CN110120892B - SDN multi-controller deployment method and system based on improved firefly algorithm - Google Patents
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Abstract
The utility model provides an SDN multi-controller deployment method and system based on an improved firefly algorithm, wherein K controllers and N switches are arranged in the whole SDN switch network, and each switch is controlled by only one controller; defining the average control time delay of the controller and the switches as the average value of the time delay from all the switches to the controller distributed to the switches, and taking the average value as a target function; obtaining an attraction function based on the average value, and taking the actual load utilization ratio between the maximum and minimum load utilization ratios of the controller as a constraint condition; and solving an objective function by utilizing an improved firefly algorithm, wherein for each firefly i, a target firefly is determined according to an attraction degree formula, the target firefly is moved according to a step length formula, the position of the firefly is solved, a controller to which each switch belongs is determined, and multi-controller deployment is realized.
Description
Technical Field
The disclosure relates to the technical field of networks, and in particular relates to an SDN multi-controller deployment method and system based on an improved firefly algorithm.
Background
With the continuous development of communication technology, the number of internet users is increasing, and more service requirements provide higher challenges for delay, packet loss rate and data rate, so that more and more problems are exposed to the traditional network architecture. In this case, the SDN network arises. The SDN decouples a control plane from a data plane, and the control capability of the network is improved by adopting a centralized control mode. The control plane is composed of controllers and is responsible for managing the whole network; while the data plane consists of simplified switches responsible for packet forwarding, the architecture makes the network easy to manage and upgrade, and the SDN network structure is shown in fig. 2.
For large networks, the management of a single controller is difficult to meet the requirements of all switches. Meanwhile, once a single controller fails, the whole network is broken down, which is costly, so that the distributed deployment of multiple controllers in the SDN network becomes a mainstream solution. The control capability of a large-scale SDN network can be obviously improved by deploying a plurality of controllers, but the cost of the network is determined by the number of the controllers, and the performance of the whole network is greatly influenced by the deployment scheme of the switches and the controllers. Among The multiple Controller deployment problems in Software Defined networks are proposed by Heller, "Heller, b., r.shell and n.mckeown, the Controller Placement protocol, am signal Computer Communication Review,2012.42 (4): p.473-478", which mainly takes into account The average latency between nodes and The load balancing of The controllers, and The SDN Network is used by ieee Transactions Network & Service Management,2017.12 (1): p.4-17. "uses simulated annealing Algorithm to solve SDN multiple Controller deployment Problem, while The SDN multiple Controller deployment Problem is used by Software Controller in" Software program ".
Firefly Algorithm (Firefly Algorithm) is a meta-heuristic Intelligent optimization Algorithm proposed by Yang et al in 2010, and the literature includes "X.S. Yang," Firefly Algorithm, levy weights and global optimization ", in Research and Development in Intelligent Systems XXVI. Springer,2010, pp.209-218", and "Yang, X.S., firefly Algorithm, stochastic Test function and Design optimization. International Journal of Bio-embedded Computation,2010.2 (2): p.78-84 (7)". It simulates the behavior of firefly seeking a couple to feed by fluorescence in nature. Compared with other intelligent optimization algorithms, the algorithm is easy to implement and simple to operate, and has been successfully applied in many fields.
Disclosure of Invention
The purpose of the embodiment of the specification is to provide an SDN multi-controller deployment method based on an improved firefly algorithm, which can ensure the lowest time delay and balanced load.
The embodiment of the specification provides an SDN multi-controller deployment method based on an improved firefly algorithm, and the SDN multi-controller deployment method is realized through the following technical scheme:
the method comprises the following steps:
setting K controllers and N switches in the whole SDN switch network, wherein each switch is controlled by only one controller;
defining the average control time delay of the controller and the switches as the average value of the time delay from all the switches to the controller distributed to the switches, and taking the average value as a target function;
obtaining an attraction function based on the average value, and taking the actual load utilization ratio between the maximum and minimum load utilization ratios of the controller as a constraint condition;
and solving an objective function by utilizing an improved firefly algorithm, wherein for each firefly i, a target firefly is determined according to an attraction degree formula, the target firefly is moved according to a step length formula, the position of the firefly is solved, a controller to which each switch belongs is determined, and multi-controller deployment is realized.
