CN111786834A - SDN switch node increment deployment method based on genetic algorithm - Google Patents
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Abstract
The invention relates to a SDN switch node incremental deployment method based on a genetic algorithm, which comprises the following steps: step S1, generating an initialized deployment sequence by adopting a random algorithm; step S2, calculating the deployment expense value corresponding to the initialization deployment sequence S; step S3, sorting the deployment expense values from small to large; step S4, dividing the sorted deployment sequence into three sets according to the deployment cost value; step S5, selecting parents with the same number of elements as the midle class; the method comprises the steps of S6, generating a new generation of deployment sequences by carrying out cross variation, S7, updating the initial deployment sequences, S8, calculating the deployment expense value of each deployment sequence in a new deployment sequence population, and S9, recording the deployment expense values
Least valued deployment sequence and its corresponding
Value, and update the optimal deployment sequence and the same
Performing a plurality of iterative searches to finally generate a deployment cost value in a deployment sequence population
And the minimum deployment sequence is used as the optimal deployment sequence of the switch nodes.
Description
Technical Field
The invention relates to the technical field of internet, in particular to an SDN switch node incremental deployment method based on a genetic algorithm.
Background
With the rapid development of Internet applications, the traffic of Internet Service Provider (ISP) networks is increasing explosively. The traditional heuristic route optimization method cannot get rid of the restriction of the flow on the route on the shortest path, so that congestion in the network frequently occurs, and the network performance, the user experience and the satisfaction cannot be guaranteed.
The emergence of Software Defined Networking (SDN) has brought a new idea for the solution of the routing optimization problem. The SDN centralized controller can dynamically and flexibly adjust the routing of the flow in the network, gets rid of the limitations of weight modification and routing convergence of the traditional network, and can greatly improve the routing efficiency. The SDN controller can realize arbitrary flow distribution of the SDN switch nodes by issuing the flow table. However, due to some economic, organizational and technical challenges, ISP networks that fully deploy SDN switch nodes are unlikely to be implemented in the short term, and hybrid SDN networks will be a more practical network scenario and a future network trend. How to determine the location and number of SDN switch nodes in a hybrid SDN network is a considerable problem to be studied.
Disclosure of Invention
In view of this, the present invention aims to provide an SDN switch node incremental deployment method based on a genetic algorithm, which solves the problem of deployment location and number of SDN nodes in the transition process from a traditional network to an SDN network. In the transition process, the deployed SDN positions and the deployed SDN quantity can minimize the maximum link utilization rate of the network, reduce network congestion and improve network performance.
In order to achieve the purpose, the invention adopts the following technical scheme:
an SDN switch node incremental deployment method based on a genetic algorithm comprises the following steps:
step S1, generating a full-permutation sequence of the network topology nodes by adopting a random algorithm as an initialized deployment sequence S;
step S2 calculating an initialized deployment sequence
The sum of the corresponding maximum link utilizations of the network as the deployment cost value of the sequence
；
Step S3, adopting bubble sorting algorithm to sort the deployment sequence from small to large according to the deployment cost value;
step S4, dividing the sequenced deployment sequence into a top set, a middle set and a low set according to the deployment cost value;
step S5, selecting parents and parents with the same number of elements as the middles according to a method of randomly selecting a male parent from the top class and a female parent from the middles;
step S6, carrying out cross variation on the selected parents to generate a new generation of deployment sequence;
step S7, updating the initial deployment sequence;
step S8, calculating the deployment cost value of each deployment sequence in the new deployment sequence population
;
Step S9 recording deployment expense values
Least valued deployment sequence and its corresponding
Value, and update the optimal deployment sequence and the same
A value;
step S10, repeating the steps S2-S7 by using the new deployment sequence population, carrying out a plurality of iterative searches, and finally generating a deployment expense value in the deployment sequence population
And the minimum deployment sequence is used as the optimal deployment sequence of the controller.
