CN110149226B - Improved particle swarm algorithm for multi-controller deployment problem in software defined network - Google Patents
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Abstract
The invention discloses an improved particle swarm algorithm for a multi-controller deployment problem in a software defined network, wherein each particle is represented as a solution of the multi-controller deployment problem by generating random particles and velocity vectors; in each iteration, the positions of the particles are updated according to the velocity vectors, and meanwhile, the velocity vectors of the particles are updated according to the global optimal value and the worst value of the particle swarm and the historical global optimal value and the worst value of the particles, so that the situation that the particles fall into a global optimal solution is avoided, and the reliability of an output result can be effectively improved; and finally, outputting the optimal particles in the particle swarm after the iteration condition is met, wherein the corresponding deployment scheme can effectively improve the corresponding performance, effectively improve the overall performance of the network and optimize the user experience.
Description
Technical Field
The invention relates to the technical field of Software Defined Networking (SDN), in particular to an improved particle swarm algorithm for a multi-controller deployment problem in the SDN.
Background
From 2016 to 2021, the global IP network data traffic will increase nearly three times, and the network traffic of the whole internet will rise from 1000EB per year to 2.3ZB per year, as represented by cisco-dominated network traffic forecasting projects.
Since future network traffic is predicted to increase greatly, the conventional IP network architecture has not been adapted to the demands of future networks. The software defined network, as a newly proposed network architecture, has many advantages over the traditional network, and is more adaptable to the challenges of future network scenarios.
Research on the development of SDN has shown that in large wide area networks, a single controller cannot meet the requirements of the network, and therefore in 2012, Heller et al proposed a multi-controller deployment problem in wide area networks. The controller deployment problem generally refers to how to determine the number of controllers, allocate the locations of the controllers and the mapping relationship between the controllers and the switches in the software defined network so as to optimize the network performance. In consideration of performance indexes such as reliability of a network, time delay of network data flow in the network, communication time delay between controllers, load balance between controllers and the like, in a multi-controller environment, different deployment positions of the controllers affect the performance of the performance indexes. And selecting a proper controller position, and determining a proper mapping relation between the controller and the switch, so that the overall performance of the network can be effectively improved, and the user experience is optimized.
The problem of multi-controller deployment is an NP-Hard problem, the research and solution schemes of the problem comprise an exhaustion algorithm, a greedy algorithm, a clustering algorithm, an integer programming algorithm and the like, but the obtained solution schemes are not ideal.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an improved particle swarm algorithm for solving the problem of multi-controller deployment in a software defined network.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an improved particle swarm algorithm for a multi-controller deployment problem in a software defined network comprises the following steps:
step S1, acquiring a network topology G, the number k of controllers required by the network topology G and a performance index function f to be optimized, and randomly generating a plurality of particles P to form a particle swarm P, wherein each particle P represents a solution of the multi-controller deployment problem (i.e. each particle represents the position of a group of controllers); specifically, the generating step of the particle group P includes:
step A1, integer numbering is carried out on the network topology G, each number represents a node, the shortest path distance between each node is calculated, and the shortest path distance is stored in a distance matrix D.
Step a2, randomly selects k number of numbers as a particle p, also denoted as a set of controller locations, at which to deploy both controllers and switches.
Step a3, according to the shortest path principle, allocates controllers to other switches in the network, ensuring that each switch is connected only to the controller nearest to it.
And step A4, calculating the total distance between the controller and the switch according to the controller position, the switch position and the mapping relation between the switch and the controller determined in the step A3 and the shortest path distance between each node in the network stored in the distance matrix D, and taking the average value of the total distance as the average delay of the network to be used as an index for evaluating the particle performance.
Step A5, circulating the steps A2-A4 to obtain a plurality of particles P which form a particle group P.
Step S2 is to randomly set a velocity vector V for each particle p, and to mark the set of velocity vectors V as V, specifically, the velocity vector V is a vector of 1 × k (k is regarded as a one-dimensional vector).