The embodiment of the specification provides an SDN multi-controller deployment system based on an improved firefly algorithm, and the SDN multi-controller deployment system is realized by the following technical scheme:
the method comprises the following steps:
setting K controllers and N switches in the whole SDN switch network, wherein each switch is controlled by only one controller;
a definition module configured to: defining the average control time delay of the controller and the switches as the average value of the time delay from all the switches to the controller distributed to the switches, and taking the average value as a target function;
obtaining an attraction function based on the average and having the actual load utilization between the maximum and minimum load utilization of the controller as a constraint;
an improved firefly algorithm solving module configured to: and solving an objective function by utilizing an improved firefly algorithm, wherein for each firefly i, a target firefly is determined according to an attraction degree formula, the target firefly is moved according to a step length formula, the position of the firefly is solved, a controller to which each switch belongs is determined, and multi-controller deployment is realized.
An embodiment of the present specification provides an SDN switch network system, which is implemented by the following technical solutions:
the method comprises the following steps:
the system comprises K controllers and N switches, wherein each switch is controlled by only one controller;
the K controllers are deployed by adopting the SDN multi-controller deployment method based on the improved firefly algorithm:
defining the average control time delay of the controller and the switches as the average value of the time delay from all the switches to the controller distributed to the switches, and taking the average value as a target function;
obtaining an attraction function based on the average value, and taking the actual load utilization ratio between the maximum and minimum load utilization ratios of the controller as a constraint condition;
and solving an objective function by utilizing an improved firefly algorithm, wherein for each firefly i, a target firefly is determined according to an attraction degree formula, the target firefly is moved according to a step length formula, the position of the firefly is solved, a controller to which each switch belongs is determined, and multi-controller deployment is realized.
Compared with the prior art, this disclosed beneficial effect is:
the present disclosure proposes an SDN multi-controller deployment algorithm that takes into account load balancing and average latency based on an improved firefly algorithm. The two-dimensional coordinates of controller deployment are given in a continuous two-dimensional space class, and the method can guarantee the minimum time delay and load balance when the multiple controllers are deployed in the SDN network based on the improved firefly algorithm.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a flow chart of a specific algorithm of an embodiment of the present disclosure;
figure 2 is a diagram of an SDN network architecture according to an embodiment of the present disclosure;
fig. 3 shows the K =3 classification result of the embodiment of the present disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
To better illustrate the technical solution of the present disclosure, first, an improved firefly algorithm is introduced
1. Defining the intensity of light
γ i Denotes the light absorption coefficient, I 0 Is the initial light intensity.
2. Degree of attraction
γ i Denotes the light absorption coefficient, beta 0 To initialize the attraction.
3. Distance between two adjacent devices
The cartesian distance is used;
4. moving mode
Wherein, beta 0 And alpha is givenA constant value is set, and the constant value,
shows [0,1]The random number of (a) is set,
the point multiplication operation is shown, and Levy refers to a Levy distribution and is specifically shown as
Lévy～μ＝t -λ ，(1＜λ≤3)
5. Improved part
The absorption coefficient controls the change of the light intensity and plays a very important role in determining the convergence speed and behavior of the algorithm. Theoretically, when the Gamma → 0, the beta = beta 0 Then the absorption coefficient at this point appears as a constant, i.e. the light emitted by the firefly does not decay with increasing distance, which allows the firefly to converge quickly to the global final position. However, when γ → ∞, β (r) = δ (r), i.e., a dirac function, which indicates that the attraction of firefly is close to 0, firefly is approximately searched randomly. And the firefly can search for the globally optimal variable. In order to enable the firefly algorithm to determine the position of the optimal solution quickly in the early stage, the method has strong local search capability in the later stage. The following adaptive adjustment formula is proposed:
in the formula: gamma ray b -an initial value;
γ e -final value, γ e >γ b ；
