CN109818865A - A kind of SDN enhancing path boxing apparatus and method - Google Patents
A kind of SDN enhancing path boxing apparatus and method Download PDFInfo
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Abstract
A kind of SDN enhancing path boxing apparatus and method, for solving based on the bin packing occurred in SDN controller path-calculating element.The present invention has comprehensively considered the relevant constraints such as bandwidth resources in communication network, disturbance, time delay, use particle swarm algorithm, in conjunction with SDN controller network topology planning module the relevant technologies, intelligent adjustment service path planning provides vanning scheme to purport in new business bandwidth resources deficiency situation in a network, belongs to telecommunication network intelligent algorithm optimization field.Main innovation point includes: 1, the layout scheme of network vanning business based on SDN technology；2, the network service scheduling based on SDN is introduced in vanning decision model；3, network global bandwidth utilization rate is improved using particle swarm algorithm model.
Description
Technical field
The invention belongs to telecommunication network intelligent algorithms to optimize field, and in particular to a kind of SDN enhancing path boxing apparatus and
Method.
Background technique
Under present communications network environment, bandwidth resources are the resources of most critical in network.When network load is unbalanced, i.e.,
Localized network bandwidth utilization rate is excessively high in network, and rest part bandwidth utilization rate is too low, can cause unnecessary network congestion
And the wasting of resources, and appearing in can not be legal to Added Business calculating in the current enough situations of overall network bandwidth resources
Path.In order to realize the resource rational utilization of network, it can be used and be distributed by the resource of some intelligent algorithm regulating networks, and
Optimal path allocation scheme is calculated, i.e. global optimization adjustment has disposed the communication link of business in a network, risen whole net
New business is able to the Internet resources disposed out, and to the rate of excitation of whole network service minimum.
In traditional router solution, the calculating of service path mainly passes through hop count, delay, shared bandwidth resources
With link cost be constraint under conditions of shortest-path first algorithm realize.The defect of traditional scheme is: if (1) from
The flow of one source node to destination node has been more than the capacity of shortest path, and shortest path will become congestion, but simultaneously this two
There may be a longer path not used sufficiently between point；(2) in the shortest path from not source node at one
In the case that chain road is overlapped, if the total flow for passing through the link has been more than the capacity of the link, congestion will occur.
With the increase of networking client, existing network size is skyrocketed through, and network congestion is also further serious.
Particle swarm algorithm (Particle swarm optimization, PSO) is the optimization algorithm based on population.It has
Have fast convergence and it is easy to operate the features such as, be used widely in numerous areas such as engineering, economic managements, become intelligence computation
The new hot spot of area research.Particle swarm optimization algorithm is that one kind is succinct with form, convergence is quick and parameter adjustment mechanism is flexible
The advantages that evolution algorithm, and be successfully applied to single-object problem, it is considered to be solve multi-objective optimization question
One of most potential method.
Summary of the invention
The present invention aiming at the shortcomings in the prior art, provides a kind of SDN enhancing path boxing apparatus and method, solve towards
The technical issues of network load Added Business is cased in SDN communication network.
To achieve the above object, the invention adopts the following technical scheme:
A kind of SDN enhancing path boxing apparatus characterized by comprising network service control centre, the network service
Control centre is that the module of Base communication and management function is provided for system, and core component is SDN controller；Network service tune
The component interface at degree center includes: the northbound interface for transmitting customized parameter, for transmitting packet-in or packet-
The packet interfaces of out message, for issuing flow table or forwarding the flow table interface of flow table, for obtaining the interface of network topology；
The functional module of network service control centre includes: network topology identification module, business load evaluation module, network router-level topology
Module；
The business load evaluation module is assessed the indication information entered in network, real by big data technology
It is existing, a set of operational indicator large database concept is constructed in network service control centre, thus according to business information rapid evaluation business
Index；
The routing calculation module is network-based topology information and Network Security Device information, calculates one from number
According to traffic ingress to forwarding device, then the flow path exported to data traffic.
To optimize above-mentioned technical proposal, the concrete measure taken further include:
Further, the SDN controller uses Floodlight, POX or NOX；The indication information includes data flow
Size, time delay, the transmission rate of amount.
