CN115361284B - Deployment adjustment method of virtual network function based on SDN - Google Patents

Deployment adjustment method of virtual network function based on SDN Download PDF

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Publication number
CN115361284B
CN115361284B CN202210716111.7A CN202210716111A CN115361284B CN 115361284 B CN115361284 B CN 115361284B CN 202210716111 A CN202210716111 A CN 202210716111A CN 115361284 B CN115361284 B CN 115361284B
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node
physical
network
virtual network
path
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CN115361284A (en
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王洋
柳瑞春
李雨泰
陈紫儿
宋桂林
雷学义
王炫中
张亚南
龚爽
欧清海
宋继高
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State Grid Information and Telecommunication Co Ltd
Beijing Zhongdian Feihua Communication Co Ltd
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State Grid Information and Telecommunication Co Ltd
Beijing Zhongdian Feihua Communication Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0813—Configuration setting characterised by the conditions triggering a change of settings
    • H04L41/0816—Configuration setting characterised by the conditions triggering a change of settings the condition being an adaptation, e.g. in response to network events
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0806—Configuration setting for initial configuration or provisioning, e.g. plug-and-play
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H04L41/0826—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability for reduction of network costs
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0893—Assignment of logical groups to network elements
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate

Abstract

The invention discloses a deployment adjustment method of virtual network functions based on SDN, which comprises the following steps: after receiving a request for deploying the virtual network function, determining an initial deployment scheme after deploying the requested virtual network function in the network; based on an initial deployment scheme, traversing physical nodes and links in SFGs formed by all SFCs in a network, and determining overload physical nodes and links; and performing deployment adjustment of the virtual network function aiming at the overload physical nodes and links. The invention can globally balance the load from the network.

Description

Deployment adjustment method of virtual network function based on SDN
Technical Field
The invention relates to the technical field of computers, in particular to a deployment adjustment method of virtual network functions based on SDN.
Background
NFV has become a promising technology to efficiently deploy and manage various network functions. In NFV architecture, these network functions in the form of software are handled as Virtual Network Functions (VNFs) and can be managed by NFV MANO. NFV may provide services in the form of an end-to-end Service Function Chain (SFC), which defines a specific sequence of VNFs and their logical connections, which may be embedded in a physical network in a flexible manner. NFV utilizes commodity servers using standard hardware, as opposed to traditional middleware that relies on dedicated hardware. One of the main benefits of this evolution is that the network functions can be flexible according to the user traffic. For example, when traffic bursts, a group of servers may be configured to run the same VNF to process data packets.
In a cloud edge collaborative network based on a Software Defined Network (SDN), due to the change of network traffic, SFCs need to adjust deployment policies by timely scaling, and conventional SFC scaling methods are often based on a single SFC in a user request, so that cost can not be effectively saved and load can not be balanced from the network overall situation.
That is, existing SFC scaling methods focus on scaling at a single SFC and do not effectively optimize network performance from the global perspective. For the base network where the SFC service is deployed, when the traffic bursts, the network function scaling is performed from the perspective of a single SFC, so that the utilization rate of the whole network resource cannot be effectively ensured, and the load between network devices is balanced. Meanwhile, existing partial researches predict the peak value of the flow and propose some optimization strategies, but do not propose a specific scaled SFC deployment scheme. In addition, in the network environment of cloud-edge combination, the scaling problem of integrating network structural characteristics of SDN cloud-edge is not considered.
Disclosure of Invention
In view of the above, the present invention aims to provide a deployment adjustment method for virtual network functions based on SDN, which can globally balance loads from a network.
Based on the above object, the present invention provides a deployment adjustment method of virtual network functions based on SDN, including:
after receiving a request for deploying the virtual network function, determining an initial deployment scheme after deploying the requested virtual network function in the network;
based on an initial deployment scheme, traversing physical nodes and links in SFGs formed by all SFCs in a network, and determining overload physical nodes and links;
and performing deployment adjustment of the virtual network function aiming at the overload physical nodes and links.