k-an adjustment parameter, k >0;
σ 2 variance between fireflies
The random coefficient largely affects the random movement of the firefly. Alpha is increased if the random searching capability of the firefly is to be enhanced, and the algorithm has stronger global searching capability at the moment; if the random searching capability of the firefly is reduced, alpha is reduced, and the algorithm has stronger local searching capability. The algorithm should have a strong global search capability at the early stage, and then alpha should be large, and the local search capability needs to be added at the later stage of the algorithm to improve the convergence rate of the algorithm. The following adaptive adjustment formula is proposed:
in the formula: alpha (alpha) ("alpha") b -an initial value;
α e -final value, α e <α b ；
k-an adjustment parameter, k >0;
σ 2 -variance between fireflies;
6. initial parameters are defined, an objective function is selected, and a general flow of an improved firefly algorithm is given:
the method comprises the following steps: setting an objective function f (x), x = (x) 1 ,...,x d ) T ；
Step two: generating a location x for initializing fireflies i I =1,2,. N, the intensity of light I generated by the light intensity formula;
step three: setting the initial light absorption coefficient gamma i Initial random coefficient α i Iteration times t and the number n of fireflies;
step four: for each firefly in the space, determining a target firefly according to an attraction degree formula, and moving towards the target firefly according to a moving step length formula;
step five: updating the formula gamma of the light absorption coefficient i And a random coefficient alpha i ；
Step six: arranging the positions of the fireflies and seeking the current best solution;
step seven: and (5) judging whether the iteration times t are reached, if so, outputting a final result, and otherwise, returning to the step four.
Example 1
The embodiment discloses an SDN multi-controller deployment method based on an improved firefly algorithm.
Defining:
(1) The collection of switches is represented as
V＝{v 1 ，v 2 ，...，v N }，s.t.v i ∈R n ，i＝1，2，...，N，
(2) The set of controllers is represented as
C＝{c 1 ，c 2 ，...，c N }，s.t.c i ∈R n ，i＝1，2，...，K，
(3) The set of switches controlled by controller j is SV j The number of the switches controlled by the controller j is N j The controller to which switch i belongs is denoted C (v) i ) Then, then
SV j ＝{v i ∈V：C(v i )＝c j }
C(v i )＝min d(v i ，c j )，s.t.c j ∈R n ，j＝1，2，...，K，
(4) The controller to which each switch belongs (also called Label of each switch) forms a set Label, then
Label＝＝{C(v 1 )，C(v 2 )，...，C(v i )}，s.t.v i ∈R n ，i＝1，2，...，N，
(5) Average control delay
In an SDN network, a controller is responsible for handling new flows uploaded by switches. In the OpenFlow protocol, when a new flow reaches a switch, the switch searches whether a flow table entry matched with the new flow exists in the flow table entries stored in the switch, and if the matched flow table entry exists, the switch operates according to a corresponding rule; if no matched flow table entry exists, the switch sends a packet _ in message to the controller, and the controller is required to respond to a new flow. When the controller completes processing the new flow, a packet _ out message is sent to the switch to inform the switch how to process the new flow, so that the forwarding of the new flow is completed. As can be seen from the working mechanism, frequent communication is performed between the switch and the controller, and therefore, the query delay from the switch to the controller, that is, the delay of the packet _ in message and the packet _ out message, is mainly considered.
The average control latency of a controller to a switch is defined as the average of the latencies of all switches to the controller assigned to it. Is expressed as follows
Where Delay denotes the average control Delay.
(6) Controller load utilization
The index constrains the load utilization of the controller. It is assumed that the performance of all controllers is the same, i.e. the maximum load rating of each controller is the same. Definition C max Is the maximum rated load of the controller, i.e. the maximum number of switches each controller can control. Then the actual load utilization of the jth controller is
Wherein
Is the actual load utilization of the jth controller.
The actual load utilization needs to be satisfied
Wherein, ratio min And ratio max Maximum and minimum load utilization of the controller, respectively.
(7) The position of the ith firefly is defined as the accumulation of two-dimensional coordinates of K controllers, namely two fireflies form a controller, namely the position of the ith firefly is a 2K-dimensional vector
Specifically, (a) 1 ，b 1 ) Is the two-dimensional coordinate of the first controller, (a) 2 ，b 2 ) Is the two-dimensional coordinate of the second controller, (a) k ，b k ) Is the two-dimensional coordinate of the kth control, and the superscript i indicates the ith firefly.