Meanwhile the invention also provides a kind of packing method of SDN enhancing path boxing apparatus as described above, features
It is, includes the following steps:
Step 1: the data traffic of Added Business needs to request the Internet resources in production environment in the network of work at present
When, if the case where inadequate resource, sending newly-increased network service to network service control centre and loading request, network service scheduling
After center receives business loading request, starting network service path vanning mechanism；
Step 2: after starting network service path vanning mechanism, OpenFlow interchanger is to network service control centre
In business load evaluation module send data traffic mirror image, distinguishing indexes information；
Step 3: after the SDN controller in network service control centre receives business loading request by northbound interface,
Traffic ingress, flowexit and carry Network switch nodes into network issue mirror image flow table；
Step 4: operation k shortest path first is every and the business disposed is needed to calculate a plurality of backup path, then will
These backup paths run particle swarm algorithm as input again, calculate the preferred plan for placing new business into network, protect
Demonstrate,proving new business can be added in SDN network, while small as far as possible to business disturbance has been disposed；
Step 5: after network service control centre calculates the scheduling routing of outflow, by SDN controller by flow table issuance
On OpenFolw interchanger on to the routing, the data transfer equipment in routing is connected into a data link；
Step 6: after receiving flow table, data traffic is drained to corresponding switch device by OpenFlow interchanger
On, interchanger adjustment is unsatisfactory for the path of desired Business Stream, modifies the flow entry for the SDN switch that the Business Stream is passed through,
Simultaneously by newly added sending down service to the forwarding in the flow table of respective switch, allowing the interchanger to complete new business.
To optimize above-mentioned technical proposal, the concrete measure taken further include:
Further, the calculating backup path in the step 4, referring to increasing one or more business newly can not be straight
When socket part is affixed one's name to, new business is cased into production network by the global adaptation based on particle swarm algorithm.
Further, the particle swarm algorithm in the step 4, specific as follows:
The coding mode of population uses natural number coding, and code length is only related to business number, spare with business
Path number is unrelated in path set, and the backup path number of business is only related to solution space, the position x of a particleiIt is by each
The backup path collection of a business selects a paths to constitute at random, in the position of i-th of particle representative of population, business k
Select that backup path is concentratedA routing, then
In this D dimension space, m particle is shared, i.e. the scale of population is m；The position particle i:Particle i speed:1≤i≤m；The history desired positions that particle i is lived through:The desired positions that all particles are lived through in group:
There are two data fields of p array and v array in particle swarm algorithm in individual Position, respectively represents position and speed
Degree, p array represent the position currently solved, Xie Wei n dimension, and n represents existing number of services；Reality of the particle swarm algorithm to bin packing
It is now that global adaptation is carried out to the backup path in existing net, the length of solution is identical with the existing number of network service, solves corresponding position
Numerical value refers to that the operating path of corresponding service chooses (0 ..., n), and algorithm steps are as follows:
Step 1) initializes population, and each position of array takes 0~n, n to indicate the quantity of current business at random, solution
Dimension is determined by number of services；
Step 2) is according to the pBest of velocity location iterative formula more new individuali, gBest is updated further according to the overall situation, wherein
pBestiIndicate individual particles i history optimal solution, gBest indicates current global optimal solution；
Step 3) enters iterative cycles, jumps out circulation when solving convergence；
Step 4) exports optimal solution gBest；
Final solution is exactly path sequence number in existing net, and fitness function is determined by disturbance number in iterative process.
Meanwhile the invention also provides a kind of packing method of SDN enhancing path boxing apparatus as described above, features
It is, includes the following steps:
Step 101: new business enters system, when first data grouping of a new business enters SDN, reaches SDN
Interchanger executes step 102；
The data grouping or data packet header are sent to SDN controller by step 102:SDN interchanger, execute step
104；
Step 103:SDN controller collects all business information and resource information in the network of its control, comprising: hands over
The service condition of flow entry, network node resource, the bandwidth availability ratio of link, the topology of business and the money of business in changing planes
Source demand, enters step 105；
Step 104:SDN controller starts PCE, calculates the road that the business needs to pass through by calculating or tactical management
The routing information of the business is added in the flow entry of SDN switch by diameter if successfully calculating outbound path, completes the industry
The deployment of business；If SDN controller can not find suitable routed path, step 103 is executed；
Step 105:SDN controller starts PCE, runs a plurality of backup path that genetic algorithm generates every business first,
Then particle swarm algorithm is reused using these paths as input, calculates the best side being placed into these business in network
Case, to guarantee that new business can be added in SDN network, and it is minimum to the disturbance of other business in network, realize load
Equilibrium enters step 106；
Calculated new configuration information is transferred to the SDN switch in network by step 106:SDN controller, into step
Rapid 107；
After step 107:SDN interchanger receives configuration information, adjustment is unsatisfactory for the path of desired Business Stream, modifies the industry
The flow entry of passed through SDN switch is flowed in business, while newly added sending down service is allowed this in the flow table of respective switch
The forwarding of interchanger completion new business；
Step 108: completing the deployment of new business and complete the load balancing of SDN network.
