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CN108923961B - Multi-entry network service function chain optimization method - Google Patents

Multi-entry network service function chain optimization method Download PDF

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CN108923961B
CN108923961B CN201810659293.2A CN201810659293A CN108923961B CN 108923961 B CN108923961 B CN 108923961B CN 201810659293 A CN201810659293 A CN 201810659293A CN 108923961 B CN108923961 B CN 108923961B
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service request
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CN108923961A (en
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李源灏
韦云凯
毛玉明
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University of Electronic Science and Technology of China
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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
    • H04L41/0893—Assignment of logical groups to network elements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5041—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5041—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
    • H04L41/5051—Service on demand, e.g. definition and deployment of services in real time
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H04L67/61—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources taking into account QoS or priority requirements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H04L67/62—Establishing a time schedule for servicing the requests
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H04L67/63—Routing a service request depending on the request content or context

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Abstract

The invention discloses a multi-portal network service function chain optimization method, which comprises the steps of performing link allocation according to the number of service request types of each access node in a short enough time, then dynamically backtracking link reallocation, allocating weighted values of different service requests according to statistical results by counting the service request types and frequencies, preferentially deploying service requests with high weighted values, combining certain services according to energy consumption requirements to achieve the balance and optimization of time delay and energy consumption, and reducing network computing resource overhead.

Description

Multi-entry network service function chain optimization method
Technical Field
The invention belongs to the technical field of network optimization, and particularly relates to a multi-portal network service function chain optimization method.
Background
With the advancement of SDN/NFV technologies, the traditional static network service model has been unable to meet the requirements of applications. Due to the close coupling relationship between the service and the special hardware equipment in the framework, the defects that network resources cannot be shared, new services are difficult to merge and the like are caused. When the network is expanded, more cost is required to be invested to deploy the new service. A service function chain consists of one or more virtual network functions, intended to provide a complete end-to-end service for the network. The service function chain has the advantages of reducing the network construction and operation and maintenance cost, improving the network resource utilization rate, improving the network and service deployment speed and the like.
The current research on service function chains mainly focuses on the problem of service chain deployment with single inlet, single outlet and no branch, and the problems of service node sharing and conflict caused by multi-inlet service chain deployment are not considered yet. Therefore, a network, such as a mobile social network, an EPC portion of a mobile communication network, etc., which provides a response to a request sent by a multi-access point and a single service provider, needs to design a service function chain deployment algorithm with higher efficiency and lower time delay and energy consumption according to the requirements of users. Meanwhile, the multi-portal service function chain algorithm can better fit with the network environment of the real world, so that the multi-portal service function chain algorithm has application value compared with the traditional algorithm.
A Service Function Chain (SFC) is a network management scheme for network Service deployment that allows network operators to distribute network functions to general-purpose servers. Also, SFC allows dynamic combination of virtual network functions and their deployment as Virtual Machines (VMs) to servers anywhere in the network according to predefined goals, forming an ordered set of service function chain sequence chains. Network functions such as firewalls, load balancers, and deep packet inspection systems may be placed in the most appropriate locations in the network to meet customer, quality of service, and regulatory requirements. Compared with the traditional network, the service function chain based on the NFV can get rid of the dependence on the specific physical network topology to a great extent, and the coupling degree with the network equipment is reduced. As data traffic traverses the service chain, the service and the context between services may share information. In the end-to-end service, the service chain only needs to be classified once, so that the whole process is more convenient and efficient.
Current service chain deployment approaches lack end-to-end service visibility for multiple portals. Single inlet single outlet networks tend to be simple and are not representative. In a multi-portal network, once a problem occurs in the network, since the network environment is more complicated. The removal of network failures becomes complicated and involves more expertise in the network and service sector. This problem becomes more acute when the service function chain spans multiple data centers or administrative boundaries.
When a service function chain is deployed, the topology dependency of the service function chain directly determines the complexity of a network, and the current service chain solution mainly considers the service function chain deployment problem in an ideal environment, but does not consider network burst factors such as service node failure. Simple operations such as changing the order of service functions in a service function chain require a change in the logical or physical topology. The problem of the network with high requirement on reliability, such as industrial internet, is particularly remarkable. When a service needs to be deployed in a production environment, once wrong configuration occurs, equipment is shut down, and huge loss is brought to a production department.
