a path of the server node which is consistent with the type of the physical node and meets the requirement of the load rate from a plurality of paths between the starting node and the terminating node by using a tabu search algorithm, and adjusting and deploying the virtual network function of the physical node to the server node of the selected path.
In this sub-step, a more preferred embodiment may be employed in selecting the path:
based on the optimal comprehensive cost, selecting a path with a server node with the same type as the physical node from a plurality of paths between the starting node and the terminating node by using a tabu search algorithm as a candidate path; aiming at each candidate path, calculating the comprehensive cost of the whole network after the virtual network function of the physical node is adjusted and deployed to the candidate path; and finally adjusting and deploying the virtual network function of the physical node to the path with the minimum comprehensive cost of the network.
The comprehensive cost of the network may include load cost, service delay cost and node start cost in the network;
in the case that the network is specifically the cloud edge collaborative network based on the SDN, the comprehensive cost of the network may be calculated according to the formula 19; that is, the comprehensive cost of the network under the current deployment condition is calculated according to the information of the base network of the network (i.e., the information of the physical network), the information of the SFG composed of all the SFCs in the network (i.e., the information of the virtual network), the mapping information from the virtual network to the physical network, the load information of the traffic peak, the time delays of all the SFCs in the network, and the starting cost of the server.
Specifically, the process of the method for sequentially performing deployment adjustment of the virtual network function on each physical link in the link set according to the load factor is shown in fig. 5, and includes the following steps:
step S501: sorting all physical links in the link set from large to small according to load rates;
step S502: and sequentially carrying out deployment adjustment on the virtual network functions on the sequenced physical links.
In the step, the sorted physical links are sequentially subjected to deployment adjustment of virtual network functions according to the sequence of the load rates from large to small;
carrying out deployment adjustment on the paths for m times for the physical link to be currently subjected to deployment adjustment; wherein m = L-2, L is the total number of physical nodes in the physical link; the deployment adjustment process of the kth path, as shown in fig. 6, includes the following sub-steps:
substep S601: taking the 1 st physical node of the link as a starting node and the (k + 2) th physical node of the link as a terminating node;
substep S602: taking a path between the starting node and the terminating node as a path to be adjusted;
substep S603: and selecting a path with a server node which is consistent with the type of the physical node and has a load rate meeting the requirement from a plurality of paths between the starting node and the terminating node by using a tabu search algorithm, and adjusting and deploying the virtual network function of the (k + 1) th physical node to the server node of the selected path.
In this sub-step, a more preferred embodiment may be employed in selecting the path:
based on the optimal comprehensive cost, selecting a path with a server node with the same type as the physical node from a plurality of paths between the starting node and the terminating node by using a tabu search algorithm as a candidate path; aiming at each candidate path, calculating the comprehensive cost of the whole network after the virtual network function of the physical node is adjusted and deployed to the candidate path; and finally adjusting and deploying the virtual network function of the physical node to a path with the minimum comprehensive cost of the network.
The comprehensive cost of the network may include load cost, service delay cost and node start cost in the network;
in the case that the network is specifically the cloud edge collaborative network based on the SDN, the comprehensive cost of the network may be calculated according to the formula 19; that is to say, the comprehensive cost of the network under the current deployment condition is calculated according to the information of the base network of the network (i.e. the information of the physical network), the information of the SFGs formed by all the SFCs in the network (i.e. the information of the virtual network), the mapping information from the virtual network to the physical network, the load information of the traffic peak, the time delays of all the SFCs in the network, and the starting cost of the server.
For example, as shown in FIG. 7, an example of SFG deployment at flow peak is illustrated. Shown in the upper part of figure 7 is the SFG deployment case without deployment adjustment (horizontal scaling),let α =0.7, when the overloaded server node comprises an edge server n 1 And a cloud server n 3 ,n 7 The overloaded physical links are part of the links that route the servers. In order to balance the load of the network, n is used for carrying out deployment adjustment 1 ,n 3 ,n 7 And migrating part of the loaded instance to a new server node deployment. Specifically, n is 1 Two instances of VNF1 of a bearer are deployed to an edge server n 8 N is to be 3 Deploying borne 1 VNF3 instance to cloud server n 10 N is to be 7 Two hosted VNF6 instances deployed to cloud server n 9 . And simultaneously, adjusting the link deployment related to the nodes. In this way, the overloaded node and link resources are partially released and the local overload condition of the network is alleviated.
In order to verify the technical effect of the technical scheme of the invention, a network topology consisting of 40 physical nodes is used, wherein 20 are cloud servers, 10 are edge servers, and 10 router nodes simulate a CEC network architecture. The nodes in the network have 204 physical links connected. In our simulation, SFC requests will be generated based on a certain arrival frequency, λ =4 at the peak of the flow. Further, we assume that each SFC is composed of 2 to 5 different VNFs, the required bandwidth of each VNF is set to 700 to 900Mbps, and the delay limit of each VNF is set to 100ms to 200ms. For the algorithm of the disposition based on the tabu search, the size of a tabu table is 20, the iteration number is 50, and epsilon =0.3 of an epsilon-greedy method is set. For the optimization goal herein, ω 1 ＝0.7，ω 2 ＝0.2，ω 3 =0.1 is the default configuration. Node and link loading threshold α = β =0.7.
