CN115150325B - Reliable routing method applied to B5G vehicle-mounted network - Google Patents
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Abstract
The invention discloses a reliable routing method applied to a B5G vehicle network, and relates to the technical field of vehicle network routing; by designing a B5G-based software-defined vehicle network architecture, a 5G base station based on a machine learning technology is used for predicting a vehicle track, so that time delay is reduced; the link performance detection mechanism is designed, the control cost and the accuracy are balanced, and the link performance can be measured more accurately; by combining cross-generation selection, small environment strategy and traditional genetic algorithm, and by means of global view of SDN controller, a more reliable transmission path is obtained.
Description
Technical Field
The invention relates to the technical field of vehicular network routing, in particular to a reliable routing method applied to a B5G vehicular network.
Background
Along with the rapid development of economy, the mobile network and the Internet of things technology are gradually mature, and how to realize intelligent driving, man-vehicle interconnection and vehicle-vehicle interconnection becomes a research hot spot, but the problems of rapid increase of vehicles, connection instability, effective signal transmission and the like are solved, the requirements on safety and reliability of the vehicle-mounted network are gradually increased, and great challenges are still brought to the research of the vehicle-mounted network. A Software Defined Network (SDN) separates control and forwarding functions, which is an emerging network architecture, improving resource utilization efficiency and implementing network programmability. SDN is applied to a vehicle network to become a Software Defined Vehicle Network (SDVNs), and an effective solution is provided for processing vehicle interconnection requirements, vehicle network topology management and network cost management.
The development of 5G technology has covered multiple fields of industry, agriculture and traffic service, in which the vehicle network is an important application field of 5G, but with the development of the vehicle network, the vehicle network becomes a heterogeneous and large-scale network, and the B5G technology can provide an effective solution for the existing vehicle network. The B5G communication uses a higher frequency band to communicate, so that global coverage under the sea, land, air and space scenes is realized, different application scenes are processed by adopting artificial intelligence and other technologies, the higher safety is further improved, and the network resource management and network intelligence level is improved.
The patent 'a cross-layer vehicle-mounted network routing method based on link transmission capability' discloses a cross-layer vehicle-mounted network routing method based on link transmission capability. The method comprehensively considers the routing method influencing the link transmission capacity in the urban environment, calculates the probability of transmitting data, the probability of occupying channels and the like of the nodes, respectively discusses the existence of barriers among the nodes, expands the underlying cross-layer routing protocol (AODV) protocol, and continuously updates the routing table according to feedback information in the routing process. However, the method has the problems of large broadcast information quantity, unstable link transmission and the like, and can not ensure reliable routing and uninterrupted transmission of data streams.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide a reliable routing method applied to a B5G vehicle-mounted network, designs a software-defined vehicle network architecture based on B5G, and predicts the vehicle track by using a 5G base station based on a machine learning technology; and a link performance detection mechanism is designed, and an improved genetic algorithm is utilized to calculate a reliable transmission path, so that the network performance is improved.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a reliable routing method applied to a B5G vehicle network, comprising: designing an SDN network framework based on a B5G technology, and applying a WSMP protocol to a 5G system; establishing a self-adaptive monitoring mechanism; the route is calculated using a QoS routing algorithm based on an improved genetic algorithm.
The design is based on an SDN network framework of a B5G technology, and specifically comprises the following steps:
with the SDN framework based on the B5G technology, the 5G base station is used for predicting the motion trail of the vehicle, multi-access edge calculation (Multi-Acess Edge Computing, MEC) is used for providing real-time data processing, and the next base station to which the vehicle is to be connected is predicted through machine learning.
The application of the WSMP protocol to the 5G system is specifically as follows:
applying a WSMP protocol to a 5G system, wherein a WSMP protocol framework comprises an NTPS module, an adaptation module and a 5G module; the NTPS module is used for realizing the packaging and transmission of the WSMP protocol by broadcasting the WSA, and the adaptation module is used for adapting the WSMP protocol to the 5G module; the 5G module realizes the MAC layer and the physical layer of the protocol stack, sends data, filters and receives the data.