Further, the step S2 is specifically:
step S2.1, nodes in the current deployment sequence
Compute deployment node addition
Maximum link utilization for a back-end network
A value of (d);
s2.2, traversing the nodes in the deployment sequence to obtain the accumulated value of the maximum link utilization rate of the network
I.e. deployment cost value of deployment sequence
。
Further, the step S2.1 specifically includes:
step S2.1.1 traversing each node in the networkaConstructed using Dijkstra's algorithm fromaStarting from shortest path tree to each node, and transposing the found graph to obtainaA shortest path tree for the destination node;
step S2.1.2: sequentially adding adjacent edges of each SDN node on the shortest path tree, checking whether a loop can be formed or not by using topological sorting, and adding an edge if the loop cannot be formed by adding the edge; otherwise, removing the edge; further, obtaining a maximum DAG graph based on the hybrid network topology;
step S2.1.3, topology sequencing is carried out on all network nodes of the network;
s2.1.4, sequentially taking out the nodes in the network according to the topology sorting resultbIf the node is a conventional router node, it will do sobToaAccording to the flow ratebThe number of outgoing links in the DAG graph is evenly distributed to the nodesbAnd corresponding traffic is transmitted and accumulated to the next hop of the shortest path on the outgoing link; if the node is an SDN node, then the unknown number is used for representing the nodebAnd (3) traffic on a link is extracted in a DAG graph, and according to the constraint of SDN node traffic conservation: the flow of the inflow node and the flow generated by the node are equal to the flow of the outflow node, an equation is listed, and meanwhile, the corresponding flow is added to the neighbor node of the SDN node;
step S2.1.5, passing on in turn, the nodes in the topological order to the destination nodeaIs completely allocated and obtainedaTraffic on all edges in the DAG graph for the destination node and flow conservation equations for the SDN nodes;
step S2.1.6, accumulating the flow on the same link of the DAG graph obtained by traversing each network node to obtain the total link flow on each link;
step S2.1.7, adding an equation about the flow conservation of the SDN nodes according to the fact that the flow of each link cannot exceed the capacity column inequality, and solving the linear programming problem by adopting a linear programming solving tool to obtain the flow distribution condition and the maximum link utilization rate of the network
。
Further, the three sets specifically include: the first 10% of the deployment sequence with the least expense value is the top class, the middle 80% is the midle class, and the last 10% of the deployment sequence with the greatest expense value is the low class.
Further, the step S6 is specifically:
s6.1, the first half elements of the offspring deployment sequence are inherited from the first half elements of the sequence of the male parent; the second half of the elements of the progeny deployment sequence are inherited from the first half of the elements in the maternal sequence;
and S6.2, randomly generating two positions according to a preset probability, and carrying out variation operation on the deployment elements in the offspring deployment sequence.
Further, the step S7 is specifically: elements in the top class are reserved to a next generation deployment sequence population, a newly generated deployment sequence is added to the next generation deployment sequence population, and a new deployment sequence is randomly generated and added to the next generation deployment sequence population.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an SDN switch node incremental deployment method based on a genetic algorithm, an optimal SDN switch node deployment sequence is obtained, and compared with other deployment methods, the method can better improve the network performance and reduce the maximum link utilization rate of a network.
Drawings
FIG. 1 is a flow chart of a method in an embodiment of the invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the present invention provides an SDN switch node incremental deployment method based on a genetic algorithm, including the following steps:
step S1: a full permutation sequence of some network topology nodes is generated as an initialized deployment sequence using a stochastic algorithm.
Step S2: computing an initialized deployment sequence
The sum of the corresponding maximum link utilizations as deployment cost values for the sequenceSpecifically:
step S2.1: for nodes in the current deployment sequence
Compute deployment node addition
Maximum link utilization of the network
The value of (c).
Step S2.1.1: traversing each node in the networkaConstructed using Dijkstra's algorithm fromaA shortest path tree from the start to each node. The found graph is then transposed to obtainaIs a shortest path tree of destination nodes.
Step S2.1.2: and sequentially adding adjacent edges of the SDN nodes on the shortest path tree, and checking whether a loop is formed by using topology sequencing. If adding an edge does not form a loop, adding the edge; otherwise, the edge is removed. In this way, we obtain a maximum Directed Acyclic Graph (DAG) Graph based on the hybrid network topology, that is, a path Graph where traffic can be split.