The algorithm of the technical scheme of the invention is SFG-Scaling, which is a cost-load balancing Scaling deployment algorithm based on SFG;
the algorithm SFC-Scaling in the prior art is a Scaling deployment algorithm facing cost and load, which is considered according to a single SFC at a time.
An algorithm SFC-LEB in the prior art is a load and energy consumption-oriented balanced deployment algorithm, and aims to reduce changes of network topology caused by scaling when the flow is at a peak while optimizing the load.
An algorithm SFC-DA in the prior art is an SFC deployment algorithm for time delay perception, and unbalanced load is generated when the flow is at a peak value.
The performance of the algorithms is verified by comparing the SFC acceptance rate, the service delay and the comprehensive scaling cost.
(1) The acceptance rate of SFCs is defined as the number of successfully deployed SFCs divided by the total number of SFCs requested by the user. As shown in fig. 8, the algorithm proposed herein can guarantee a higher acceptance rate when the amount of traffic is larger. The SFC-Scaling and SFC-LEB balance the load of the network to ensure the smooth deployment of the subsequent services, so that the acceptance rate of the SFC-Scaling and the SFC-LEB is still over 70 percent although the acceptance rate is reduced along with the increase of the number of the services. The SFC-LEB does not consider the optimization of time delay, and the SFC which fails to be deployed due to service timeout can be deployed, so the acceptance rate is lower than that of SFC-Scaling. The SFC-DA does not scale the deployment situation when the flow is peak, a large amount of services fail to be deployed due to insufficient resources of a single server node or a single link, and the acceptance rate of the SFC is the worst.
(2) The delay of the service is defined in the delay model, and the result is shown in fig. 9. In terms of service latency, SFC-Scaling performs best, and tends to deploy each SFC on a path with lower latency than if SFG-Scaling was optimized for multiple SFCs together. Meanwhile, the SFG-Scaling algorithm presented herein performs the second best. The SFC-DA is a deployment algorithm facing to delay optimization, and a critical path with the shortest delay is occupied in the early stage, so that the delay is low. However, the SFC-DA algorithm does not scale the SFC, so as the traffic increases, nodes and links on the critical path cannot carry more SFCs, and therefore deployment needs to be completed through a farther path, which results in a large amount of time delay. The SFC-LEB algorithm does not focus on the latency behaviour of the traffic and therefore the latency is the highest.
(3) The comprehensive scaling cost is defined as a weighted calculation value of the network load degree, the service delay, the node starting cost and the acceptance rate, and is used as an optimization target of the text. As can be seen from fig. 10, the algorithm proposed herein has the lowest average integrated deployment cost. The algorithm presented herein is directed to multiple SFGs for a request, and performs better in terms of network load rate when compared to a single SFC scaling algorithm. Compared with the SFC deployment algorithm with load-energy consumption balance, the SFC-Scaling algorithm has more consideration on time delay cost, so that the comprehensive cost is lower. The time delay-aware SFC deployment algorithm cannot alleviate the local overload during the peak period of the traffic, so the deployment cost is high. As time increases, the overall deployment cost increases gradually because no node can carry more VNFs.
Fig. 11 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute a related program to implement the method for adjusting the deployment of the virtual network function provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static Memory device, a dynamic Memory device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called by the processor 1010 for execution.
The input/output interface 1030 is used for connecting an input/output module, and can be connected with a nonlinear receiver to receive information from the nonlinear receiver so as to realize information input and output. The i/o module may be configured as a component within the device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and the output devices may include a display, speaker, vibrator, indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
In the technical scheme of the invention, after receiving a request for deploying a virtual network function, an initial deployment scheme after the requested virtual network function is deployed in a network is determined; based on an initial deployment scheme, aiming at SFGs formed by all SFCs in a network, traversing physical nodes and links in the SFGs, and determining overloaded physical nodes and links; and carrying out deployment adjustment of virtual network functions aiming at the overloaded physical nodes and links. Therefore, the overloaded nodes and links in the network global can be redeployed and routed aiming at the SFG formed by combining all the deployed SFCs, and a global virtual network function deployment adjustment scheme is output; compared with the prior art of scaling network functions from the perspective of a single SFC, the technical scheme of the invention can efficiently ensure the utilization rate of the whole network resources and balance the load among network devices.