Meanwhile the invention also provides a kind of packing method of SDN enhancing path boxing apparatus as described above, features
It is, includes the following steps:
Step 201: Added Business enters in network and requests Internet resources, firstly evaluates each item constraint item of Added Business
Part and required resource situation；
Step 202: load evaluation, starting network service vanning before first determine whether current residual Internet resources whether
Meet the direct calculating in Added Business path, if resource satisfaction enters step 203；
Step 203: path computing, operation ksp algorithm calculate the path of Added Business, are meeting all-network resource peace treaty
Both Added Business path can be directly calculated when beam condition；
Step 204: incasement operation calls that is, when the resources supplIes of current network are not able to satisfy the demand of Added Business
Bin packing algorithm adjusts the business disposed to meet the resource requirement of Added Business；
Step 205: network service path adjustment, adjustment finish all old business and for Added Business calculate it is all can
After set of paths, starts the old business and newly-increased business that formal deployment needs to adjust, complete entire incasement operation.
To optimize above-mentioned technical proposal, the concrete measure taken further include:
Further, in the step 204, bin packing is solved using particle swarm algorithm, the specific steps are as follows:
Step 301: initialization a group particle, population size m, including random site p and speed v；
Step 302: evaluating the fitness fitness of each particle；
Step 303: to each particle, by its target function value compared with its desired positions passed through pBest, if compared with
It is good, then as current desired positions pBest；
Step 304: finding the highest particle of target function value, i.e., best position gBest in current population；
Step 305: adjusting particle speed and position according to the following formula；
In formula, t indicates the number of iterations, and ω indicates inertia weight, c1、c2Indicate accelerator coefficient, respectively indicate individual study because
Son and social learning's factor, ri、r2Indicate equally distributed random number, pBest on [0,1]iIndicate the history of individual particles i
Optimal solution, gBest indicate current global optimal solution, vi(i) speed of particle i when the number of iterations is t, v are indicatedi(t+1) it indicates
The speed of particle i, x when the number of iterations is t+1i(t) position of particle i when the number of iterations is t, x are indicatedi(t+1) iteration time is indicated
The position of particle i when number is t+1；
Step 306: not up to termination condition then goes to step 302, and stopping criterion for iteration is selected as maximum according to particular problem and changes
The optimal location that generation number or Particle Swarm search so far meets predetermined minimum adaptation threshold value；
It is solved in optimization problem with particle swarm algorithm, particle is made of position vector and velocity vector, according to objective function
To calculate the adaptive value of current position；In each speed iteration, particle according to the history of oneself in addition to preferably recording
Except being learnt, learnt also according to particle optimal in current population, is flown so that it is determined that how to adjust next time and change
Capable speed, direction finally determine that the particle completes the position vector after an iteration；
In population bin packing algorithm, the quality between particle position is compared by target value, every generation population
pBestiAnd it gBest and updates by comparing the target function value between particle；It is required under present networks problem model
It can ask minimum by the ratio of disturbance business number and total business number, therefore choose following objective function:
In formula, θ is the rate of excitation that business is subject to；As ω > 1, indicate link bandwidth overload, i.e., current network resources without
Method carries all business now netted, so such particle objective function is set as -1, when a particle target function value is -1
When, this particle is exactly infeasible solution.
The beneficial effects of the present invention are: proposing a kind of enhancing path based in SDN controller based on particle swarm algorithm
Packing method obtains a kind of efficient path loading strategy, Neng Gou in conjunction with the flexible deployment ability of SDN path-calculating element
In the complex network environment of multiple constraint, personalized service deployment service is provided, meanwhile, by calculating lower network cost most
Shortest path improves the router-level topology efficiency and overall bandwidth resource utilization of network service.
Detailed description of the invention
Fig. 1 is SDN network enhancing path bin packing basic framework figure.