Disclosure of Invention
The invention aims to: in order to solve the above problems in the prior art, the present invention provides a method for optimizing a service function chain of a multi-portal network.
The technical scheme of the invention is as follows: a multi-portal network service function chain optimization method comprises the following steps:
A. initializing a network environment, acquiring a physical network, an Access Point (AP) of a multi-entry network and a Content Provider (CP), and setting a set of service request types and corresponding request frequency vectors in a time period tau;
B. judging whether a network request exists or not; if yes, classifying the service requests in the time period tau according to the entry numbers and the service request types, and establishing an entry set and a service request type set; if not, performing the step E;
C. b, sorting the various service requests in the step A according to the weight vectors to obtain an ordered set which is increased in number, and outputting service request strategy type sequence vectors;
D. performing service function deployment and energy consumption optimization processing by adopting a Parallels-merge algorithm;
E. judging whether undeployed virtual network functions exist or not; if yes, returning to the step B; if not, the operation ends.
Further, the weight vector in step C is represented as:
Wk＝pk×fk
wherein, WkAs weight vectors, pkFor the kth element, f, in the set of service request types within the time period τkThe kth element in the frequency vector is requested.
Further, in the step D, a Parallels-merge algorithm is adopted to perform service function deployment and energy consumption optimization, and the method specifically includes the following sub-steps:
d1, judging whether a service request needs to be deployed at present; if yes, go to step D2; if not, the operation is ended;
d2, acquiring the quantity of each entry request, a service request type set, an entry set and a service request strategy type sequence vector;
d3, according to the sequence of the service requests provided by the service request strategy type sequence vector, mapping the ordered vectors in the step B to a physical network by adopting a K shortest path algorithm based on Dijkstra algorithm to obtain a plurality of shortest path sets P;
d4, judging whether the shortest-circuit set P is an empty set; if yes, outputting error information that the network capacity is full; if not, go to step D5;
d5, judging whether the number of nodes contained in the shortest path set P is larger than the number of virtual services required by the current service request type; if yes, go to step D6; if not, returning to the step D3;
d6, merging the shortest circuits in the shortest circuit set by merging the same virtual service types by adopting a backtracking method, calculating the shortest circuits of the content providers of the access ports after merging, calculating the time delay and the energy consumption from the access ports to the content providers, and storing the shortest circuits and the energy consumption into a Dvec matrix and an Evec matrix;
d7, judging whether the Dvec matrix and the Edec matrix are both empty sets; if so, ending the operation; if not, go to step D8;
d8, judging whether the increased time delay is smaller than the reduced energy consumption; if yes, merging the virtual service functions; if not, returning to the step D7;
d9, updating the network topology edge weight matrix, and judging whether the network reliability is greater than a reliability threshold value; if so, ending the operation; if not, the procedure returns to step D7.
Further, in step D6, the shortest path of the merged inlet content provider is calculated and is represented as:
d(AP,CP)＝d(AP,vi)+d(vi,CP)
where d (AP, CP) represents the shortest distance between the access point AP and the content provider CP, d (AP, v)i) Indicating from AP to node v where some virtual service function is placediD (v) ofiCP) denotes a node v in which a certain virtual service function is placediDistance to CP.
Further, step D6 further includes converting D (AP, v)i) And d (AP, v)j)+l(ei,j) And (3) comparison:
when d (AP, v)i)＝d(AP,vj)+l(ei,j) In (v) isj)＝In(vi)；
When d (AP, v)i)＞d(AP,vj)+l(ei,j) In (v) isj)＝In(vi)+d(AP,vj)-d(AP,vi)+l(ei,j)；
When d (AP, v)i)＜d(AP,vj)+l(ei,j) In (v) isj)＝In(vi)+2l(ei,j)；
Wherein In (-) is the delay increment, l (e)i,j) A policy band loop is programmed for the current service chain.
The invention has the beneficial effects that: the invention can optimize the network aiming at the weight values of different energy consumption and time delay, quickly process and coordinate the time delay and energy consumption optimization problem of the multi-entrance network, effectively reduce the calculation resource occupation of the message in the service chain system, and reduce the end-to-end time delay in the long-time running process, thereby improving the stability of the service chain system and optimizing the user service experience.