The self-adaptive monitoring mechanism is established, and specifically comprises the following steps:
controlling the balance between the overhead and the precision according to the dynamic concept polling interval of the network scale and the link load change; and calculating the update of the detection interval according to the bandwidth change rate and the last monitoring interval, wherein the bandwidth change rate is shown in the formula (1):
wherein Δb is the bandwidth change rate, band is the current occupied bandwidth of the link, lastBand is the last occupied bandwidth, ε 1 A real number greater than 0;
updating the detection interval as shown in formula (2):
wherein minPed is the minimum polling interval determined by the network size; lastPed is the last polling interval and maxPed is the maximum discussion interval; alpha 1 And alpha 2 Is a set constant, and different polling interval change strategies are adopted by judging the bandwidth change rate.
The self-adaptive monitoring mechanism comprises the steps of setting monitoring points, traversing the monitoring points and sending messages.
The set monitoring point specifically comprises: running LLDP protocol and setting link;
initializing the monitoring time of all links of the links as minPed, and adding the minPed into a monitoring queue;
and the controller receives the OpenFlow message of one link and updates the corresponding link bandwidth, link delay, packet loss rate and monitoring point.
The traversal monitoring points specifically comprise: initializing global time;
detecting whether the monitoring queue is empty or not, and traversing all monitoring points if the monitoring queue is not empty;
the global time point is increased by 1 continuously until the time of the monitoring point is equal to the global time point, the corresponding link is measured and the monitoring point is removed.
The QoS routing algorithm based on the improved genetic algorithm calculates a route, and the method comprises the following steps:
s1: performing network pretreatment, chromosome coding and fitness function construction;
s2: initializing, namely generating a plurality of paths from a source node to a target node by adopting a depth-first traversal algorithm, and selecting an adjacent node which is not randomly accessed as a next node;
s3: selecting across generations, and selecting excellent chromosomes from the populations of the current iteration and the last iteration through a roulette method;
s4: cross selection, namely randomly selecting one gene point as a cross point for any two chromosomes to generate two new chromosomes;
s5: a mutation operation, namely randomly selecting a gene from a chromosome as a mutation point, and then generating a new path from a burst point to a target node by using a depth-first search algorithm;
s6: the small environment strategy increases the contrast mechanism of two generations of chromosomes in the crossover and mutation operation, if the fitness of offspring is higher than that of a parent, the chromosomes are updated, otherwise, the chromosomes are unchanged;
s7: deletion of chromosomes, deletion crossover or mutation operations using two passes result in loops present in the chromosome;
by combining cross-generation selection, small environment strategy and original genetic algorithm, the SDN controller runs the improved genetic algorithm to generate a path on the basis of the self-adaptive monitoring mechanism when the flow is injected into the network by applying the cross-generation selection, the small environment strategy and the original genetic algorithm to the QoS routing algorithm, and sends the path to a flow table entry on the switch.
S1, a specific process comprises the following steps:
s1.1: network preprocessing, namely eliminating edge nodes which do not meet the bandwidth requirement in the network, deleting links which do not meet the data flow by an SDN controller, and constructing a virtual network which meets the bandwidth requirement;
s1.2: chromosome coding adopts a variable length chromosome coding mechanism, each chromosome represents a route path, the first gene represents a source node, the last gene represents a destination node, the chromosome sequence represents the path network node sequence, and the single node on the chromosome is ensured;
s1.3: and constructing a fitness function, and selecting a link bandwidth, a link delay and a packet loss rate as performance evaluation indexes.
The performance evaluation index calculation specifically comprises the following steps:
s1.3.1: the link bandwidth adopts a passive mode, the occupied bandwidth is measured according to the statistical data of the counter of the switch port, the bandwidth is measured by utilizing the return information of rxBytes and txBytes, the bit numbers received and transmitted by the ports are respectively represented, the switch s1 and the switch s2 are connected through the ports p1 and p2, and the bandwidth is calculated in the time period of t1 and t2, as shown in the formulas (3) and (4):
wherein,and->txBytes of port p1 at times t2 and t1, respectively,/->And->rxBytes for p1 at times t2 and t1, respectively; the data at port p2 are represented in equation (4), respectively;
T p1 and T p2 Respectively representing the total bit number of the ports p1 and p2 in the time period from t1 to t2, and taking the average value of the two ports as the calculated value of the occupied bandwidth of the link;
s1.3.2: the link delay is measured by sending a packet_out message and an Echo request message, respectively.
The sending packet_out message, the SDN controller sends the packet_out message carrying the timestamp to the switch s1, and the switch s1 forwards the message to the switch s2.
The SDN controller sends the Echo request message with the time stamp to the switch s1 and the switch s2 respectively; the link delay between the switches is calculated as shown in equation (6):
where t is the path delay and t1 and t2 are the controller to switch delays, respectively.