(8) Objective function, attraction function, and constraint condition
The objective function selects an average control Delay function,
the constraint condition is
r refers to the Cartesian distance between two fireflies, which is the Cartesian distance between i and j fireflies, for i fireflies, the target fireflies j is selected, and Delay should be the target function of j, that is, the value calculated by Delay.
In the formula: gamma ray b -an initial value;
γ e -final value, γ e >γ b ；
k is a regulating parameter, k >0;
σ 2 variance between fireflies
And, gamma i And obtained by iterative updating according to the above formula,
γ b ，γ e and k is a preset parameter.
In a specific implementation example, the SDN multi-controller deployment method based on the improved firefly algorithm specifically includes the following steps:
the method comprises the following steps: setting an initial condition;
an objective function Delay, an attraction function β (r),
setting the initial light absorption coefficient gamma i Initial random coefficient α i Iteration times t, the number of fireflies n,
constraint conditions
Generating n fireflies;
step two: according to the formula C (v) i )＝min d(v i ，c j )，s.t.c j ∈R n ，j＝1，2，...，K，
Calculating a Label;
step three: judging whether the fireflies are qualified or not according to the constraint conditions, and if not, entering a fifth step;
step four: carrying out random generation on unqualified fireflies again, and entering the third step;
step five: for each firefly i, determining a target firefly according to an attraction degree formula, and moving according to a step length formula;
step six: according to the formula C (v) i )＝min d(v i ，c j )，s.t.c j ∈R n ，j＝1，2，...，K，
Calculating Label and updating the light absorption coefficient formula gamma i And a random coefficient alpha i ；
Step seven: judging whether the iteration times are reached, if not, entering a step four;
step eight: ending the algorithm, outputting the optimal result, X i And a Label.
X i Representing a 2 k-dimensional coordinate, combined two by two to obtain, two-dimensional coordinates of each controller, label, topology, each exchangeThe machine belongs to the controller.
Label＝＝{C(v 1 )，C(v 2 )，...，C(v i )}，s.t.v j ∈R n ，j＝1，2，...，N，
This represents the set of controllers to which each switch belongs.
C(v i )＝min d(v i ，c j )，s.t.c j ∈R n ，j＝1，2，...，K，
And the calculation method of the controller of each switch is that the controller with the minimum Cartesian distance to the switch in the current topology.
Specifically, referring to fig. 3, the classification result with K =3 is shown, in which a black solid line represents a link between switches, a thin dotted line represents a link between a controller and a switch, a dense dotted line represents a link between controllers, the optimal firefly position is Xi (6.5, 7,13,15,20, 7), and the controller Label value (C2, C1, C2, C3, C2) to which the switch belongs.
For a given SDN switch network topology, firstly, aiming at the requirement of deploying multiple controllers in an SDN network, a firefly algorithm is popularized to a multi-objective optimization framework; secondly, aiming at the defects that the firefly algorithm is easy to fall into local optimum in the early stage and the convergence speed in the later stage is low, an improved firefly algorithm is provided; and finally, updating and iterating the position of the controller by using an improved firefly algorithm by taking the load balance as a constraint condition and the average time delay of the switch and the subordinate controller as a target function, and finally determining the deployment position of the controller in a continuous two-dimensional space. The method and the device are mainly applied to network control and management occasions.
The present disclosure belongs to the field of next generation network control and management and software defined networking, and is a novel algorithm for deploying controllers in a software defined network. In particular to an improved firefly algorithm and an SDN multi-controller deployment algorithm based on the improved firefly algorithm for ensuring minimum time delay and load balance.
Example II
The embodiment of the specification provides an SDN multi-controller deployment system based on an improved firefly algorithm, and the SDN multi-controller deployment system is realized by the following technical scheme:
the method comprises the following steps:
setting K controllers and N switches in the whole SDN switch network, wherein each switch is controlled by only one controller;
a definition module configured to: defining the average control time delay of the controller and the switches as the average value of the time delay from all the switches to the controller distributed to the switches, and taking the average value as a target function;
obtaining an attraction function based on the average and having the actual load utilization between the maximum and minimum load utilization of the controller as a constraint;
an improved firefly algorithm solving module configured to: and solving an objective function by using an improved firefly algorithm, wherein for each firefly i, a target firefly is determined according to an attraction degree formula, the firefly position is solved by moving according to a step length formula, the controller to which each switch belongs is determined, and multi-controller deployment is realized.