Fig. 2 is packing method flow chart.
Fig. 3 is the flow diagram that particle swarm algorithm solves bin packing.
Specific embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.
A kind of SDN provided by the invention enhances path boxing apparatus, comprising:
Network service control centre refers to providing the module of Base communication and management function, core component for system
It is SDN controller, such as Floodlight, POX, NOX.Component interface specifically includes that the 1, north orientation for transmitting customized parameter
Interface；2, for transmitting the packet interfaces of packet-in packet-out message；3, for issuing flow table or forwarding flow
The flow table interface of table；4, for obtaining the interface of network topology.Its main functional module includes: the identification of network topology, industry
Business load evaluation, network router-level topology etc..
Business load assessment refers to commenting the indication informations such as size, the time delay of data traffic entered in network
Estimate, which can realize using by technologies such as existing big datas, and a set of industry is constructed in network service control centre
Business index large database concept, so as to according to business information rapid evaluation operational indicator.
Router-level topology refers to network-based topology information and Network Security Device information, calculates one from data
Traffic ingress is to forwarding device, then the flow path exported to data traffic.
A kind of SDN provided by the invention enhances path packing method, comprising:
Step 1: the data traffic of Added Business needs to request the Internet resources in production environment in the network of work at present
When, if the case where inadequate resource, newly-increased network service at this time being sent to network service control centre and loads request, network
It, will starting network service path vanning mechanism after traffic scheduling center receives business loading request.
Step 2: after starting network service path vanning mechanism, OpenFlow interchanger will be into network service scheduling
Business load evaluation module in the heart sends data traffic mirror image, identifies the indication informations such as size, the transmission rate of data traffic.
Step 3: after the SDN controller in network service control centre receives business loading request by northbound interface,
Traffic ingress, flowexit and carry Network switch nodes into network issue mirror image flow table.
Step 4: operation k shortest path first is every and the business disposed is needed to calculate a plurality of backup path, then will
These backup paths run particle swarm algorithm as input again, calculate the preferred plan for placing new business into network, protect
Demonstrate,proving new business can be added in SDN network, while small as far as possible to business disturbance has been disposed.Wherein, spare road is calculated
When diameter refers to that increasing one or more business newly can not directly dispose, by the global adaptation based on particle swarm algorithm by new industry
Business vanning is into production network.
Step 5: after network service control centre calculates the scheduling routing of outflow, by SDN controller by flow table issuance
On OpenFolw interchanger on to the routing, the data transfer equipment in routing is connected into a data link.
Step 6: after receiving flow table, data traffic is drained to corresponding switch device by OpenFlow interchanger
On, interchanger adjustment is unsatisfactory for the path of desired Business Stream, modifies the flow entry for the SDN switch that the Business Stream is passed through,
Simultaneously by newly added sending down service to the forwarding in the flow table of respective switch, allowing the interchanger to complete new business.
Before the above method, business load optimization is carried out, basic step is as follows:
A, Added Business data input: occurring new flow business demand in network, the measurement of Network status includes logarithm
According to being pre-processed, such as the bandwidth resources of network, number of nodes, time delay etc.；On the other hand it will fill to having calculated outbound path
It is loaded into the data analysis of the business into network, such as start node, the tool of the link bandwidth needed and hop count no more than etc.
Body description.
B, optimization aim establishment, modeling and analysis: to actual network characteristic and flow business feature founding mathematical models,
Where finding out the factor for influencing network performance, network performance, network operation control, network design are promoted.
C, optimum results are exported: on abstract and analysis foundation, controlling behavior pair is being passed through to current network and transmission demand
Deployed service path is adjusted, and reaches the optimal target of global load balancing.
The path SDN enhancing packing method of the invention and device are extracted, deployment by Added Business request, business constraint
Traffic scheduling issues triggering vanning mechanism；Then k shortest path first generates backup path, then calculates industry based on particle swarm algorithm
Business flow loading strategy；Complete issuing for scheduling strategy in last traffic scheduling center.More than packing method direct in conventional method
Further, the load disturbance degree that can more easily understand and compare business vanning front and back network, can accomplish balanced, low disturb
Dynamic vanning.