Further, the technical scheme of the invention also provides a comprehensive optimization and evaluation model of the scaling cost at the flow peak of the cloud edge collaborative network based on the SDN, and the comprehensive optimization and evaluation model is applied to the deployment and adjustment scheme of the virtual network function, so that the cost-load balance of the SFG can be realized, the comprehensive cost of the network after adjustment and deployment can be optimized, the service quality of the SFC can be ensured at the flow peak of the SDN network, and the load can be globally balanced from the network.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
In addition, well known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures for simplicity of illustration and discussion, and so as not to obscure the invention. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the present invention is to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures, such as Dynamic RAM (DRAM), may use the discussed embodiments.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalents, improvements, and the like that may be made without departing from the spirit or scope of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A deployment adjustment method for virtual network functions is characterized by comprising the following steps:
after a request for deploying a virtual network function is received, determining an initial deployment scheme after the requested virtual network function is deployed in the SDN-based network;
based on an initial deployment scheme, aiming at SFGs formed by all SFCs in the network, traversing physical nodes and links in the SFGs, and determining overloaded physical nodes and links;
and carrying out deployment adjustment on virtual network functions aiming at the overloaded physical nodes and links.
2. The method according to claim 1, wherein the performing deployment adjustment of virtual network functions for overloaded physical nodes and links specifically comprises:
respectively forming overloaded physical nodes and links into a node set and a link set;
carrying out deployment adjustment of virtual network functions on all physical nodes in the node set in sequence according to the load rate;
and carrying out deployment adjustment of virtual network functions on all links in the link set according to the load rate.
3. The method according to claim 2, wherein the sequentially performing deployment adjustment of the virtual network function on the physical nodes in the node set according to the load factor specifically includes:
sorting all physical nodes in the node set from large load rate to small load rate; and sequentially carrying out deployment adjustment of the virtual network functions on the sequenced physical nodes:
for a physical node to be deployed and adjusted currently, determining a precursor node and a subsequent node of an SFC to which the physical node belongs;
taking the precursor node as an initial node and taking the successor node as a termination node; taking a path between the starting node and the terminating node as a path to be adjusted;
and selecting a path of the server node which is consistent with the type of the physical node and meets the requirement of the load rate from a plurality of paths between the starting node and the terminating node by using a tabu search algorithm, and adjusting and deploying the virtual network function of the physical node to the server node of the selected path.
4. The method according to claim 2, wherein the sequentially performing deployment adjustment of the virtual network function on the physical links in the link set according to the load factor specifically includes:
sorting all physical links in the link set from large to small according to load rates; and sequentially carrying out deployment adjustment of the virtual network function on the sequenced physical links:
carrying out deployment adjustment on the paths for m times for the physical link to be currently subjected to deployment adjustment; wherein m = L-2, L is the total number of physical nodes in the physical link; the deployment adjustment process of the k path is as follows:
taking the 1 st physical node of the physical link as an initial node and taking the (k + 2) th physical node of the physical link as a termination node; taking a path between the starting node and the terminating node as a path to be adjusted;
and selecting a path with a server node which is consistent with the type of the physical node and meets the requirement of the load rate from a plurality of paths between the starting node and the terminating node by using a tabu search algorithm, and adjusting and deploying the virtual network function of the (k + 1) th physical node to the server node of the selected path.
5. The method according to claim 3 or 4, wherein the using tabu search algorithm selects a path having a server node with a load rate meeting requirements and the type of the physical node from a plurality of paths between the start node and the end node, and specifically comprises:
based on the optimal comprehensive cost, selecting a path with a server node with the same type as the physical node from a plurality of paths between the starting node and the terminating node by using a tabu search algorithm as a candidate path;
aiming at each candidate path, calculating the comprehensive cost of the whole network after the virtual network function of the physical node is adjusted and deployed to the candidate path;
and finally adjusting and deploying the virtual network function of the physical node to the path with the minimum comprehensive cost of the network.
6. The method according to claim 5, wherein the composite cost specifically comprises: load cost, traffic delay cost, and node turn-on cost.
7. Method according to claim 6, wherein the network is in particular a SDN based cloud edge collaborative network.
8. The method of claim 7, wherein the overall cost is calculated as shown in equation 19:
wherein, ω is 1 ，ω 2 ，ω 3 Is a custom parameter, ω 1 +ω 2 +ω 3 =1, ρ is the acceptance rate penalty value;
when the flow is at the peak value, the load rate of the whole network is increased;
represents the sum of the delay rates of all SFCs; snum represents the number of SFCs contained in the SFG; act c represents a total startup cost of servers in the network that have deployed virtual network functions;
representing the total cost of switching on all servers in the network.
9. The method of claim 8, wherein the peak traffic flow rate is the load rate of the entire network
Specifically, as calculated by equation 11:
wherein,
when the flow peak value is expressed, the node load rate of the whole network is represented;
when the flow peak value is expressed, the link load rate of the whole network is represented; | N SV L represents the number of server nodes of the entire network; | E | represents the number of physical links of the entire network.
10. An electronic device comprising a central processing unit, a signal processing and storage unit, and a computer program stored on the signal processing and storage unit and executable on the central processing unit, characterized in that the central processing unit implements the method according to any of claims 1-9 when executing the program.
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| US20200099625A1 (en) * | 2018-09-24 | 2020-03-26 | Netsia, Inc. | Path determination method and system for delay-optimized service function chaining |
| CN112738820A (en) * | 2020-12-22 | 2021-04-30 | 国网北京市电力公司 | Dynamic deployment method and device of service function chain and computer equipment |
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