CN115801665B - Intra-domain route protection method based on forwarding graph - Google Patents
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Abstract
The invention belongs to the technical field of intra-domain routing protection in the Internet and discloses an intra-domain routing protection method based on a forwarding graph. The invention solves the problems that the existing route protection scheme can not fully protect all possible single faults in the network or needs additional auxiliary mechanism assistance. Thus, the present invention may provide an effective solution for ISPs to address intra-domain route availability.
Description
Technical Field
The invention belongs to the technical field of intra-domain route protection in the Internet, and particularly relates to a forwarding graph-based intra-domain route protection method.
Background
Over the past several decades, the internet has evolved rapidly from a small network that initially supports services such as sending mail to a large infrastructure that supports social networks, online transactions, instant messaging, and the like. At the same time, internet service providers (INTERNET SERVICE Provider, ISP) are facing increasing demands in terms of quality of service, such as providing end users with quality of service, including uninterrupted service, low latency, high bandwidth, etc.
Among the problems encountered with the internet service described above, how an ISP provides uninterrupted service to users presents a significant challenge. To address this problem, the academia proposes to provide uninterrupted service by using NPC, DC, MARA, JNHOR-SP, not-Via, and Segment Routing (SR) techniques, etc., to cope with sudden failure situations in the network. Although NPC and DC are simple to implement, the fault protection rate is low. The core idea of MARA is to convert the network topology to a DAG. However, these schemes are closely related to network topologies, and in some topologies the failure rate is low. JNHOR-SP uses route ordering and Joker links to increase the failure rate of DAG graphs, joker links in the network have two directions, but JNHOR-SP can provide on average around 95% failure rate due to the drawbacks of the calculation Joker link algorithm. LFID by expanding DAG, part of links are modified into bidirectional links, so that the problems of network failure and network congestion are solved at the same time, but the failure protection rate of LFID is between 88.9% and 98.2%. Not-Via and segment routing, while protecting all possible single failures of the network, both require assistance by auxiliary mechanisms, which are difficult to deploy in practice.
Thus, the existing route protection algorithm still has the following problems: (1) It is not possible to cope with all possible single failure situations in the network; (2) assistance requiring additional assistance mechanisms; (3) does not support enhanced deployment; (4) storing a plurality of backup next hops.
Disclosure of Invention
For convenience of description, we first define some labels, which are applicable to the whole invention.
One network topology may be represented as graph g= (V, E). In the graph G V is used to represent the set of all nodes in the network topology and E is used to represent the set of all links in the network topology. Let r (d) denote the reverse shortest path tree with destination node d, and G (d) denote the forwarding graph with destination node d.
We construct a forwarding graph using genetic algorithms, the coding rules for chromosomes are: the directions of all sides in the graph G are represented by two numbers a and b, and the value of each number is 0 or 1, so that four conditions exist in the code corresponding to one side (u and v), and 00 represents that u cannot reach v and v cannot reach u;01 represents u.fwdarw.v; 10 denotes u≡v;11 denotesThe P (G (d)) is used for representing the fault protection rate of the destination node of the node d, the value range is [0,1], and the calculation method of the fault protection rate is as followsWhere f (v, d) =1 when node v is protected, otherwise f (v, d) =0. We use S (G (d)) to represent the degree of path stretch taking node d as the destination node, and the calculation formula of the degree of path stretch is/> Where D (v, D) represents the cost of the shortest path from v to D, and BD (v, D, b) represents the cost of the backup path from v to D when the failed node is b. We use fit (c) to represent fitness, where fit represents the fitness function and c represents a chromosome.
In the genetic algorithm, we use P (G (d)) and S (G (d)) to represent fitness at the same time, when two fitness comparisons, consider two cases: if P (G (d)) of both fitness levels is equal to 1, then the smaller the S (G (d)) is, the better the fitness is; otherwise, the larger P (G (d)) is, the better the adaptability is.
In the genetic algorithm, the genes have a certain probability of mutation, in the method, the mutation of the genes represents the change of the edge direction, so that in order to ensure that loops are not generated in a network after each genetic mutation, whether loops exist in the network is detected after the genetic mutation, and if the loops exist, the mutation is canceled. Under certain probability, the chromosomes will cross-exchange with one another and exchange genes with one another.