The currently used coding techniques of the coding mode of population has a binary coding, natural number coding, floating-point encoding,
Character code etc..Here gather bin packing proposed in this paper using natural number coding, code length and business number
Correlation concentrates path number unrelated with the backup path of business, and the backup path number of business is only related to solution space.One grain
The position x of soniIt is to select a paths to constitute at random by the backup path collection of each business, such as in i-th of particle of population
In the position of representative, in the trim set of business k selection theA routing, then
In this D dimension space, m particle is shared, i.e. the scale of population is m；The position particle i:Particle i speed:The history desired positions that 1≤i≤m, particle i are lived through:The desired positions that all particles are lived through in group (or in field):
There are two data fields of p array and v array in particle swarm algorithm in individual Position, respectively represents position and speed
Degree, p array represent the position currently solved, and solution is that n is tieed up in this algorithm, and n represents existing number of services；Particle swarm algorithm is to dress
The realization of case problem is based on previously mentioned vanning model, is equally to carry out global adaptation to the backup path in existing net, solution
Length is identical with the existing number of network service, and the numerical value for solving corresponding position refers to that the operating path of corresponding service chooses (0 ..., n),
Algorithm steps are as follows:
Step 1) initializes population, and each position of array takes 0~n, n to indicate the quantity of current business at random, solution
Dimension is determined by number of services.
Step 2) is according to the pBest of velocity location iterative formula more new individuali, gBest is updated further according to the overall situation, wherein
pBestiIndicate individual particles i history optimal solution, gBest indicates current global optimal solution.
Step 3) enters iterative cycles, jumps out circulation when solving convergence.
Step 4) exports optimal solution gBest.
Final solution is exactly path sequence number in existing net, and fitness function is determined by disturbance number in iterative process.
Since rate of excitation is objective function in this problem, so the work at present routing information element of existing network service is set as
1.5, other backup paths are set as 1, can increase the probability for choosing work at present path in this way, can accelerate to restrain.
Fig. 1 gives SDN network enhancing path bin packing basic framework figure.As shown in the figure, a simplification is used
Model be explicitly described the packing method in the present invention, SDN network enhances the execution of path bin packing basic framework
Journey is divided into the following steps:
Step 101: new business enters system；When first data grouping of one new business enters SDN, SDN is reached
Interchanger executes step 102.
The data grouping or data packet header are sent to SDN controller by step 102:SDN interchanger, execute step
104。
Step 103:SDN controller collects all business information and resource information in the network of its control: interchanger
In flow entry, the service condition of network node resource, the bandwidth availability ratio of link, the topology of business and business resource need
It asks.Enter step 105.
Step 104:SDN controller starts PCE, calculates the road that the business needs to pass through by calculating or tactical management
The routing information of the business is added in the flow entry of SDN switch by diameter if successfully calculating outbound path, completes the industry
The deployment of business；If SDN controller can not find suitable routed path, step 103 is executed.
Step 105:SDN controller starts PCE, runs a plurality of backup path that genetic algorithm generates every business first,
Then particle swarm algorithm is reused using these paths as input, calculates the best side being placed into these business in network
Case, to guarantee that new business can be added in SDN network, and it is minimum to the disturbance of other business in network, realize load
Equilibrium enters step 106.
Calculated new configuration information is transferred to the SDN switch in network by step 106:SDN controller, into step
Rapid 107.
After step 107:SDN interchanger receives configuration information, adjustment is unsatisfactory for the path of desired Business Stream, modifies the industry
The flow entry of passed through SDN switch is flowed in business, while newly added sending down service is allowed this in the flow table of respective switch
The forwarding of interchanger completion new business.
Step 108: completing the deployment of new business and complete the load balancing of SDN network.
Fig. 2 gives the flow chart of the packing method in the present invention, is specifically divided into the following steps:
Step 201: Added Business enters in network and requests Internet resources, firstly evaluates each item constraint item of Added Business
Part and required resource situation.
Step 202: load evaluation, starting network service vanning before first determine whether current residual Internet resources whether
Meet the direct calculating in Added Business path, if resource satisfaction enters step 203.
Step 203: path computing, operation ksp algorithm calculate the path of Added Business, are meeting all-network resource peace treaty
Added Business path can be directly calculated when beam condition.
Step 204: incasement operation calls that is, when the resources supplIes of current network are not able to satisfy the demand of Added Business
The bin packing algorithm of this paper adjusts the business disposed to meet the resource requirement of Added Business.
Step 205: network service path adjustment, adjustment finish all old business and for Added Business calculate it is all can
After set of paths, starts the old business and newly-increased business that formal deployment needs to adjust, complete entire incasement operation.