The deployment adjustment of the virtual network function is performed on the overload physical node and the overload physical link, which specifically comprises the following steps:
respectively forming an overload physical node and a link into a node set and a link set;
sequentially performing deployment adjustment of virtual network functions on each physical node in the node set according to the load rate;
and sequentially performing deployment adjustment of virtual network functions on each link in the link set according to the load rate.
Preferably, the deploying adjustment of the virtual network function is sequentially performed on each physical node in the node set according to the load rate, which specifically includes:
ordering all physical nodes in the node set according to the load rate from big to small; sequentially performing deployment adjustment of virtual network functions on the ordered physical nodes:
for a physical node to be deployed and adjusted currently, determining a precursor node and a subsequent node of SFC to which the physical node belongs;
the precursor node is used as a starting node, and the subsequent node is used as a termination node; taking a path between the starting node and the ending node as a path to be adjusted;
and selecting a path with a server node which is consistent with the physical node type and meets the load rate requirement from a plurality of paths between the starting node and the ending 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 deploying and adjusting of the virtual network function are sequentially performed on each physical link in the link set according to the load rate, which specifically includes:
ordering all physical links in the link set according to the load rate from big to small; sequentially performing deployment adjustment of virtual network functions on the ordered physical links:
for the physical link to be deployed and adjusted currently, deploying and adjusting m paths; the deployment adjustment process of the kth path is as follows:
taking the 1 st physical node of the physical link as an initial node and the (k+2) th physical node of the physical link as a termination node; taking a path between the starting node and the ending node as a path to be adjusted;
and selecting a path with a server node which is consistent with the physical node type and meets the load rate requirement from a plurality of paths between the starting node and the ending 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 usage tabu search algorithm selects a path having a server node consistent with the physical node type and the load rate meeting the requirement from a plurality of paths between the start node and the end node, and specifically includes:
selecting a path with a server node consistent with the physical node type from a plurality of paths between the starting node and the ending node as a candidate path by using a tabu search algorithm based on the optimal comprehensive cost;
for 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;
the virtual network function of the physical node is finally adjusted to be deployed to a path with the minimum comprehensive cost of the network.
Preferably, the comprehensive cost specifically includes: load cost, traffic delay cost, and node opening cost.
Preferably, the network is specifically an SDN-based cloud edge collaborative network.
The invention also provides an electronic device comprising a central processing unit, a signal processing and storing unit and a computer program stored on the signal processing and storing unit and capable of running on the central processing unit, wherein the central processing unit executes the deployment adjustment method of the virtual network function based on SDN.
In the technical scheme of the invention, after receiving a request for deploying the virtual network function, an initial deployment scheme after deploying the virtual network function of the request is determined in an SDN-based network; traversing physical nodes and links of SFGs formed by all SFCs in the network based on an initial deployment scheme, and determining overload physical nodes and links; and performing deployment adjustment of the virtual network function aiming at the overload physical nodes and links. Therefore, aiming at the SFG combined by all the deployed SFCs, the overloaded nodes and links in the network global are redeployed and routed, and a deployment adjustment scheme of the global virtual network function is output; compared with the existing technology for scaling network functions from the single SFC angle, the technical scheme of the invention can efficiently ensure the utilization rate of the whole network resource and balance the load between network devices.