Step S3, updating the position of each particle p according to the current position and velocity vector of the particle p, where the calculation formula is:
pnew = pold + v；
wherein p isnewThe updated position for the particle p.
poldIs the current position of the particle p.
v is the velocity vector of particle p; further, when the velocity vector v is not an integer, rounding processing is performed on the velocity vector v.
Step S4, searching the historical optimal value and the historical worst value of each particle p, and marking as pbestAnd pworstSimultaneously searching the historical optimal value and the historical worst value of all the particles P in the whole particle swarm P, and recording as gbestAnd gworst。
Step S5, updating the velocity vector of the particle p, the update formula is:
vnew= w*vold
+c1*r1*(pbest-pold)+c2*r2(gbest-pold)
+c3* (1-r1)*(pold-pworst)+c4*(1-r2)*(pold-gworst)；
wherein v isnewThe updated velocity vector for particle p.
w is the velocity inertia vector when the particle p updates the velocity vector, and the calculation formula is as follows:
w = 0.5 + (fgbest-fgworst)/fgbest；
wherein f isgbestFor the historical optimum g of all the particles P in the entire particle swarm PbestThe corresponding performance indicator function.
fgworstFor the historical worst value g of all the particles P in the whole particle group PworstThe corresponding performance indicator function.
c1、c2、c3And c4Is 1.
r1And r2Is in the interval of [0, 1 ]]The random number of (2).
S6, setting an algorithm termination condition, circularly iterating S3-S6, judging whether the algorithm termination condition is reached, if so, terminating the circular iteration, outputting the particles p after the last circulation, namely, the optimal solution of the multi-controller deployment problem, and if not, outputting vnewAs the velocity vector v in step S3, continuing the loop iteration of steps S3-S6; in this embodiment, when the number of iteration cycles of the algorithm exceeds 50, the algorithm is terminated; or g obtained in step S5bestAnd gworstIs compared to g of the last iteration of the loopbestAnd gworstIf the amplification does not exceed 10%, the algorithm is terminated.
The invention has the beneficial effects that: the method of the invention has the following advantages:
1. the particle swarm optimization method improves the particle algorithm, can dynamically change the evolution speed of the particles according to the population state, takes the worst value and the optimal value in the particle swarm into consideration at the same time, takes the worst value and the optimal value as one of the parameters of the particle evolution speed, enlarges the search space, effectively avoids the particle swarm optimization from falling into the local optimal solution, and is more favorable for finding the global optimal solution;
2. the solution obtained by the algorithm is close to the optimal value obtained by an exhaustive algorithm, and the delay of the network can be effectively reduced;
3. the algorithm has low operation complexity, can quickly obtain a solution, and effectively reduces the time required by solving compared with an exhaustive algorithm;
4. the position of the controller is used for representing a particle, the performance of the particle is calculated according to the position, and when other performance indexes such as the reliability of a network and the like are replaced, the algorithm is still effective and is suitable for various optimization scenes.
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The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a functional block diagram of the present invention;
fig. 2 is a flow chart of the present invention.
Detailed Description
Referring to fig. 1 and 2, an improved particle swarm algorithm for a multi-controller deployment problem in a software defined network includes the following steps:
step S1, obtaining a network topology G, the number k of controllers required for the network topology G, and a performance index function f (in this embodiment, the performance index function is selected as an average time delay of the network), and randomly generating a plurality of particles P to form a particle group P, where each particle P represents a solution to the multiple controller deployment problem (i.e., each particle represents a position of a group of controllers); specifically, the generating step of the particle group P includes:
step A1, integer numbering is carried out on the network topology G, each number represents a node, the shortest path distance between each node is calculated, and the shortest path distance is stored in a distance matrix D.
Step a2, randomly selects k number of numbers as a particle p, also denoted as a set of controller locations, at which to deploy both controllers and switches.