Fig. 3 gives the flow diagram that particle swarm algorithm solves bin packing, is specifically divided into the following steps:
Step 301: initialization a group particle (population size m), including random site p and speed v.
Step 302: evaluating the fitness fitness of each particle.
Step 303: to each particle, by its target function value compared with its desired positions passed through pBest, if compared with
It is good, then as current desired positions pBest.
Step 304: finding position gBest best in the i.e. current population of the highest particle of target function value.
Step 305: particle speed and position are adjusted according to formula:
In formula, t indicates the number of iterations, and ω indicates inertia weight, c1、c2Indicate accelerator coefficient, respectively indicate individual study because
Son and social learning's factor, r1、r2Indicate equally distributed random number, pBest on [0,1]iIndicate individual particles i history most
Excellent solution, gBest indicate current global optimal solution；vi(t) speed of particle i when the number of iterations is t, v are indicatedi(t+1) it indicates to change
The speed of particle i when generation number is t+1；xi(t) position of particle i when the number of iterations is t, x are indicatedi(t+1) the number of iterations is indicated
The position of particle i when for t+1.
Step 306: not up to termination condition then goes to step 302.
Stopping criterion for iteration according to particular problem is typically chosen as maximum number of iterations or Particle Swarm searches so far
Optimal location meets predetermined minimum adaptation threshold value.
It is solved in optimization problem with particle swarm algorithm, particle is by position vector (particle is in the position of solution space) and speed
Vector constitutes (direction and the speed that determine flight next time), and the adaptive value that current position is calculated according to objective function (can
To be interpreted as distance of the small bird apart from food).In each speed iteration, particle according to the history of oneself in addition to can preferably record
Except being learnt, it can also be learnt according to particle optimal in current population, so that it is determined that how next time adjusts and change
The speed of flight, direction finally determine that the particle completes the position vector after an iteration.
In population bin packing algorithm, what the quality between particle position was compared by target value, every generation population
PBestiAnd it gBest and updates by comparing the target function value between particle.Under present networks problem model
It is required to ask minimum by the ratio of disturbance business number and total business number, therefore chooses following objective function:
In formula, θ is the rate of excitation that business is subject to.It is necessary to be noted that indicating that link bandwidth is super as ω > 1
It carries, i.e., current network resources can not carry all business of existing net, so such particle objective function is set as -1, when one
When particle target function value is -1, this particle is exactly infeasible solution.
In this case, load balancing is not only met by PSO Algorithm out, but also is able to satisfy Added Business deployment
Solution.Above-mentioned optimization problem is substantially NP-hard problem, and solution is extremely difficult, and is not had also at present
There is suitable multinomial algorithm to solve this problem, generally tends to comprehensively consider efficiency of algorithm and optimum results in engineering, seek
Seek the satisfactory solution of problem.The problem of this kind of multiple shot array of traditional Optimization Method such as branch-and-bound, Dynamic Programming, even if right
In the lesser problem of scale, operation time is also to be difficult to receive, needless to say for fairly large practical problem.Cause
This, people generally use heuritic approach to solve real-life complete problem.The characteristics of above-mentioned optimization problem is search
Space is big, it is contemplated that using can be solved with the particle swarm algorithm of fast convergence.
The above is only the preferred embodiment of the present invention, protection scope of the present invention is not limited merely to above-described embodiment,
All technical solutions belonged under thinking of the present invention all belong to the scope of protection of the present invention.It should be pointed out that for the art
For those of ordinary skill, several improvements and modifications without departing from the principles of the present invention should be regarded as protection of the invention
Range.
Claims (8)
1. a kind of SDN enhances path boxing apparatus characterized by comprising network service control centre, the network service tune
Degree center is that the module of Base communication and management function is provided for system, and core component is SDN controller；Network service scheduling
The component interface at center includes: the northbound interface for transmitting customized parameter, for transmitting packet-in or packet-out
The packet interfaces of message, for issuing flow table or forwarding the flow table interface of flow table, for obtaining the interface of network topology；Network
The functional module at traffic scheduling center includes: network topology identification module, business load evaluation module, network router-level topology mould
Block；
The business load evaluation module is assessed the indication information entered in network, is realized by big data technology,
A set of operational indicator large database concept is constructed in network service control centre, thus according to business information rapid evaluation operational indicator
Property；
The routing calculation module is network-based topology information and Network Security Device information, calculates one from data flow
Entrance is measured to forwarding device, then the flow path exported to data traffic.