Furthermore, the technical scheme of the invention also provides a cloud edge cooperative network flow peak time scaling cost comprehensive optimization evaluation model based on SDN, which is applied to the deployment adjustment scheme of the virtual network function, so that the cost-load balance of SFG can be realized, the comprehensive cost of the network after deployment adjustment is optimized, the SDN network can ensure the service quality of SFC at the flow peak, and the load is globally balanced from the network.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a cloud edge cooperative network architecture based on SDN according to an embodiment of the present invention;
fig. 2 is a flowchart of a deployment adjustment method of a virtual network function based on 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 virtual network functions 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 physical links 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 SFG deployment at peak flow provided by an embodiment of the present invention;
FIGS. 8, 9 and 10 are schematic diagrams showing experimental results of various deployment adjustment algorithms according to embodiments 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
The present invention will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present invention should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present disclosure pertains. The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
In order to solve the above problems, the present inventors provide a global SFG scaling method under multiple SFC scenarios, consider SFGs formed by combining all SFCs deployed in a network, redeploy and route overloaded nodes and links in the network global, and output a scaling scheme of SFGs, that is, a deployment adjustment scheme of global virtual network functions.
More preferably, the technical scheme of the invention also provides a scaling cost comprehensive optimization evaluation model when the SDN cloud edge cooperates with the network flow peak value, and the scaling cost comprehensive optimization evaluation model is applied to the scaling scheme of the global SFG, so that a cost-load balance scaling deployment algorithm of the SFG can be realized, the comprehensive cost of the network after the deployment is adjusted is optimized, the SDN network can ensure the service quality of the SFC at the flow peak value, and the load is balanced from the network global.
The following describes the technical scheme of the embodiment of the present invention in detail with reference to the accompanying drawings.
The cloud edge collaborative network architecture based on SDN is shown in FIG. 1, and comprises a cloud layer, an edge layer and a terminal layer. The Cloud Layer (Cloud Layer) includes Cloud Server (Cloud Server) nodes and Router (Router) nodes, the Edge Layer (Edge Layer) includes Edge Server (Edge Server) and Router (Router) nodes, and the Terminal Layer (Terminal Layer) includes access and user terminals of IoT devices. The user request generated by the terminal layer and the basic information of the cloud-side layer network are taken as input of an NFV management orchestrator, a plurality of requests can form an SFG based on instance sharing, and the NFV MANO outputs deployment strategies 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 under an SFC scene are established.
The network model reflects information of the physical network and is defined as follows:
defining a base network of an SDN-based cloud edge collaborative network as an undirected graph G (N, E), wherein N is a physical node set, and N= { N SV ,N RT }＝{n 1 ,n 2 ,...,n |N| E is a set of physical links, e= { E 1 ,e 2 ,...,e |E| }。
Wherein N is SV ＝{Ν E ,N C },Ν E N is the edge server node set C Is a cloud server node set.
The ith physical node N in N i Can use tupleRepresentation of->Is the total computing resource of the physical node, +.>Indicating the remaining computing resources of the node, pr i Indicating the node's ability to process data packets, actc i Representing the turn-on cost at which the current node is enabled. type (type) i Indicating the type of the node.
When n is i ∈N RT When being a routing node, type i =0, indicating that the routing node has no server,pr i =0, and thus does not carry VNF.
When n is i When being an edge server node, type i ＝1，n i ∈N E ∈N SV 。
When n is i When being a cloud server node, type i ＝2，n i ∈N C ∈N SV 。
Cloud server nodes have more computing resources and data processing capabilities than edge server nodes.
E＝{E EE ,E EC ,E CC ' represents a 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 cloud server nodes and edge server nodes, E CC Representing a set of links between cloud server nodes.
Tuple for each physical linkRepresentation of->Representing the total bandwidth resources of the physical link, +.>Representing the bandwidth resources remaining for the link, d j Representing link e j Is a function of the propagation delay of the optical fiber. e, e j ∈E EE When the link is between the edge nodes, the bandwidth of the link is small, and the propagation delay is small; e, e j ∈E EC When the link is connected with the edge node and the cloud server node, the link has larger bandwidth and larger propagation delay; e, e j ∈E CC When the links are connected with different cloud server nodes, the large link bandwidth and the small propagation delay are provided. Note e j Can be written as +.>Wherein i is 1 ,i 2 Representing link e j Sequence numbers of two physical nodes connected. Similarly, let go of>
In combination with the network model, two variables nlr, elr are defined to describe the load rates of the physical nodes and the physical links, respectively, as shown in formulas 1 and 2, respectively, so as to evaluate the load condition of the whole network.