The network is then topologically ordered for all network nodes, step S2.1.3.
S2.1.4, sequentially taking out the nodes in the network according to the topology sorting resultbIf the node is a conventional router node, it will do sobToaAccording to the flow ratebThe number of outgoing links in the DAG graph is evenly distributed to the nodesbAnd corresponding traffic is transmitted and accumulated to the next hop of the shortest path on the outgoing link; if the node is an SDN node, then the unknown number is used for representing the nodebAnd (3) traffic on a link is extracted in a DAG graph, and according to the constraint of SDN node traffic conservation: the flow into the node plus the flow generated by the node is equal to the flow out of the node, the equation is set forth. And meanwhile, corresponding flow is accumulated to the neighbor nodes of the SDN node.
In turn, at step S2.1.5, the steps are repeatedCan order the nodes in the topology to the destination nodeaThe flow demand of (2) is completely distributed. And is obtained byaTraffic on all edges in the DAG graph for the destination node and flow conservation equations for the SDN node.
And S2.1.6, accumulating the flow on the same link of the DAG graph obtained by traversing each network node to obtain the total link flow on each link. The traffic according to each link cannot exceed the capacity column inequality. And then adding an equation about the flow conservation of the SDN nodes to optimize the maximum link utilization rate of the network. Solving the linear programming problem by using a linear programming solving tool to obtain the flow distribution condition and the maximum link utilization rate of the network
。
Step S2.2: traversing nodes in the deployment sequence to obtain the accumulated value of the maximum link utilization rate of the network
I.e. deployment cost value of deployment sequence
。
Step S3: the deployment sequence is ordered from small to large in terms of its deployment cost value using a bubble ordering algorithm.
Step S4: and dividing the sorted deployment sequence into three sets according to the deployment expense values of the deployment sequence. The first 10% of the deployment sequence with the least expense value is the top class, the middle 80% is the midle class, and the last 10% of the deployment sequence with the greatest expense value is the low class.
Step S5: randomly selecting a male parent from the top class and a female parent from the midle class. Parents with the scale of the number of elements in the midle class are selected.
Step S6: and carrying out cross variation on the selected parents with the same number of elements as the midle class to generate a new generation of deployment sequence.
Step S6.1: the first half elements of the progeny deployment sequence are inherited from the first half elements in the sequence of the male parent; the second half of the elements of the progeny deployment sequence are inherited from the first half of the elements in the maternal sequence.
Step S6.2: and randomly generating two positions according to a preset probability, and performing mutation operation of the deployment elements in the offspring deployment sequence. Specifically, deployment elements in two locations are swapped.
Step S7: the initial deployment sequence is updated. Specifically, elements in the top class are retained in a next-generation deployment sequence population, newly generated deployment sequences are added into the next-generation deployment sequence population, and some new deployment sequences are randomly generated and added into the next-generation deployment sequence population to avoid generating local optima.
Step S8: according to the steps S2.1.1-S2.1.6, calculating the deployment cost value of each deployment sequence in the new deployment sequence population
。
Step S9: recording deployment cost values
Least valued deployment sequence and its corresponding
Value, and update the optimal deployment sequence and the same
The value is obtained.
Step S10: repeating the steps S2-S7 by using the new deployment sequence population, carrying out a plurality of iterative searches, and finally generating a deployment expense value in the deployment sequence population
The minimum deployment sequence is the optimal deployment sequence of SDN switch nodes.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (6)
1. An SDN switch node incremental deployment method based on a genetic algorithm is characterized by comprising the following steps:
step S1, generating a full-permutation sequence of the network topology nodes by adopting a random algorithm as an initialized deployment sequence S;
step S2 calculating an initialized deployment sequence
The sum of the corresponding maximum link utilizations of the network as the deployment cost value of the sequence
；
Step S3, adopting bubble sorting algorithm to sort the deployment sequence from small to large according to the deployment cost value;
step S4, dividing the sequenced deployment sequence into a top set, a middle set and a low set according to the deployment cost value;
step S5, selecting parents and parents with the same number of elements as the middles according to a method of randomly selecting a male parent from the top class and a female parent from the middles;
step S6, carrying out cross variation on the selected parents to generate a new generation of deployment sequence;
step S7, updating the initial deployment sequence;
step S8, calculating the deployment cost value of each deployment sequence in the new deployment sequence population
;
Step S9 recording deployment expense values
Least valued deployment sequence and its corresponding
Value, and update the optimal deployment sequence and the same
A value;
step S10, repeating the steps S2-S7 by using the new deployment sequence population, carrying out a plurality of iterative searches, and finally generating a deployment expense value in the deployment sequence population
And the minimum deployment sequence is used as the optimal deployment sequence of the controller.