2. a kind of SDN as described in claim 1 enhances path boxing apparatus, it is characterised in that: the SDN controller uses
Floodlight, POX or NOX；The indication information includes the size, time delay, transmission rate of data traffic.
3. a kind of packing method of SDN enhancing path boxing apparatus as described in claim 1, which is characterized in that including as follows
Step:
Step 1: when the data traffic of Added Business needs to request the Internet resources in production environment in the network of work at present,
If the case where inadequate resource, newly-increased network service loading is sent to network service control centre and is requested, in network service scheduling
After the heart receives business loading request, starting network service path vanning mechanism；
Step 2: after starting network service path vanning mechanism, OpenFlow interchanger is into network service control centre
Business load evaluation module sends data traffic mirror image, distinguishing indexes information；
Step 3: after the SDN controller in network service control centre receives business loading request by northbound interface, to net
Traffic ingress, flowexit and carry Network switch nodes in network issue mirror image flow table；
Step 4: operation k shortest path first is every and the business disposed is needed to calculate a plurality of backup path, then by these
Backup path runs particle swarm algorithm as input again, calculates the preferred plan for placing new business into network, guarantees new
Business can be added in SDN network, while to disposed business disturbance it is small as far as possible；
Step 5: network service control centre calculate outflow scheduling routing after, by SDN controller by flow table issuance arrive this
On OpenFolw interchanger in routing, the data transfer equipment in routing is connected into a data link；
Step 6: after receiving flow table, data traffic is drained on corresponding switch device by OpenFlow interchanger, is handed over
Adjustment of changing planes is unsatisfactory for the path of desired Business Stream, modifies the flow entry for the SDN switch that the Business Stream is passed through, and simultaneously will
Newly added sending down service is to the forwarding in the flow table of respective switch, allowing the interchanger to complete new business.
4. packing method as claimed in claim 3, it is characterised in that: the calculating backup path in the step 4 refers to
When increasing one or more business newly can not directly dispose, new business vanning is arrived by the global adaptation based on particle swarm algorithm
It produces in network.
5. packing method as claimed in claim 4, it is characterised in that: the particle swarm algorithm in the step 4, specific as follows:
The coding mode of population uses natural number coding, code length, backup path with business related to business number
Concentrate path number unrelated, the backup path number of business is only related to solution space, the position x of a particleiIt is by each industry
The backup path collection of business selects a paths to constitute at random, in the position of i-th of particle representative of population, business k selection
Backup path concentrate theA routing, then
In this D dimension space, m particle is shared, i.e. the scale of population is m；The position particle i:Grain
Sub- i speed:The history desired positions that particle i is lived through:Group
The desired positions that interior all particles are lived through:
There are two data fields of p array and v array in particle swarm algorithm in individual Position, respectively represents position and speed, p number
Group represents the position currently solved, Xie Wei n dimension, and n represents existing number of services；Particle swarm algorithm is pair to the realization of bin packing
Now the backup path in net carries out global adaptation, and the length of solution is identical with the existing number of network service, and the numerical value for solving corresponding position is
Refer to that the operating path of corresponding service chooses (0 ..., n), algorithm steps are as follows:
Step 1) initializes population, and each position of array takes 0~n, n to indicate the quantity of current business, the dimension of solution at random
It is determined by number of services；
Step 2) is according to the pBest of velocity location iterative formula more new individuali, gBest is updated further according to the overall situation, wherein pBesti
Indicate individual particles i history optimal solution, gBest indicates current global optimal solution；
Step 3) enters iterative cycles, jumps out circulation when solving convergence；
Step 4) exports optimal solution gBest；
Final solution is exactly path sequence number in existing net, and fitness function is determined by disturbance number in iterative process.