Step a3, according to the shortest path principle, allocates controllers to other switches in the network, ensuring that each switch is connected only to the controller nearest to it.
Step A4, controller position, switch position and mapping between switch and controller determined according to step A3
And (4) calculating the total distance between the controller and the switch by combining the shortest path distance between each node in the network stored in the distance matrix D, and taking the average value of the total distance as the average delay of the network to be used as an index for evaluating the performance of the particle (namely the fitness of the particle).
Step A5, circulating the steps A2-A4 to obtain a plurality of particles P which form a particle group P.
Step S2 is to randomly set a velocity vector V for each particle p, and to mark the set of velocity vectors V as V, specifically, the velocity vector V is a vector of 1 × k (k is regarded as a one-dimensional vector).
Step S3, updating the position of each particle p according to the current position and velocity vector of the particle p, where the calculation formula is:
pnew = pold + v；
wherein p isnewThe updated position for the particle p.
poldIs the current position of the particle p.
v is the velocity vector of particle p; further, since in step a1, an integer is used as the node number in the network, in this embodiment, when the velocity vector v is not an integer, rounding is performed on the velocity vector v to ensure that the updated particle position can still be mapped to the corresponding network node.
Step S4, searching the historical optimal value and the historical worst value of each particle p, and marking as pbestAnd pworstSimultaneously searching the historical optimal value and the historical worst value of all the particles P in the whole particle swarm P, and recording as gbestAnd gworst。
Step S5, updating the velocity vector of the particle p, the update formula is:
vnew= w*vold
+c1*r1*(pbest-pold)+c2*r2(gbest-pold)
+c3* (1-r1)*(pold-pworst)+c4*(1-r2)*(pold-gworst)；
wherein v isnewThe updated velocity vector for particle p.
w is the velocity inertia vector when the particle p updates the velocity vector, and the calculation formula is as follows:
w = 0.5 + (fgbest-fgworst)/fgbest；
wherein f isgbestFor the historical optimum g of all the particles P in the entire particle swarm PbestThe corresponding performance indicator function.
fgworstFor the historical worst value g of all the particles P in the whole particle group PworstThe corresponding performance indicator function.
c1、c2、c3And c4Is 1.
r1And r2Is in the interval of [0, 1 ]]The random number of (2).
Similarly, when v is obtainednewWhen the value of (b) is not an integer, for vnewRounding up is performed to ensure that subsequently updated particle positions can still be mapped to corresponding network nodes.
S6, setting an algorithm termination condition, circularly iterating S3-S6, judging whether the algorithm termination condition is reached, if so, terminating the circular iteration, outputting the particles p after the last circulation, namely, the optimal solution of the multi-controller deployment problem, and if not, outputting vnewAs the velocity vector v in step S3, continuing the loop iteration of steps S3-S6; in this embodiment, the algorithm termination condition is: when the iteration number of the algorithm loop exceeds 50, the algorithm is terminated; or g obtained in step S5bestAnd gworstIs compared to g of the last iteration of the loopbestAnd gworstIf the amplification does not exceed 10%, the algorithm is terminated.
The invention generates random particles and velocity vectors, and each particle is represented as a solution of the multi-controller deployment problem; in each iteration, the positions of the particles are updated according to the velocity vectors, and meanwhile, the velocity vectors of the particles are updated according to the global optimal value and the worst value of the particle swarm and the historical global optimal value and the worst value of the particles, so that the situation that the particles fall into a global optimal solution is avoided, and the reliability of an output result can be effectively improved; and finally, outputting the optimal particles in the particle swarm after the iteration condition is met, wherein the corresponding deployment scheme can effectively improve the corresponding performance, effectively improve the overall performance of the network and optimize the user experience.
The above embodiments do not limit the scope of the present invention, and those skilled in the art can make equivalent modifications and variations without departing from the overall concept of the present invention.