Further, the load factor of the whole network is as shown in formula 3:
the SFG model reflects information of the virtual network and is defined as follows:
describing SFC business 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 function components, < ->Is a virtual link set, representing a traffic flow path, and snum represents the number of SFCs contained in the SFG. Set rd= { RD 1 ,rd 2 ,...,rd snum Record G V Maximum tolerated delay for each SFC in the system. Similarly, the s-th SFCs may be expressed as/>
The ith virtual node is composed of tuplesDescription in which v i VNF type representing the virtual node, +.>Representing the computational resources required by the virtual node, P i Indicating the size of the data packet to be processed by the virtual node,/->Representing the virtual node +.>The SFC set to which it belongs.
Jth virtual link routing tupleDescription of the invention wherein->Respectively indicate->Source virtual node and target virtual node of (a). />Representation->Bandwidth resources required, < > on->Indicating the flow passing->Is a SFC set of (c). 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 definedTo describe the mapping of virtual networks to physical networks.Representing virtual node->Deployed at physical node n i' Applying; />Representing virtual Link->Deployed on physical link e j' And (3) upper part. Assuming a virtual node to server node mapping of 1 to 1, the virtual link to physical link is an m to n mapping.
Combining SFC information corresponding to nodes and linksAnd deployment information->Each piece of SFC deployment information may be obtained. The physical node set of SFCs deployment is +.>The physical link set isFor->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 the network is shown in formula 6:
the flow change assessment model may reflect load information of the flow peaks, defined as follows:
when defining traffic peaks, virtual nodesIs +.>Virtual Link->Is +.>Is provided with->Virtual node +.>The flow rate change of (2) is shown in formula 7:
virtual linksThe flow rate change of (2) is shown in formula 8:
when (when)When the currently deployed server has the capability of bearing new VNF examples required by burst traffic, selecting to perform vertical scaling of the VNF examples, otherwise, performing horizontal scaling only, and deploying n in the new server node 1 Instances and allocates paths. Similarly, let go of>And when the route is scaled vertically, requesting new bandwidth on the original path, and otherwise, scaling the route horizontally.
At the time of the flow peak, the node load factor is shown in formula 9:
at the time of the traffic peak, the link load rate is shown in formula 10:
at the time of the traffic peak, the load factor of the whole network is shown in formula 11:
wherein,when the flow peak value is represented, the node load rate of the whole network is represented; />When the flow peak value is represented, the link load rate of the whole network is represented; n SV The i indicates the number of server nodes of the entire network; i E i represents the number of physical links of the entire network.
The total cost of opening a server in the network that has deployed virtual nodes, i.e., deployed virtual network functions, is shown in equation 12:
wherein, actc i Indicating the start-up cost of the i-th server,representing virtual node->And physical node n i Mapping relation of->Representation->Deployed on physical node n i ；/>Representation->Not deployed at physical node n i ；
The delay model is defined as follows:
the end-to-end delay of the SFC includes three parts, propagation delay, transmission delay, queuing and processing delay.