Drawings
Fig. 1 is a flowchart illustrating a method for optimizing a service function chain of a multi-portal network according to the present invention.
Fig. 2 is a schematic flow chart of the Parallels-merge algorithm in the 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 the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Fig. 1 is a flow chart illustrating a method for optimizing a service function chain of a multi-portal network according to the present invention. A multi-portal network service function chain optimization method comprises the following steps:
A. initializing a network environment, acquiring a physical network, an Access Point (AP) of a multi-entry network and a Content Provider (CP), and setting a set of service request types and corresponding request frequency vectors in a time period tau;
B. judging whether a network request exists or not; if yes, classifying the service requests in the time period tau according to the entry numbers and the service request types, and establishing an entry set and a service request type set; if not, performing the step E;
C. b, sorting the various service requests in the step A according to the weight vectors to obtain an ordered set which is increased in number, and outputting service request strategy type sequence vectors;
D. performing service function deployment and energy consumption optimization processing by adopting a Parallels-merge algorithm;
E. judging whether undeployed virtual network functions exist or not; if yes, returning to the step B; if not, the operation ends.
In an optional embodiment of the present invention, in the step a, the physical network G ═ N, L, the Access Point AP (Access _ Point) and the Content Provider CP (Content _ Provider) of the multi-portal network are obtained, a delay weight α meeting the QoS of the user, an energy consumption weight β, and a set Policy ═ p of service request types in the time period τ are set1,p2,p3...pnAnd the corresponding request frequency vector F ═ F1,f2,f3...fn}, reliability requirement R ∈ (0, 1).
In an optional embodiment of the present invention, in the step B, all the entry numbers and service request types in the network are traversed, and classified according to the entry numbers and the service request types, and an entry set and a service request type set are established.
In an optional embodiment of the present invention, in the step C, the various service requests in the time period τ are classified and weighted and sorted, and each service request p is sorted according to weight, and a weight vector of each service request is calculated, which is represented as:
Wk＝pk×fk
wherein, WkAs weight vectors, pkFor the kth element, f, in the service request type set Policy within the time period τkThe kth element in the frequency vector is requested.
Weight vector W for various service requestskAnd sequencing to obtain an ordered set which is increased progressively according to the numerical value, and outputting a service request strategy type sequence vector.
In an optional embodiment of the present invention, in the step D, a Parallels-merge algorithm is adopted to perform service function deployment and energy consumption optimization processing, as shown in fig. 2, a schematic flow diagram of the Parallels-merge algorithm in the embodiment of the present invention specifically includes the following sub-steps:
d1, judging whether a service request needs to be deployed at present; if yes, go to step D2; if not, the operation is ended;
d2, acquiring the quantity of each entry request, a service request type set, an entry set and a service request strategy type sequence vector;
d3, according to the sequence of the service requests provided by the service request strategy type sequence vector, mapping the ordered vectors in the step B to a physical network by adopting a K shortest path algorithm based on Dijkstra algorithm to obtain a plurality of shortest path sets P;
d4, judging whether the shortest-circuit set P is an empty set; if yes, outputting error information that the network capacity is full; if not, go to step D5;
d5, judging whether the number of nodes contained in the shortest path set P is larger than the number of virtual services required by the current service request type; if yes, go to step D6; if not, returning to the step D3;
d6, merging the shortest circuits in the shortest circuit set by merging the same virtual service types by adopting a backtracking method, calculating the shortest circuits of the content providers of the access ports after merging, calculating the time delay and the energy consumption from the access ports to the content providers, and storing the shortest circuits and the energy consumption into a Dvec matrix and an Evec matrix;
d7, judging whether the Dvec matrix and the Edec matrix are both empty sets; if so, ending the operation; if not, go to step D8;
d8, judging whether the increased time delay is smaller than the reduced energy consumption; if yes, merging the virtual service functions; if not, returning to the step D7;
d9, updating the network topology edge weight matrix, and judging whether the network reliability is greater than a reliability threshold value; if so, ending the operation; if not, the procedure returns to step D7.
In an optional embodiment of the present invention, the step D2 obtains the access points of the multi-portal network and the service request type of each access point, according to the ordered Vector W in the step CkPriority deployment high priorityAnd re-servicing.