In order to solve the problems of the existing route protection algorithm in the background technology, the genetic algorithm is introduced for the first time in the internet internal route protection to construct the forwarding graph, therefore, the invention provides an internal route protection method based on the forwarding graph, which mainly comprises the following steps:
Step 1, putting all nodes in a network into a stack T 1;
step 2, judging whether the stack T 1 is empty or not, if not, executing the step 3, otherwise, ending the algorithm;
Step 3, taking out a stack top node d from a stack T 1;
constructing a reverse shortest path tree r (d) taking d as a destination node by using a Dijiesla algorithm;
constructing a forwarding graph G (d) by utilizing a genetic algorithm according to the reverse shortest path tree r (d);
Step 6, putting all nodes except the destination node d in the G (d) into a stack T 2, and executing the step 7;
Step 7, judging whether the stack T 2 is empty, if not, executing step 8, otherwise, executing step 2;
step 8, taking out a stack top node v from a stack T 2;
Step 9, in the forwarding graph G (d), two nodes a and b adjacent to v are found, and step 10 is executed;
and 10, judging whether a is a father node of v in the reverse shortest path tree r (d), if so, setting a as the optimal next hop of v, setting b as the backup next hop of v, otherwise, setting b as the optimal next hop of v, setting a as the backup next hop of v, and executing the step 7.
In the above technical solution, the method related to constructing the forwarding graph G (d) by using the genetic algorithm in the algorithm step 5 is:
step 1, setting parameters of population scale n, evolution round number m, variation probability p and crossover probability q in a genetic algorithm;
Step2, initializing a list c;
Step 3, placing all edges in the graph G in a stack T, and executing step 4;
step 4, judging whether the stack T is empty, if not, executing step 10, otherwise, executing step 5;
Step 5, taking out the top edge (u, v) of the stack;
Step 6, judging whether the (u, v) belongs to a reverse shortest path tree r (d), if so, executing the step 7, otherwise, executing the step 8;
step 7, judging whether u is a father node of v in the reverse shortest path tree r (d), if so, adding 10 into the list c, otherwise, adding 01 into the sequence c, and executing step 4;
step 8, adding 00 to the list c;
step 9, executing step 4;
Step 10, copying the list c for n times to obtain c 1,c2,…cn, and executing step 11;
Step 11, initializing a variable i=1, and executing step 12;
Step 12, judging whether i is less than or equal to m, if so, executing step 13, otherwise, executing step 18;
Step 13, calculating fitness of each chromosome to obtain f 1,f2,…fn, and executing step 14;
Step 14, sorting all chromosomes according to the fitness, eliminating half of chromosomes with lower fitness, and retaining half of chromosomes with high fitness, namely Performing step 15 on the chromosomes;
Step 15, generating by mutation crossing New chromosomes, in which the gene of initial state 1 will not be mutated, since these positions correspond to the optimal next hop;
Step 16, reassigning i=i+1 to i;
Step 17, executing step 12;
Step 18, sorting all chromosomes according to the fitness, and picking out the chromosome with highest fitness Step 19 is performed;
step 19, initializing a forwarding graph G (d), only reserving all nodes in the graph G, and executing step 20;
Step 20, chromosome is subjected to A group of codes, and the corresponding edges are put in the stack T, step 21 is executed;
Step 21, judging whether the stack T is empty, if not, executing step 22, otherwise, ending the algorithm;
step 22, taking out stack top elements (a, b, u, v), wherein a, b are codes corresponding to edges (u, v), and executing step 23;
step 23: judging whether a is 1, if so, adding a side u+.v in G (d), executing step 24, otherwise, executing step 24;
step 24, judging whether b is 1, if so, adding the edge u-v in G (d), executing step 25, otherwise executing step 25;
Step 25, executing step 21.
Compared with the existing routing protection algorithm in different domains, the method has the following advantages:
1. In the intra-domain route protection method, only two next hops are stored in each router, one is the optimal next hop, and the other is the backup next hop, and compared with the existing route protection method, the storage cost can be greatly reduced. 2. The intra-domain routing protection method can cope with all possible single fault situations in the network, and the fault protection rate can reach 100%. In addition, the method does not need the assistance of an additional auxiliary mechanism, is easier to deploy and supports incremental deployment. Therefore, the intra-domain route protection method of the invention can provide an effective solution for ISP to solve the intra-domain route availability.
Drawings
FIG. 1 is a schematic flow diagram of a forwarding graph-based intra-domain routing protection method of the present invention;
FIG. 2 is a schematic flow chart of an algorithm for constructing a forwarding graph based on a genetic algorithm according to the present invention;
FIG. 3 is an enlarged view of a portion of the algorithm flow of FIG. 2;
FIG. 4 is an enlarged view of a portion of the algorithm flow of FIG. 2;
FIG. 5 is an enlarged view III of a portion of the algorithm flow of FIG. 2;
Fig. 6 is a schematic diagram of a network topology G according to an embodiment of the present invention;
FIG. 7 is a reverse shortest path tree of FIG. G with node d as the destination node;
FIG. 8 is a forwarding graph generated by a genetic algorithm with node d as the destination node;
Fig. 9 is a graph of comparison of simulation experiment values of the routing protection algorithm in different domains in terms of fault protection rate;
FIG. 10 is a graph showing the comparison of simulated experiment values of the routing protection algorithm in different domains with respect to the path stretching degree
Among them, in order to facilitate the understanding of the algorithm flow of constructing the forwarding graph G (d) from the reverse shortest path tree r (d) using the genetic algorithm more clearly by those skilled in the art, we divide fig. 2 into three drawings of fig. 3 to 5. In fig. 9 and 10, the list "RPBFG" is also named the intra-domain routing protection algorithm of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent.