1. Propagation delay: related to the length of the physical link, by d j And (5) determining. The sum of propagation delays of SFCs is shown in equation 13:
2. transmission delay: the transmission delay of the 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 formula 14:
3. queuing and processing time delay: the processing delay of the VNF of the SFCs, which is related to the packet size, the node processing capability, and the requested computing resources, can be calculated according to equation 15:
wherein the virtual nodeDeployment at physical node->And (3) upper part. />The queuing delay of the VNF is equal to the waiting processing time after the arrival of the pending data packet. Modeling the server of the access point as M/M/1 queue, arriving at the server +.>Task compliance for computingAnd (5) circulating the poisson process, wherein the arrival rate is lambda. Thus Server->Average latency of completion of VNF->(including queuing and service time) can be expressed as shown in equation 16:
wherein,thus, the queuing and processing delay of SFCs can be expressed as equation 17:
to sum up, the total delay of SFCs is calculated as equation 18:
delay s ＝ppd s +trd s +qpd s (equation 18)
The optimization problem model is defined as follows:
the optimization objective herein 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) as expressed in equation 19:
wherein omega 1 ，ω 2 ，ω 3 Is a custom parameter omega 1 +ω 2 +ω 3 ＝1。At the peak of the flow, the wholeLoad factor of the network; rd s Representing the maximum tolerated time delay of the SFCs; />Representing the time delay rate of SFCs; />Representing the sum of the delay rates of all SFCs; snum represents the number of SFCs contained by the SFG; actc represents the total cost of opening of servers in the network where virtual network functions have been deployed; />Representing the total cost of opening of all servers in the network; ρ is an acceptance rate penalty value, which is caused by the traffic peak resulting in the failure to deploy the service, and can be calculated according to the following formula 20:
where total_sfc represents the number of all SFCs and accepted_sfc represents the number of SFCs successfully deployed.
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 nodes or links does not exceed its total resources, as shown in equations 21, 22:
the mapping of server nodes and virtual nodes is then 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 virtual network functions based on 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 deploying the requested virtual network function in the network.
Specifically, after receiving a request for deploying virtual network functions sent by user equipment, determining information of a source node and a plurality of virtual network functions to be deployed from the request; and then determining a deployment scheme after deploying the plurality of virtual network functions of the request in the SDN-based network by adopting the existing method, and taking the deployment scheme as an initial deployment scheme.
Step S202: based on the initial deployment scheme, traversing physical nodes and physical links in SFGs formed by all SFCs in the network, and determining overload physical nodes and physical links.
Specifically, based on an initial deployment scheme, traversing physical nodes and physical links of SFGs formed by all SFCs in a network, and determining overload physical nodes and physical links;
for example, a physical node whose load rate exceeds a node load degree threshold α is determined as an overloaded physical node; a physical link whose load factor exceeds a link load factor threshold β is determined to be an overloaded physical link. Where α and β may be set empirically by one skilled in the art, such as setting α=β=0.7.
And the overload physical nodes form a node set, and the overload physical links form a link set.
Step S203: and performing deployment adjustment of the virtual network function aiming at the overload physical nodes and physical links.
In the step, deployment adjustment of virtual network functions is sequentially carried out on each physical node in the node set according to the load rate; and sequentially performing deployment adjustment of virtual network functions on each physical link in the link set according to the load rate.
Specifically, the method for sequentially performing deployment adjustment of virtual network functions on each physical node in the node set according to the load rate, the flow is shown in fig. 3, and the method comprises the following steps:
step S301: ordering all physical nodes in the node set according to the load rate from big to small;
step S302: and sequentially performing deployment adjustment on the virtual network functions on the physical nodes after sequencing.
In the step, the deployment adjustment of the virtual network function is sequentially carried out on the ordered physical nodes according to the order of the load rate from large to small; for the physical node to be deployed and adjusted currently, a specific method for performing deployment and adjustment of a virtual network function is shown in fig. 4, and the flow includes the following sub-steps:
substep S401: determining a precursor node and a subsequent node of the SFC to which the physical node belongs;
substep S402: the precursor node is used as a starting node, and the subsequent node is used as a termination node; taking a path between the starting node and the ending node as a path to be adjusted;
substep S403: and selecting a path with a server node which is consistent with the physical node type and meets the load rate requirement from a plurality of paths between the starting node and the ending 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 substep, a preferred embodiment may be used in selecting the path:
selecting a path with a server node consistent with the physical node type from a plurality of paths between the starting node and the ending node as a candidate path by using a tabu search algorithm based on the optimal comprehensive cost; for 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; the virtual network function of the physical node is finally adjusted to be deployed to a path with the minimum comprehensive cost of the network.