The specific implementation steps in this embodiment can be referred to in embodiment one, and are not described in detail here.
Example III
An embodiment of the present specification provides an SDN switch network system, which is implemented by the following technical solutions:
the method comprises the following steps:
the system comprises K controllers and N switches, wherein each switch is controlled by only one controller;
the K controllers are deployed by adopting the SDN multi-controller deployment method based on the improved firefly algorithm:
defining the average control time delay of the controller and the switches as the average value of the time delay from all the switches to the controller distributed to the switches, and taking the average value as a target function;
obtaining an attraction function based on the average value, and taking the actual load utilization ratio between the maximum and minimum load utilization ratios of the controller as a constraint condition;
and solving an objective function by using an improved firefly algorithm, wherein for each firefly i, a target firefly is determined according to an attraction degree formula, the firefly position is solved by moving according to a step length formula, the controller to which each switch belongs is determined, and multi-controller deployment is realized.
The specific implementation steps in this embodiment can be referred to as embodiment one, and are not described in detail here.
It is to be understood that throughout the description of the present specification, reference to the term "one embodiment", "another embodiment", "other embodiments", or "first through nth embodiments", etc., is intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, or materials described may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. An SDN multi-controller deployment method based on an improved firefly algorithm is characterized by comprising the following steps:
setting K controllers and N switches in the whole SDN switch network, wherein each switch is controlled by only one controller;
defining the average control time delay of the controller and the switches as the average value of the time delay from all the switches to the controller distributed to the switches, and taking the average value as a target function;
obtaining an attraction function based on the average value, and taking the actual load utilization ratio between the maximum and minimum load utilization ratios of the controller as a constraint condition;
utilize modified firefly algorithm to solve objective function, wherein, to each firefly i, according to the attraction degree formula, confirm the target firefly to move according to the step length formula, solve the position of firefly, confirm the controller that every switch belongs to, realize that many controllers deploy, wherein, the attraction degree formula is:
γ i denotes the light absorption coefficient, beta 0 For initializing the attraction degree, gamma is a Cartesian distance;
the step size formula is:
wherein,
indicates [0,1]The random number of (a) is set,
the point multiplication operation is shown, and Levy refers to a Levy distribution and is specifically shown as
Lévy～μ＝t -λ ，(1＜λ≤3)；
The improved firefly algorithm is a firefly algorithm introduced with an adaptive adjustment formula, wherein the adaptive adjustment formula is as follows:
in the formula: gamma ray b -an initial value;
γ e -final value, γ e ＞γ b ；
α b -an initial value;
α e the final value, α e ＜α b ；
k is an adjusting parameter, and k is more than 0;
σ 2 -variance between fireflies.
2. The SDN multi-controller deployment method based on the improved firefly algorithm of claim 1, wherein the number of switches, the number of controllers, and the number of switches controlled by the controller j are respectively expressed in the form of respective sets.
3. The improved firefly algorithm based SDN multi-controller deployment method of claim 1, wherein the controllers to which switch i belongs are represented as respective functions, the controllers to which each switch belongs forming a set.
4. The SDN multi-controller deployment method based on the improved firefly algorithm of claim 1, wherein the average control delay from controller to switch is the average of all switches to their assigned controller delay, and the formula is as follows
Where Delay denotes the average control Delay, d (v) i ，C(v i ) Means a distance between the ith switch and its controller;
the controller represents the ith switch;
wherein the set of switches represents: v = { V = 1 ，v 2 ，...，v N }，s.t.v i ∈R n ，i＝1，2，...，N；
The controller to which switch i belongs is denoted C (vi)
C(v i )＝mind(v i ，c j )，s.t.c j ∈R n ，j＝1，2，...，K
Wherein,
indicating the distance between the ith switch and the jth controller.