In an alternative embodiment of the present invention, the step D3 uses a shortest path algorithm to obtain the shortest path p from the access port to the content provider CP0,k＝1,DiRecording path when d is equal to di；
In an alternative embodiment of the present invention, the step D4 determines the Path traveled by the service request initiated from the access point to the content providerkWhether it is empty; if so, indicating that the current network cannot deploy the service function chain of the type, and outputting error information that the network capacity is full; if not, go to step D5;
in an optional embodiment of the present invention, the step D5 determines whether the number of nodes included in the shortest path set P is greater than the number of virtual services required by the current service request type policy, because the service function chain must satisfy the reliability of the end-to-end service, if the path satisfies that the number of nodes is greater than the number of virtual services, that is, by obtaining the number of nodes remaining on the shortest path and capable of providing services, it determines whether the number of nodes is greater than the number of virtual services, if so, execute step D6; if the number of nodes on the link is less than the number of virtual services, the process returns to step D3 to calculate a new shortest path.
In an optional embodiment of the present invention, the step D6 uses a backtracking method to find the kth shortest path of the shortest paths from the access port to the content provider CP, and calculates a point v on the shortest path from the access port to the content provideriAnd its neighboring node vjIncreased time delay; expressed as:
d(AP,CP)＝d(AP,vi)+d(vi,CP)
where d (AP, CP) represents the shortest distance between the access point AP and the content provider CP, d (AP, v)i) Indicating from AP to node v where some virtual service function is placediD (v) ofiCP) denotes a node v in which a certain virtual service function is placediDistance to CP;
d (AP, v) according to the theory of graph theoryi) And d (AP, v)j)+l(ei,j) And (3) comparison:
when d (AP, v)i)＝d(AP,vj)+l(ei,j) In (v) isj)＝In(vi),k＝k+1；
When d (AP, v)i)＞d(AP,vj)+l(ei,j) In (v) isj)＝In(vi)+d(AP,vj)-d(AP,vi)+l(ei,j)；
When d (AP, v)i)＜d(AP,vj)+l(ei,j) In (v) isj)＝In(vi)+2l(ei,j)；
Wherein l (e)i,vi) Is a node viAnd node vjMinimum delay between, In (-) is the delay increment, l (e)i,j) A policy band loop is programmed for the current service chain.
In an optional embodiment of the present invention, the step D8 determines whether α Δ D is smaller than β Δ E, Δ D is an increased delay value, Δ E is a decreased value of energy consumption, if yes, merges the virtual service functions, and merges from the last service of the farthest path to the shortest cycle, otherwise, performs step D7;
the invention obtains the service request type set Policy in the time period tau from the access point of the multi-portal network, counts the service quantity of the service request type in the access point AP of all the multi-portal networks for each type of service request set until the last service request Policy-k is deployed, and moves out the service request k after the service request k is deployed every time, thereby obtaining the physical position of the service corresponding to the service request type set Policy in the physical network GThe total network delay sigma D and the total physical network energy consumption are carried out until P is an empty set.
Figure GDA0002335428590000062
Denotes the use of fnType service, and deployment at nkOn the physical node, k represents the kth entry node, i represents the ith type service request, j1J represents the service request1A service;
Figure GDA0002335428590000063
represents the occupied bandwidth biAnd is disposed atkOn a physical link; j is a function of2J' th indicating occupation of the service request2A physical link;
in an optional embodiment of the present invention, the step D9 updates the network topology edge weight matrix, and determines whether the network reliability is greater than the reliability threshold; if so, ending the operation; if not, returning to the step D7, and completing unreliable services in the network according to the set reliability requirement and the reliability threshold value until 1- | (1-R) ≥ R; where R is the reliability coefficient for each class of service, the unreliable coefficients R for all services are multiplied until the reliability coefficient R is met.