The comprehensive cost of the network can comprise load cost, service delay cost and node opening cost in the network;
in the case where the network is specifically the SDN-based cloud edge collaborative network, the comprehensive cost of the network may be calculated according to the above formula 19; that is, the comprehensive cost of the network in the current deployment situation 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 composed of all SFCs in the network (i.e. the information of the virtual network), the mapping information of the virtual network to the physical network, the load information of the traffic peak value, the time delay of all SFCs in the network and the starting cost of the server.
Specifically, the method for sequentially performing deployment adjustment of virtual network functions on each physical link in the link set according to the load rate, the flow is shown in fig. 5, and the method comprises the following steps:
step S501: ordering all physical links in the link set according to the load rate from big to small;
step S502: and sequentially performing deployment adjustment on the virtual network functions on the ordered physical links.
In the step, the deployment adjustment of the virtual network function is sequentially carried out on the ordered physical links according to the order of the load rate from large to small;
for the physical link to be deployed and adjusted currently, deploying and adjusting m paths; 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:
sub-step S601: taking the 1 st physical node of the link as an initial node and the (k+2) th physical node of the link as a termination node;
substep S602: taking a path between the starting node and the ending node as a path to be adjusted;
substep S603: and selecting a path with a server node which is consistent with the physical node type and meets the load rate requirement from a plurality of paths between the starting node and the ending 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 substep, a preferred embodiment may be used in selecting the path:
selecting a path with a server node consistent with the physical node type from a plurality of paths between the starting node and the ending node as a candidate path by using a tabu search algorithm based on the optimal comprehensive cost; for 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; the virtual network function of the physical node is finally adjusted to be deployed to a path with the minimum comprehensive cost of the network.
The comprehensive cost of the network can comprise load cost, service delay cost and node opening cost in the network;
in the case where the network is specifically the SDN-based cloud edge collaborative network, the comprehensive cost of the network may be calculated according to the above formula 19; that is, the comprehensive cost of the network in the current deployment situation 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 composed of all SFCs in the network (i.e. the information of the virtual network), the mapping information of the virtual network to the physical network, the load information of the traffic peak value, the time delay of all SFCs in the network and the starting cost of the server.
For example, as shown in fig. 7, one example of SFG deployment at traffic peaks is illustrated. In the upper part of fig. 7, the SFG deployment situation when the adjustment (horizontal scaling) is not deployed is shown, let α=0.7, where the overloaded server node includes edge server n 1 And cloud server n 3 ,n 7 The overloaded physical links are part of the links that go through these servers. To balance the load of the network, n is set when deployment adjustment is performed 1 ,n 3 ,n 7 Some instances of the bearer migrate to the new server node deployment. Specifically, n is 1 The two VNF1 instances of the bearer are deployed to edge server n 8 Will n 3 Deployment of 1 VNF3 instance of bearer to cloud server n 10 Will n 7 Two VNF6 instances of the bearer are deployed to cloud server n 9 . And simultaneously, adjusting the link deployment related to the nodes. Thus, overloaded nodesAnd link resources are partially released, the local overload situation of the network is relieved.
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 cloud servers, 10 edge servers and 10 router nodes simulate the CEC network architecture. Nodes in the network have 204 physical links connected. In our simulation, SFC requests will be generated according to a certain arrival frequency, λ=4 at the peak of the flow. Furthermore, we assume that each SFC consists 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 our algorithm based on the deployment of tabu search, the tabu table size is 20, the iteration number is 50, and epsilon=0.3 of epsilon-greedy method is set. Omega for the optimization objective herein 1 ＝0.7，ω 2 ＝0.2，ω 3 =0.1 is the default configuration. Node and link load threshold α=β=0.7.