6. a kind of packing method of SDN enhancing path boxing apparatus as described in claim 1, which is characterized in that including as follows
Step:
Step 101: new business enters system, when first data grouping of a new business enters SDN, reaches SDN exchange
Machine executes step 102；
The data grouping or data packet header are sent to SDN controller by step 102:SDN interchanger, execute step 104；
Step 103:SDN controller collects all business information and resource information in the network of its control, comprising: interchanger
In flow entry, the service condition of network node resource, the bandwidth availability ratio of link, the topology of business and business resource need
It asks, enters step 105；
Step 104:SDN controller starts PCE, calculates the path that the business needs to pass through by calculating or tactical management, such as
Fruit successfully calculates outbound path, then the routing information of the business is added in the flow entry of SDN switch, completes the portion of the business
Administration；If SDN controller can not find suitable routed path, step 103 is executed；
Step 105:SDN controller starts PCE, runs a plurality of backup path that genetic algorithm generates every business first, then
Particle swarm algorithm is reused using these paths as input, calculates and these business is placed into the preferred plan in network, is come
Guarantee that new business can be added in SDN network, and minimum to the disturbance of other business in network, realize load balancing,
Enter step 106；
Calculated new configuration information is transferred to the SDN switch in network by step 106:SDN controller, is entered step
107；
After step 107:SDN interchanger receives configuration information, adjustment is unsatisfactory for the path of desired Business Stream, modifies the Business Stream
The flow entry for the SDN switch passed through, while by newly added sending down service in the flow table of respective switch, allowing the exchange
The forwarding of machine completion new business；
Step 108: completing the deployment of new business and complete the load balancing of SDN network.
7. a kind of packing method of SDN enhancing path boxing apparatus as described in claim 1, which is characterized in that including as follows
Step:
Step 201: Added Business enters in network and requests Internet resources, firstly evaluate Added Business every constraint condition and
Required resource situation；
Step 202: load evaluation first determines whether the Internet resources of current residual meet before starting network service vanning
The direct calculating in Added Business path, if resource satisfaction enters step 203；
Step 203: path computing, operation ksp algorithm calculate the path of Added Business, are meeting all-network resource and constraint item
Both Added Business path can be directly calculated when part；
Step 204: incasement operation calls vanning that is, when the resources supplIes of current network are not able to satisfy the demand of Added Business
Algorithm adjusts the business disposed to meet the resource requirement of Added Business；
Step 205: the adjustment of network service path, adjustment finish all old business and calculate all available roads for Added Business
After diameter set, starts the old business and newly-increased business that formal deployment needs to adjust, complete entire incasement operation.
8. packing method as claimed in claim 7, it is characterised in that: in the step 204, solve to fill using particle swarm algorithm
Case problem, the specific steps are as follows:
Step 301: initialization a group particle, population size m, including random site p and speed v；
Step 302: evaluating the fitness fitness of each particle；
Step 303: to each particle, by its target function value compared with its desired positions passed through pBest, if preferably,
As current desired positions pBest；
Step 304: finding the highest particle of target function value, i.e., best position gBest in current population；
Step 305: adjusting particle speed and position according to the following formula；
In formula, t indicates the number of iterations, and ω indicates inertia weight, c1、c2Indicate accelerator coefficient, respectively indicate individual Studying factors and
Social learning's factor, r1、r2Indicate equally distributed random number, pBest on [0,1]iIndicate that the history of individual particles i is optimal
Solution, gBest indicate current global optimal solution, vi(t) speed of particle i when the number of iterations is t, v are indicatedi(t+1) iteration is indicated
The speed of particle i, x when number is t+1i(t) position of particle i when the number of iterations is t, x are indicatedi(t+1) indicate that the number of iterations is
The position of particle i when t+1；
Step 306: not up to termination condition then goes to step 302, and stopping criterion for iteration is selected as greatest iteration time according to particular problem
The optimal location that several or Particle Swarm searches so far meets predetermined minimum adaptation threshold value；
It is solved in optimization problem with particle swarm algorithm, particle is made of position vector and velocity vector, is counted according to objective function
The adaptive value of current position；In each speed iteration, particle is in addition to preferably recording progress according to the history of oneself
Except study, learnt also according to particle optimal in current population, so that it is determined that how next time adjusts and change of flight
Speed, direction finally determine that the particle completes the position vector after an iteration；
In population bin packing algorithm, the quality between particle position is compared by target value, the pBest of every generation populationi
And it gBest and updates by comparing the target function value between particle；It is required to ask under present networks problem model
It is minimum by the ratio of disturbance business number and total business number, therefore choose following objective function:
In formula, θ is the rate of excitation that business is subject to；As ω > 1, link bandwidth overload is indicated, i.e., current network resources can not be held
All business now netted are carried, so such particle objective function is set as -1, when a particle target function value is -1, this
Particle is exactly infeasible solution.
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