Based on the dynamic planning and statistical principle, the invention performs link allocation according to the service request type number of each access node in a short enough time, then dynamically backtracks link reallocation, and judges whether to exchange network stability and energy conservation by sacrificing certain network delay on the premise of ensuring end-to-end service according to the current network energy consumption requirement; the method solves the problem of comprehensive optimization of time delay and energy consumption of dynamic deployment of service function chains under a multi-portal network, obtains topology information of the current network through an SDN switch and stores the topology information as an adjacency matrix/adjacency list, supposes that a plurality of Access Points (AP) of the network send out a plurality of network service requests simultaneously within a specified extremely short time tau, preferentially deploys service requests with high weight by counting the types and frequency of the service requests, distributing weight values of different service requests according to the counting result, and combines some services according to the energy consumption requirement to achieve the balance and optimization of time delay and energy consumption, thereby reducing the network computing resource consumption.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (4)

1. A multi-portal network service function chain optimization method is characterized by comprising the following steps:
A. initializing a network environment, acquiring a physical network, an Access Point (AP) of a multi-entry network and a Content Provider (CP), and setting a set of service request types and corresponding request frequency vectors in a time period tau;
B. judging whether a network request exists or not; if yes, classifying the service requests in the time period tau according to the entry numbers and the service request types, and establishing an entry set and a service request type set; if not, performing the step E;
C. b, sorting the various service requests in the step A according to the weight vectors to obtain an ordered set which is increased in number, and outputting service request strategy type sequence vectors;
D. performing service function deployment and energy consumption optimization processing by adopting a Parallels-merge algorithm; the method specifically comprises the following steps:
d1, judging whether a service request needs to be deployed at present; if yes, go to step D2; if not, the operation is ended;
d2, acquiring the quantity of each entry request, a service request type set, an entry set and a service request strategy type sequence vector;
d3, according to the sequence of the service requests provided by the service request strategy type sequence vector, mapping the ordered vectors in the step B to a physical network by adopting a K shortest path algorithm based on Dijkstra algorithm to obtain a plurality of shortest path sets P;
d4, judging whether the shortest-circuit set P is an empty set; if yes, outputting error information that the network capacity is full; if not, go to step D5;
d5, judging whether the number of nodes contained in the shortest path set P is larger than the number of virtual services required by the current service request type; if yes, go to step D6; if not, returning to the step D3;
d6, merging the shortest circuits in the shortest circuit set by merging the same virtual service types by adopting a backtracking method, calculating the shortest circuits of the content providers of the access ports after merging, calculating the time delay and the energy consumption from the access ports to the content providers, and storing the shortest circuits and the energy consumption into a Dvec matrix and an Evec matrix;
d7, judging whether the Dvec matrix and the Edec matrix are both empty sets; if so, ending the operation; if not, go to step D8;
d8, judging whether the increased time delay is smaller than the reduced energy consumption; if yes, merging the virtual service functions; if not, returning to the step D7;
d9, updating the network topology edge weight matrix, and judging whether the network reliability is greater than a reliability threshold value; if so, ending the operation; if not, returning to the step D7;
E. judging whether undeployed virtual network functions exist or not; if yes, returning to the step B; if not, the operation ends.
2. The method of claim 1, wherein the weight vector in step C is represented by:
Wk＝pk×fk
wherein, WkAs weight vectors, pkFor the kth element, f, in the set of service request types within the time period τkThe kth element in the frequency vector is requested.
3. The method for optimizing service function chains of a multi-portal network as claimed in claim 2, wherein the shortest path of the merged portal content provider is calculated in step D6 and expressed as:
d(AP,CP)＝d(AP,vi)+d(vi,CP)
where d (AP, CP) represents the shortest distance between the access point AP and the content provider CP, d (AP, v)i) Indicating from AP to node v where some virtual service function is placediD (v) ofiCP) denotes a node v in which a certain virtual service function is placediDistance to CP.
4. The multi-portal network service function chain optimization method as claimed in claim 3Wherein step D6 further comprises converting D (AP, v)i) And d (AP, v)j)+l(ei,j) And (3) comparison:
when d (AP, v)i)＝d(AP,vj)+l(ei,j) In (v) isj)＝In(vi)；
When d (AP, v)i)＞d(AP,vj)+l(ei,j) In (v) isj)＝In(vi)+d(AP,vj)-d(AP,vi)+l(ei,j)；
When d (AP, v)i)＜d(AP,vj)+l(ei,j) In (v) isj)＝In(vi)+2l(ei,j)；
Wherein d (AP, v)j) Indicating from AP to node v where some virtual service function is placedjThe distance of (a) to (b),
in (-) is the delay increment, l (e)i,j) A policy band loop is programmed for the current service chain.
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