The algorithm of the technical scheme of the invention is SFG-Scaling, and is a cost-load balancing Scaling deployment algorithm based on SFG;
the SFC-Scaling algorithm of the prior art scheme is a Scaling deployment algorithm facing to cost and load according to a single SFC at a time.
The SFC-LEB algorithm is a balanced deployment algorithm for load and energy consumption, and aims to reduce the change of scaling on network topology when the flow peak value is reduced while optimizing the load.
The SFC-DA algorithm in the prior art is a time delay-aware SFC deployment algorithm, and the load is unbalanced when the flow is in 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 SFCs successfully deployed divided by the total number of SFCs requested by the user. As shown in fig. 8, the algorithm proposed herein can ensure that there is still a higher acceptance rate when the number of traffic is large. Both SFC-Scaling and SFC-LEB balance the load of the network to ensure successful deployment of subsequent services so that as the number of services increases, the acceptance rate of both is still over 70% although it decreases. SFC-LEB does not consider the optimization of latency, there may be SFC that fails deployment due to service timeout, and therefore acceptance rate is lower than SFC-Scaling. The SFC-DA does not scale the deployment situation when the traffic peaks, and a large amount of services are failed to deploy because of insufficient resources of a single server node or a single link, so that the SFC acceptance rate is worst.
(2) The delay of the service is defined in a delay model, the result of which is shown in fig. 9. In terms of service delay, SFC-Scaling performs best, and tends to be deployed on a lower-delay path for each SFC than SFG-Scaling is for multiple SFCs to jointly optimize. Meanwhile, the SFG-Scaling algorithm proposed herein is the second best performing. SFC-DA is a deployment algorithm facing delay optimization, and the shortest-delay critical path is occupied in the early stage, so that the delay is lower. However, SFC-DA algorithms do not scale SFCs, so as traffic increases, nodes and links on critical paths are already unable to carry more SFCs, and therefore deployment needs to be completed over more distant paths, creating a significant amount of delay. The SFC-LEB algorithm does not care for the delay behavior of the traffic and therefore the delay is highest.
(3) We define the integrated scaling cost as a weighted calculation of network load, service latency, node turn-on cost, and acceptance rate, while being the optimization objective herein. As can be seen from fig. 10, the algorithm presented herein has the lowest average overall deployment cost. The algorithm presented herein is directed to a single-request multi-SFG with better performance in terms of network load factor when compared to single-SFC scaling algorithms. Compared with the SFC deployment algorithm with balanced load and energy consumption, the SFC-Scaling algorithm has more consideration on the time delay cost, so that the comprehensive cost is lower. The time delay-aware SFC deployment algorithm cannot alleviate the local overload situation during the traffic peak, so the deployment cost is high. With the increase of time, the SFC-DA algorithm has no node capable of bearing more VNs, so that the increase of the comprehensive deployment cost is smooth.
Fig. 11 is a schematic diagram showing a hardware structure of a more specific electronic device according to the present embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc., for executing relevant programs to implement the deployment adjustment method of virtual network functions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module and may be connected with a nonlinear receiver to receive information from the nonlinear receiver for information input and output. The input/output module may be configured as a component in a 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 types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
In the technical scheme of the invention, after receiving a request for deploying the virtual network function, an initial deployment scheme after deploying the requested virtual network function in the network is determined; based on an initial deployment scheme, traversing physical nodes and links in SFGs formed by all SFCs in a network, and determining overload physical nodes and links; and performing deployment adjustment of the virtual network function aiming at the overload physical nodes and links. Therefore, aiming at the SFG combined by all the deployed SFCs, the overloaded nodes and links in the network global are redeployed and routed, and a deployment adjustment scheme of the global virtual network function is output; compared with the existing technology for scaling network functions from the single SFC angle, the technical scheme of the invention can efficiently ensure the utilization rate of the whole network resource and balance the load between network devices.