S1.3.3: the packet loss rate is calculated by using txplackets and rxplackets information to respectively represent the number of transmitted and received data packets, as shown in the formulas (7) and (8):
wherein,and->Respectively representing the number of data packets sent by the p1 port at the time t2 and the time t1, +.>And->The number of data packets received by the p2 port at the time t2 and the time t1 are respectively represented; TP is the total number of data packets sent in the time period from port t1 to port t2, RP is the total number of data packets received in the time period from port t1 to port t 2; and calculating the packet loss rate, wherein the calculation is as shown in a formula (9):
normalizing the performance evaluation index, setting different weights for different indexes, and calculating a fitness function, wherein the fitness function is shown as a formula (10):
f(x)＝q 1 ×f band (x)+q 2 ×(1-f loss (x))+q 3 ×(1-f delay (x)) (10)
wherein f band Is the ratio of the bandwidth of the chromosome to the sum of all chromosomes, q 1 Is f band Weights of (2); f (f) loss Is chromosome packet loss rate, q 2 Is that loss Weights of (2); f (f) delay Is the chromosome delay ratio, q 2 Is f delay Weights of (2); q 1 +q 2 +q 3 =1; the largest chromosome fitness function value is the route path.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in:
1. the invention uses the 5G base station to locate the vehicle and predicts the vehicle by means of machine learning technology, thereby reducing time delay.
2. The invention designs a self-adaptive link detection mechanism, balances control overhead and accuracy, and can more accurately measure the performance of a link.
3. The QoS routing algorithm based on the improved genetic algorithm obtains a more reliable transmission path by combining cross-generation selection, small environment strategy and traditional genetic algorithm and by means of the global view of the SDN controller.
Drawings
Fig. 1 is a flowchart of a reliable routing method applied to a B5G vehicle network according to an embodiment of the present invention;
fig. 2 is a network architecture diagram provided in an embodiment of the present invention;
FIG. 3 is a simplified topology diagram of a routing performance test provided by an embodiment of the present invention;
FIG. 4 is a complex topology diagram of a routing performance test provided by an embodiment of the present invention;
fig. 5 is a topology diagram of link performance design according to an embodiment of the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples.
A reliable routing method applied to a B5G vehicle network, comprising: designing an SDN network framework based on the B5G technology, and applying a WSMP protocol to a 5G system as shown in FIG. 2; establishing a self-adaptive monitoring mechanism; the route is calculated using a QoS routing algorithm based on an improved genetic algorithm.
The design is based on an SDN network framework of a B5G technology, and specifically comprises the following steps:
with the SDN framework based on the B5G technology, the 5G base station is used for predicting the motion trail of the vehicle, multi-access edge calculation (Multi-Acess Edge Computing, MEC) is used for providing real-time data processing, and the next base station to which the vehicle is to be connected is predicted through machine learning.
The application of the WSMP protocol to the 5G system is specifically as follows:
applying a WSMP protocol to a 5G system, wherein a WSMP protocol framework comprises an NTPS module, an adaptation module and a 5G module; the NTPS module is used for realizing the packaging and transmission of the WSMP protocol by broadcasting the WSA, and the adaptation module is used for adapting the WSMP protocol to the 5G module; the 5G module realizes the MAC layer and the physical layer of the protocol stack, sends data, filters and receives the data.
The self-adaptive monitoring mechanism is established, and specifically comprises the following steps:
controlling the balance between the overhead and the precision according to the dynamic concept polling interval of the network scale and the link load change; and calculating the update of the detection interval according to the bandwidth change rate and the last monitoring interval, wherein the bandwidth change rate is shown in the formula (1):
wherein Δb is the bandwidth change rate, band is the current occupied bandwidth of the link, lastBand is the last occupied bandwidth, ε 1 A real number greater than 0;
updating the detection interval as shown in formula (2):
wherein minPed is the minimum polling interval determined by the network size; lastPed is the last polling interval and maxPed is the maximum discussion interval; alpha 1 And alpha 2 Is a set constant, and different polling interval change strategies are adopted by judging the bandwidth change rate.
The self-adaptive monitoring mechanism comprises the steps of setting monitoring points, traversing the monitoring points and sending messages.
The set monitoring point specifically comprises: running LLDP protocol and setting link;
initializing the monitoring time of all links of the links as minPed, and adding the minPed into a monitoring queue;
and the controller receives the OpenFlow message of one link and updates the corresponding link bandwidth, link delay, packet loss rate and monitoring point.