As shown in fig. 1 to 8, based on the algorithm flow described in the above summary, the following details the steps of this embodiment, and for convenience of explanation, we set the population size to be only 4 and the evolution round number to be only 2, because the algorithm execution process for each destination node is similar, the following describes the algorithm steps with the destination node as d:
step 1, constructing a reverse shortest path tree r (d) with a destination node d by using a Di Jie Style algorithm, as shown in figure 7;
Setting parameters of a genetic algorithm, wherein the population scale n=4, the evolution round number m=2, the variation probability p=0.03 and the crossover probability q=0.7;
step 3, initializing a list c, wherein c is empty;
step 4, placing all edges in the graph on stacks T, T= [ (d, a), (d, b), (a, b), (a, c), (c, f), (f, g), (g, e), (g, h), (h, e), (e, b) ];
step 5, the stack T is not empty at the moment, and the step 6 is executed;
Step 6, taking out the top edge (e, b) of the stack, wherein T= [ (d, a), (d, b), (a, b), (a, c), (c, f), (f, g), (g, e), (g, h), (h, e) ];
step 7, (e, b) in r (d), executing step 8;
Step 8, in r (d), e is not the parent node of b, adding 01 to c, where c= [01];
step 5, the stack T is not empty at the moment, and the step 6 is executed;
Step 6, taking the top edge (h, e) of the stack, in which case T= [ (d, a), (d, b), (a, b), (a, c), (c, f), (f, g), (g, e), (g, h) ]
Step 7, (h, e) in r (d), executing step 8;
step 8, in r (d), h is not the parent of e, adding 01 to c, where c= [0101];
step 5, the stack T is not empty at the moment, and the step 6 is executed;
step 6, taking the top edge (g, h) of the stack, in which case T= [ (d, a), (d, b), (a, b), (a, c), (c, f), (f, g), (g, e) ]
Step 7, (g, h) is not in r (d), step 9 is performed;
Step 9, adding 00 to c, where c= [010100]
Step 5, the stack T is not empty at the moment, and the step 6 is executed;
step 6, the top element (g, e) is taken out, where T= [ (d, a), (d, b), (a, b), (a, c), (c, f), (f, g) ]
Step 7, (g, e) in r (d), executing step 8;
Step 8, in r (d), g is not the parent of e, adding 01 to c, where c= [01010001];
step 5, the stack T is not empty at the moment, and the step 6 is executed;
Step 6, the top element (f, g) is taken out, at this time T= [ (d, a), (d, b), (a, b), (a, c), (c, f) ]
Step 7, (f, g) is not in r (d), step 9 is performed;
step 9, adding 00 to c, wherein c= [0101000100];
step 5, the stack T is not empty at the moment, and the step 6 is executed;
step 6, the stack top element (c, f) is taken out, at this time T= [ (d, a), (d, b), (a, b), (a, c) ]
Step 7, (c, f) in r (d), executing step 8;
Step 8, in r (d), c is the parent node of f, adding 10 to c, where c= [010100010010];
step 5, the stack T is not empty at the moment, and the step 6 is executed;
Step 6, the stack top element (a, c) is taken out, at this time T= [ (d, a), (d, b), (a, b) ]
Step 7 (a, c) in r (d), executing step 8;
Step 8, in r (d), a is the parent node of c, adding 10 to c, where c= [01010001001010];
step 5, the stack T is not empty at the moment, and the step 6 is executed;
step 6, the stack top element (a, b) is taken out, and at the moment, T= [ (d, a), (d, b) ]
Step 7, (a, b) is not in r (d), step 9 is performed;
step 9, adding 00 to c, wherein c= [0101000100101000];
step 5, the stack T is not empty at the moment, and the step 6 is executed;
step 6, the stack top element (d, b) is taken out, at this time T= [ (d, a) ]
Step 7, (d, b) in r (d), executing step 8;
Step 8, in r (d), d is the parent node of b, adding 10 to c, where c= [010100010010100010]; step 5, the stack T is not empty at the moment, and the step 6 is executed;
step 6, the stack top element (d, a) is taken out, at this time T= [ ]
Step 7, (d, a) in r (d), executing step 8;
Step 8, in r (d), d is the parent node of a, adding 10 to c, where c= [01010001001010001010]; step 5, when the stack T is empty, executing step 11;
Step 11, copying the c by 4 parts to obtain c 1＝c2＝c3＝c4 = [01010001001010001010];
step 12, initializing a variable i=1;
step 13, at the moment, i is smaller than or equal to m, and step 14 is executed;
Step 14: fitness is calculated separately for each chromosome, to obtain f 1＝f2＝f3＝f4 = (0, +++).