2. The genetic algorithm-based SDN switch node incremental deployment method of claim 1, wherein the step S2 specifically comprises:
step S2.1, nodes in the current deployment sequence
Compute deployment node addition
Maximum link utilization for a back-end network
A value of (d);
s2.2, traversing the nodes in the deployment sequence to obtain the accumulated value of the maximum link utilization rate of the network
I.e. deployment cost value of deployment sequence
。
3. The genetic algorithm-based SDN switch node incremental deployment method of claim 2, wherein the step S2.1 is specifically:
step S2.1.1 traversing each node in the networkaConstructed using Dijkstra's algorithm fromaStarting from shortest path tree to each node, and transposing the found graph to obtainaA shortest path tree for the destination node;
step S2.1.2: sequentially adding adjacent edges of each SDN node on the shortest path tree, checking whether a loop can be formed or not by using topological sorting, and adding an edge if the loop cannot be formed by adding the edge; otherwise, removing the edge; further, obtaining a maximum DAG graph based on the hybrid network topology;
step S2.1.3, topology sequencing is carried out on all network nodes of the network;
s2.1.4, sequentially taking out the nodes in the network according to the topology sorting resultbIf the node is a conventional router node, it will do sobToaAccording to the flow ratebThe number of outgoing links in the DAG graph is evenly distributed to the nodesbAnd corresponding traffic is transmitted and accumulated to the next hop of the shortest path on the outgoing link; if the node is an SDN node, then the unknown number is used for representing the nodebAnd (3) traffic on a link is extracted in a DAG graph, and according to the constraint of SDN node traffic conservation: the flow of the inflow node and the flow generated by the node are equal to the flow of the outflow node, an equation is listed, and meanwhile, the corresponding flow is added to the neighbor node of the SDN node;
step S2.1.5, passing on in turn, the nodes in the topological order to the destination nodeaIs completely allocated and obtainedaTraffic on all edges in the DAG graph for the destination node and flow conservation equations for the SDN nodes;
step S2.1.6, accumulating the flow on the same link of the DAG graph obtained by traversing each network node to obtain the total link flow on each link;
step S2.1.7, adding an equation about flow conservation of the SDN nodes according to the fact that the flow of each link cannot exceed the capacity column inequality, and solving a linear programming problem by adopting a linear programming solving tool to obtain the flow distribution condition and the networkMaximum link utilization of
。
4. The genetic algorithm-based SDN switch node deployment method of claim 1, wherein the three sets are specifically: the first 10% of the deployment sequence with the least expense value is the top class, the middle 80% is the midle class, and the last 10% of the deployment sequence with the greatest expense value is the low class.
5. The genetic algorithm-based SDN switch node incremental deployment method of claim 1, wherein the step S6 specifically comprises:
s6.1, the first half elements of the offspring deployment sequence are inherited from the first half elements of the sequence of the male parent; the second half of the elements of the progeny deployment sequence are inherited from the first half of the elements in the maternal sequence;
and S6.2, randomly generating two positions according to a preset probability, and carrying out variation operation on the deployment elements in the offspring deployment sequence.
6. The genetic algorithm-based SDN switch node incremental deployment method of claim 1, wherein the step S7 specifically comprises: elements in the top class are reserved to a next generation deployment sequence population, a newly generated deployment sequence is added to the next generation deployment sequence population, and a new deployment sequence is randomly generated and added to the next generation deployment sequence population.
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