The traversal monitoring points specifically comprise: initializing global time;
detecting whether the monitoring queue is empty or not, and traversing all monitoring points if the monitoring queue is not empty;
the global time point is increased by 1 continuously until the time of the monitoring point is equal to the global time point, the corresponding link is measured and the monitoring point is removed.
The QoS routing algorithm based on the improved genetic algorithm calculates a route, as shown in fig. 1, and includes the following steps:
s1: performing network pretreatment, chromosome coding and fitness function construction, wherein the designed network topology is shown in fig. 3 and 4;
s1.1: network preprocessing, namely eliminating edge nodes which do not meet the bandwidth requirement in the network, deleting links which do not meet the data flow by an SDN controller, and constructing a virtual network which meets the bandwidth requirement;
s1.2: chromosome coding adopts a variable length chromosome coding mechanism, each chromosome represents a route path, the first gene represents a source node, the last gene represents a destination node, the chromosome sequence represents the path network node sequence, and the single node on the chromosome is ensured;
s1.3: constructing a fitness function, and selecting a link bandwidth, a link delay and a packet loss rate as performance evaluation indexes;
in this embodiment, the network topology is designed as shown in fig. 5, and includes a ryu controller, two switches, and four hosts.
The performance evaluation index calculation specifically comprises the following steps:
s1.3.1: the link bandwidth adopts a passive mode, the occupied bandwidth is measured according to the statistical data of the counter of the switch port, the bandwidth is measured by utilizing the return information of rxBytes and txBytes, the bit numbers received and transmitted by the ports are respectively represented, the switch s1 and the switch s2 are connected through the ports p1 and p2, and the bandwidth is calculated in the time period of t1 and t2, as shown in the formulas (3) and (4):
wherein,and->txBytes of port p1 at times t2 and t1, respectively,/->And->rxBytes for p1 at times t2 and t1, respectively; the data at port p2 are represented in equation (4), respectively;
T p1 and T p2 And respectively representing the total bit number of the ports p1 and p2 in the time period from t1 to t2, and taking the average value of the two ports as the calculated value of the occupied bandwidth of the link.
S1.3.2: the link delay is measured by sending a packet_out message and an Echo request message, respectively;
the SDN controller sends the packet_out message carrying the time stamp to the switch s1, and the switch s1 forwards the message to the switch s2;
the SDN controller sends the Echo request message with the time stamp to the switch s1 and the switch s2 respectively; the link delay between the switches is calculated as shown in equation (6):
where t is the path delay and t1 and t2 are the controller to switch delays, respectively.
S1.3.3: the packet loss rate is calculated by using txplackets and rxplackets information to respectively represent the number of transmitted and received data packets, as shown in the formulas (7) and (8):
wherein,and->Respectively representing the number of data packets sent by the p1 port at the time t2 and the time t1, +.>And->The number of data packets received by the p2 port at the time t2 and the time t1 are respectively represented; TP is the total number of data packets sent in the time period from port t1 to port t2, RP is the total number of data packets received in the time period from port t1 to port t 2; and calculating the packet loss rate, wherein the calculation is as shown in a formula (9):
normalizing the performance evaluation index, setting different weights for different indexes, and calculating a fitness function, wherein the fitness function is shown as a formula (10):
f(x)＝q 1 ×f band (x)+q 2 ×(1-f loss (x))+q 3 ×(1-f delay (x)) (10)
wherein f band Is the ratio of the bandwidth of the chromosome to the sum of all chromosomes, q 1 Is f band Weights of (2); f (f) loss Is chromosome packet loss rate, q 2 Is f loss Weights of (2); f (f) delay Is the chromosome delay ratio, q 2 Is f delay Weights of (2); q 1 +q 2 +q 3 =1; the route path with the largest chromosome fitness function value;
s2: initializing, namely generating a plurality of paths from a source node to a target node by adopting a depth-first traversal algorithm, and selecting an adjacent node which is not randomly accessed as a next node;
s3: selecting across generations, and selecting excellent chromosomes from the populations of the current iteration and the last iteration through a roulette method;
s4: cross selection, namely randomly selecting one gene point as a cross point for any two chromosomes to generate two new chromosomes;
s5: a mutation operation, namely randomly selecting a gene from a chromosome as a mutation point, and then generating a new path from a burst point to a target node by using a depth-first search algorithm;
in this embodiment, the mutation rate is set to be 0.1, a point is randomly selected as a mutation point for any chromosome, and then a depth-first search algorithm is used to generate a new path from the burst point to the destination node. For example, h1, s7, s4, s9, h7, mutating at s4, regenerating a new path of s4, s2, s6, s9, h 7;
s6: the small environment strategy increases the contrast mechanism of two generations of chromosomes in the crossover and mutation operation, if the fitness of offspring is higher than that of a parent, the chromosomes are updated, otherwise, the chromosomes are unchanged;
s7: deletion of chromosomes, deletion crossover or mutation operations using two passes result in loops present in the chromosome;
in this embodiment, for chromosomes h1, s4, s2, s6, s8, s4, s2, s5, s9, and h7, such chromosomes are provided with loops, which affect the performance of the algorithm, and the loops are deleted using a two-pass method;
by combining cross-generation selection, small environment strategy and original genetic algorithm, the SDN controller runs the improved genetic algorithm to generate a path on the basis of the self-adaptive monitoring mechanism when the flow is injected into the network by applying the cross-generation selection, the small environment strategy and the original genetic algorithm to the QoS routing algorithm, and sends the path to a flow table entry on the switch.