Furthermore, the technical scheme of the invention also provides a cloud edge cooperative network flow peak time scaling cost comprehensive optimization evaluation model based on SDN, which is applied to the deployment adjustment scheme of the virtual network function, so that the cost-load balance of SFG can be realized, the comprehensive cost of the network after deployment adjustment is optimized, the SDN network can ensure the service quality of SFC at the flow peak, and the load is globally balanced from the network.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to 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 storage media for a computer 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, which can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the disclosure, including the claims, is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the 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.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the invention. Furthermore, the 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., such 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 in nature and not as restrictive.
While the invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.

Claims (5)

1. A deployment adjustment method for a virtual network function, comprising:
after receiving a request for deploying the virtual network function, determining an initial deployment scheme after deploying the virtual network function of the request in a network based on a software defined network SDN;
based on an initial deployment scheme, traversing physical nodes and links in all end-to-end service function chains SFC in the network, and determining overload physical nodes and links;
performing deployment adjustment of virtual network functions for overloaded physical nodes and links:
respectively forming an overload physical node and a link into a node set and a link set;
and sequentially performing deployment adjustment of virtual network functions on each physical node/link in the node set/link set according to the load rate:
ordering all physical nodes in the node set according to the load rate from big to small; sequentially performing deployment adjustment of virtual network functions on the ordered physical nodes:
for a physical node to be deployed and adjusted currently, determining a precursor node and a subsequent node of SFC to which the physical node belongs;
the precursor node is used as a starting node, and the subsequent node is used as a termination node; taking a path between the starting node and the ending node as a path to be adjusted;
selecting a path with a server node which is consistent with the physical node type and meets the load rate requirement from a plurality of paths between the starting node and the ending 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;
ordering all physical links in the link set according to the load rate from big to small; sequentially performing deployment adjustment of virtual network functions on the ordered physical links:
for the physical link to be deployed and adjusted currently, deploying and adjusting m paths; wherein m=l-2, L is the total number of physical nodes in the physical link; the deployment adjustment process of the kth path is as follows:
taking the 1 st physical node of the physical link as an initial node and the (k+2) th physical node of the physical link as a termination node; taking a path between the starting node and the ending node as a path to be adjusted;
selecting a path with a server node which is consistent with the physical node type and meets the load rate requirement from a plurality of paths between the starting node and the ending 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;
the method for selecting the paths with the server nodes consistent with the physical node type and the load rate meeting the requirement from a plurality of paths between the starting node and the ending node by using a tabu search algorithm specifically comprises the following steps:
selecting a path with a server node consistent with the physical node type from a plurality of paths between the starting node and the ending node as a candidate path by using a tabu search algorithm based on the optimal comprehensive cost;
for 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;
finally adjusting the virtual network function of the physical node to be deployed to a path with the minimum comprehensive cost of the network;
the comprehensive cost calculation mode is shown in formula 19:
wherein omega 1 ，ω 2 ，ω 3 Is a custom parameter omega 1 +ω 2 +ω 3 =1, ρ is the acceptance rate penalty;when the load is the peak value of the flow, the load rate of the whole network; />Representing the sum of the delay rates of all SFCs; snum represents the SFC number; actc represents the total cost of opening of servers in the network where virtual network functions have been deployed; />Representing the total cost of opening for all servers in the network.
2. The method according to claim 1, wherein the integrated cost specifically comprises: load cost, traffic delay cost, and node opening cost.
3. The method of claim 1, wherein the network is in particular an SDN based cloud edge collaborative network.
4. The method of claim 1, wherein the load factor of the entire network at the time of the traffic peakSpecifically, the method is calculated as shown in formula 11:
wherein,when the flow peak value is represented, the node load rate of the whole network is represented; />When the flow peak value is represented, the link load rate of the whole network is represented; n SV The i indicates the number of server nodes of the entire network; i E i represents the number of physical links of the entire network.
5. 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 1-4 when executing the program.
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