5. The SDN multi-controller deployment method based on the improved firefly algorithm of claim 1, wherein the performance of all controllers is assumed to be the same, i.e. the maximum rated load of each controller is the same; definition C max For the maximum rated load of the controller, i.e., the maximum number of switches each controller can control, the actual load utilization of the jth controller is
Wherein,
is the actual load utilization, N, of the jth controller j Indicating the number of switches controlled by the jth controller.
6. The SDN multi-controller deployment method based on improved firefly algorithm of claim 1, wherein the ith firefly position is defined as the sum of two-dimensional coordinates of K controllers, that is, a 2K-dimensional vector
Wherein,
two-dimensional coordinates representing the kth controller position for the ith firefly.
7. The SDN multi-controller deployment method based on the improved firefly algorithm of claim 4, wherein the attraction function
γ i The optical absorption coefficient is specifically defined as follows:
in the formula: gamma ray b -an initial value; gamma ray e -final value, γ e ＞γ b (ii) a k-adjusting parameter, k >0; σ 2-variance between fireflies; gamma ray b ，γ e And k is a parameter preset according to specific requirements, and r represents the distance between the target firefly and the target firefly.
8. An SDN multi-controller deployment method based on an improved firefly algorithm is characterized by comprising the following steps:
the method comprises the following steps: setting initial conditions;
an objective function Delay, an attraction function beta (gamma),
wherein Delay represents the average control Delay; function of attraction
Setting the initial light absorption coefficient gamma i Initial random coefficient α i Iteration times t, the number of fireflies n,
constraint conditions
Generating n fireflies;
step two: according to formula C (v) i )＝min d(v i ，c j )，s.t.c j ∈R n J =1, 2.. K, calculating Label;
step three: judging whether the fireflies are qualified or not according to the constraint conditions, and if not, entering a fifth step;
step four: carrying out random generation on unqualified fireflies again, and entering the third step;
step five: for each firefly i, determining a target firefly according to an attraction degree formula, and moving according to a step length formula;
step six: according to formula C (v) i )＝mind(v i c j )，s.t.c j ∈R n J =1, 2.. K, calculating Label and updating the light absorption coefficient formula γ i And a random coefficient alpha i ；
Step seven: judging whether the iteration times are reached, if not, entering a step four;
step eight: finishing the algorithm, outputting an optimal result, namely Xi and Label, wherein the Label represents a controller set to which each switch belongs;
Label＝{C(v 1 )，C(v 2 )，...，C(v i )}，s.t.v i ∈R n ，i＝1，2，...，N，
wherein,
and represents the controller of the ith switch.
9. An SDN multi-controller deployment system based on an improved firefly algorithm is provided, wherein K controllers and N switches are arranged in the whole SDN switch network, and each switch is controlled by only one controller; the method is characterized by comprising the following steps:
a definition module configured to: defining the average control time delay of the controller and the switches as the average value of the time delay from all the switches to the controller distributed to the switches, and taking the average value as a target function;
obtaining an attraction function based on the average and having the actual load utilization between the maximum and minimum load utilization of the controller as a constraint;
an improved firefly algorithm solving module configured to: and solving an objective function by utilizing an improved firefly algorithm, wherein for each firefly i, a target firefly is determined according to an attraction degree formula, the target firefly is moved according to a step length formula, the position of the firefly is solved, a controller to which each switch belongs is determined, and multi-controller deployment is realized.
10. An SDN switch network system, comprising:
the system comprises K controllers and N switches, wherein each switch is controlled by only one controller;
k controllers are deployed by adopting an SDN multi-controller deployment method based on an improved firefly algorithm:
defining the average control time delay of the controller and the switches as the average value of the time delay from all the switches to the controller distributed to the switches, and taking the average value as a target function;
obtaining an attraction function based on the average value, and taking the actual load utilization ratio between the maximum and minimum load utilization ratios of the controller as a constraint condition;
and solving an objective function by utilizing an improved firefly algorithm, wherein for each firefly i, a target firefly is determined according to an attraction degree formula, the target firefly is moved according to a step length formula, the position of the firefly is solved, a controller to which each switch belongs is determined, and multi-controller deployment is realized.
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