Claims (7)
1. A reliable routing method applied to a B5G vehicle network is characterized in that: comprising the following steps: designing an SDN network framework based on a B5G technology, and applying a WSMP protocol to a 5G system; establishing a self-adaptive monitoring mechanism; calculating a route by adopting a QoS routing algorithm based on an improved genetic algorithm;
the design is based on an SDN network framework of a B5G technology, and specifically comprises the following steps:
using an SDN framework based on a B5G technology, predicting a vehicle motion track by using a 5G base station, providing real-time data processing by using Multi-access edge calculation (Multi-Acess Edge Computing, MEC), and predicting the next base station to which the vehicle is to be connected by machine learning;
the application of the WSMP protocol to the 5G system is specifically as follows:
applying a WSMP protocol to a 5G system, wherein a WSMP protocol framework comprises an NTPS module, an adaptation module and a 5G module; the NTPS module is used for realizing the packaging and transmission of the WSMP protocol by broadcasting the WSA, and the adaptation module is used for adapting the WSMP protocol to the 5G module; the 5G module realizes the MAC layer and the physical layer of the protocol stack, sends data, filters and receives the data;
the self-adaptive monitoring mechanism is established, and specifically comprises the following steps:
controlling the balance between the overhead and the precision according to the dynamic concept polling interval of the network scale and the link load change; and calculating the update of the detection interval according to the bandwidth change rate and the last monitoring interval, wherein the bandwidth change rate is shown in the formula (1):
wherein Δb is the bandwidth change rate, band is the current occupied bandwidth of the link, lastBand is the last occupied bandwidth, ε 1 A real number greater than 0;
updating the detection interval as shown in formula (2):
wherein minPed is the minimum polling interval determined by the network size; lastPed is the last polling interval and maxPed is the maximum discussion interval; alpha 1 And alpha 2 Is a set constant, and adopts different polling interval change strategies by judging the bandwidth change rate;
the QoS routing algorithm based on the improved genetic algorithm calculates a route, and the method comprises the following steps:
s1: performing network pretreatment, chromosome coding and fitness function construction;
s1.1: network preprocessing, namely eliminating edge nodes which do not meet the bandwidth requirement in the network, deleting links which do not meet the data flow by an SDN controller, and constructing a virtual network which meets the bandwidth requirement;
s1.2: chromosome coding adopts a variable length chromosome coding mechanism, each chromosome represents a route path, the first gene represents a source node, the last gene represents a destination node, the chromosome sequence represents the path network node sequence, and the single node on the chromosome is ensured;
s1.3: constructing a fitness function, and selecting a link bandwidth, a link delay and a packet loss rate as performance evaluation indexes;
s2: initializing, namely generating a plurality of paths from a source node to a target node by adopting a depth-first traversal algorithm, and selecting an adjacent node which is not randomly accessed as a next node;
s3: selecting across generations, and selecting excellent chromosomes from the populations of the current iteration and the last iteration through a roulette method;
s4: cross selection, namely randomly selecting one gene point as a cross point for any two chromosomes to generate two new chromosomes;
s5: a mutation operation, namely randomly selecting a gene from a chromosome as a mutation point, and then generating a new path from a burst point to a target node by using a depth-first search algorithm;
s6: the small environment strategy increases the contrast mechanism of two generations of chromosomes in the crossover and mutation operation, if the fitness of offspring is higher than that of a parent, the chromosomes are updated, otherwise, the chromosomes are unchanged;
s7: deletion of chromosomes, deletion crossover or mutation operations using two passes result in loops present in the chromosome;
by combining cross-generation selection, small environment strategy and original genetic algorithm, the SDN controller runs the improved genetic algorithm to generate a path on the basis of the self-adaptive monitoring mechanism when the flow is injected into the network by applying the cross-generation selection, the small environment strategy and the original genetic algorithm to the QoS routing algorithm, and sends the path to a flow table entry on the switch.