Claims (4)
1. An improved particle swarm algorithm for a multi-controller deployment problem in a software defined network is characterized by comprising the following steps of:
step S1, acquiring a network topology G, the number k of controllers required by the network topology G and a performance index function f required to be optimized, and randomly generating a plurality of particles P to form a particle swarm P, wherein each particle P represents a solution of the multi-controller deployment problem, namely each particle represents the position of a group of controllers;
step S2, randomly setting a velocity vector of each particle p as V, and recording a set formed by the velocity vectors V as V;
step S3, updating the position of each particle p according to the current position and velocity vector of the particle p, where the calculation formula is:
pnew = pold + v；
wherein p isnewUpdated positions for the particle p;
poldis the current position of the particle p;
v is the velocity vector of particle p;
step S4, searching the historical optimal value and the historical history of each particle pWorst value, denoted as pbestAnd pworstSimultaneously searching the historical optimal value and the historical worst value of all the particles P in the whole particle swarm P, and recording as gbestAnd gworst；
Step S5, updating the velocity vector of the particle p, the update formula is:
vnew= w*vold
+c1*r1*(pbest-pold)+c2*r2(gbest-pold)
+c3* (1-r1)*(pold-pworst)+c4*(1-r2)*(pold-gworst)；
wherein v isnewAn updated velocity vector for particle p;
w is the velocity inertia vector when the particle p updates the velocity vector, and the calculation formula is as follows:
w = 0.5 + (fgbest-fgworst)/fgbest；
wherein f isgbestFor the historical optimum g of all the particles P in the entire particle swarm PbestA corresponding performance indicator function;
fgworstfor the historical worst value g of all the particles P in the whole particle group PworstA corresponding performance indicator function;
c1、c2、c3and c4Is 1;
r1and r2Is in the interval of [0, 1 ]]The random number of (2);
s6, setting an algorithm termination condition, circularly iterating S3-S6, judging whether the algorithm termination condition is reached, if so, terminating the circular iteration, outputting the particles p after the last circulation, namely, the optimal solution of the multi-controller deployment problem, and if not, outputting vnewAs the velocity vector v in step S3, continuing the loop iteration of steps S3-S6;
in the step S1, the generating step of the particle group P includes:
step A1, carrying out integer numbering on the network topology G, wherein each number represents a node, calculating the shortest path distance between each node, and storing the shortest path distance into a distance matrix D;
step A2, randomly selecting k numbers as a particle p, also expressed as a group of controller positions, and deploying controllers and switches at the positions simultaneously;
step A3, distributing controllers for other switches in the network according to the principle of shortest path, and ensuring that each switch is only connected with the controller nearest to the switch;
step A4, according to the controller position, the switch position and the mapping relation between the switch and the controller determined in the step A3, the shortest path distance between each node in the network stored by the distance matrix D is combined to calculate the total distance between the controller and the switch, and the average value of the total distance is taken as the average delay of the network and is used as an index for evaluating the particle performance;
step A5, circulating the steps A2-A4 to obtain a plurality of particles P which form a particle group P.
2. The improved particle swarm algorithm for the multi-controller deployment problem in the software defined network as claimed in claim 1, wherein in step S2, the velocity vector v is a vector of 1 × k.
3. The improved particle swarm algorithm for the multi-controller deployment problem in the software defined network as claimed in claim 1, wherein in step S3, when the velocity vector v is not an integer, the velocity vector v is rounded.
4. The improved particle swarm algorithm for the multi-controller deployment problem in the software defined network as claimed in claim 1, wherein in the step S6, the algorithm is terminated when the iteration number of the algorithm loop exceeds 50; or g obtained in step S5bestAnd gworstIs compared to g of the last iteration of the loopbestAnd gworstIf the amplification does not exceed 10%, the algorithm is terminated.
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| CN111650878B (en) * | 2020-06-15 | 2021-05-04 | 北京理工大学 | Method for optimizing programmability of flow when multiple controllers in software defined network fail |
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