2. The reliable routing method applied to a B5G vehicle network according to claim 1, wherein: the self-adaptive monitoring mechanism comprises the steps of setting monitoring points, traversing the monitoring points and sending messages.
3. The reliable routing method applied to the B5G vehicular network according to claim 2, wherein:
the set monitoring point specifically comprises:
running LLDP protocol and setting link;
initializing the monitoring time of all links of the links as minPed, and adding the minPed into a monitoring queue;
and the controller receives the OpenFlow message of one link and updates the corresponding link bandwidth, link delay, packet loss rate and monitoring point.
4. The reliable routing method applied to the B5G vehicular network according to claim 2, wherein:
the monitoring points are traversed, and the global time is initialized;
detecting whether the monitoring queue is empty or not, and traversing all monitoring points if the monitoring queue is not empty;
the global time point is increased by 1 continuously until the time of the monitoring point is equal to the global time point, the corresponding link is measured and the monitoring point is removed.
5. The reliable routing method applied to a B5G vehicle network according to claim 1, wherein:
the link bandwidth adopts a passive mode, the occupied bandwidth is measured according to the statistical data of a counter of a switch port, the bandwidth is measured by utilizing the return information of rxBytes and txBytes, the bit numbers received and transmitted by the ports are respectively represented, a switch s1 and a switch s2 are connected through ports p1 and p2, and the bandwidth is calculated in the time period of t1 and t2, as shown in the formulas (3) and (4):
wherein,and->txBytes of port p1 at times t2 and t1, respectively,/->And->rxBytes for p1 at times t2 and t1, respectively; the data at port p2 are represented in equation (4), respectively;
T p1 and T p2 And respectively representing the total bit number of the ports p1 and p2 in the time period from t1 to t2, and taking the average value of the two ports as the calculated value of the occupied bandwidth of the link.
6. A reliable routing method for B5G vehicular networks according to claim 3, characterized in that:
the link delay is measured by respectively sending a packet_out message and an Echo request message;
the SDN controller sends the packet_out message carrying the time stamp to the switch s1, and the switch s1 forwards the message to the switch s2;
the Echo request message is sent, the SDN controller sends the Echo request message with a timestamp to the switch s1 and the switch s2 respectively, and then the link delay between the switches is calculated, as shown in formula (6):
where t is the path delay and t1 and t2 are the controller to switch delays, respectively.
7. The reliable routing method applied to a B5G vehicle network according to claim 1, wherein:
the packet loss rate uses txplackets and rxplackets information to respectively represent the calculation of the number of transmitted and received data packets, as shown in the formulas (7) and (8):
wherein,and->Respectively representing the number of data packets sent by the p1 port at the time t2 and the time t1, +.>And->The number of data packets received by the p2 port at the time t2 and the time t1 are respectively represented; TP is the total number of data packets sent in the time period from port t1 to port t2, RP is the total number of data packets received in the time period from port t1 to port t 2; and calculating the packet loss rate, wherein the calculation is as shown in a formula (9):
normalizing the performance evaluation index, setting different weights for different indexes, and calculating a fitness function, wherein the fitness function is shown as a formula (10):
f(x)＝q 1 ×f band (x)+q 2 ×(1-f loss (x))+q 3 ×(1-f delay (x)) (10)
wherein f band Is the ratio of the bandwidth of the chromosome to the sum of all chromosomes, q 1 Is f band Weights of (2); f (f) loss Is chromosome packet loss rate, q 2 Is f loss Weights of (2); f (f) delay Is the chromosome delay ratio, q 2 Is f delay Weights of (2); q 1 +q 2 +q 3 =1; the largest chromosome fitness function value is the route path.
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