Step 15, reserving the first half chromosome after sequencing, and reserving the rest c 1,c2;
Step 16, generating two new chromosomes c 3＝[01011001111010111010],c4 = [01011001111010100010] through mutation and crossover
Step 17: i=i+1;
step 13, at this time, i=2, i is less than or equal to m, and step 14 is executed;
Step 14, calculating fitness for each chromosome respectively to obtain f 1＝f2＝(0,+∞),f3＝(0.625,s3),f4＝(0.375,s4);
step 15, sorting the chromosomes according to fitness, and reserving the first half of chromosomes to leave c 3,c4;
Step 16, generating two new chromosomes c 1＝[01011011111110111010],c2 = 01011011111110111010 by mutation crossing;
Step 17: i=i+1;
Step 13, when i= 3,i is larger than m, executing step 19;
initializing a forwarding graph G (d), wherein only all points in the network topology graph are reserved;
Step 20, calculating fitness f1＝(1,s1),f2＝(1,s2),f3＝((0.625,s3),f4＝(0.375,s4), of each chromosome, and selecting the chromosome with the best fitness from all chromosomes Let s 1<s2 here, so/>
Step 21, putting the two codes in the chromosome in a group and the corresponding edges in a stack T, wherein T= [ (10 da), (10 db), (11 ab), (10 ac), (11 cf), (11 fg), (11 ge), (10 gh), (01 he), (01 eb) ];
step 22, the stack T is not empty, and step 23 is executed;
step 23, taking out a stack top element (01 eb), wherein 01 is a code corresponding to the edge (e, b);
step 24, adding edges b≡e in G (d);
step 25, adding edges b≡e in G (d);
step 22, the stack T is not empty, and step 23 is executed;
step 23, taking out the stack top element (01 he);
Step 24, adding edges e≡h in G (d);
step 25, adding edges e≡h in G (d);
step 22, the stack T is not empty, and step 23 is executed;
step 23, taking out the stack top element (10 gh);
Step 24, adding edges g≡h in G (d);
step 25, adding edges g≡h in G (d);
step 22, the stack T is not empty, and step 23 is executed;
Step 23, taking out the stack top element (11 ge);
Step 24, adding edges in G (d)
Step 25 adding edges in G (d)
Step 22, the stack T is not empty, and step 23 is executed;
step 23, taking out the stack top element;
Step 24, adding edges in G (d)
Step 25 adding edges in G (d)
Step 22, the stack T is not empty, and step 23 is executed;
step 23, taking out the stack top element (11 cf);
Step 24, adding edges in G (d)
Step 25 adding edges in G (d)
Step 22, the stack T is not empty, and step 23 is executed;
step 23, taking out the stack top element (10 ac);
Step 24, adding edges a≡c in G (d);
step 25, adding edges a≡c in G (d);
step 22, the stack T is not empty, and step 23 is executed;
Step 23, taking out the stack top element (11 ab);
Step 24, adding edges in G (d)
Step 25 adding edges in G (d)
Step 22, the stack T is not empty, and step 23 is executed;
step 23, taking out the stack top element (10 db);
step 24, adding a side d≡b to the G (d);
step 25, adding a side d≡b to the G (d);
step 22, the stack T is not empty, and step 23 is executed;
Step 23, taking out the stack top element (10 da);
Step 24, adding a side d≡a in G (d);
step 25, adding a side d≡a in G (d);
step 22, stack T is empty, and step 26 is executed;
step 26, putting all nodes except d in G (d) into a stack T, wherein T= [ a, b, c, e, f, G, h ] is shown in fig. 8;
Step 27, the stack T is not empty, and step 28 is executed;
step 28, taking out the stack top element h;
the adjacent nodes in step 29:h are e, g;
Step 30, setting the optimal next hop of h as e, and setting the backup next hop as g;
Step 27, the stack T is not empty, and step 28 is executed;
Step 28, taking out the stack top element g;
The adjacent nodes of step 29:g are e, f;
Step 30, setting the optimal next hop of g as e, and setting the backup next hop as f;
Step 27, the stack T is not empty, and step 28 is executed;
Step 28, taking out the stack top element f;
the adjacency node of step 29:f is c, g;
step 30, setting the optimal next hop of f as c, and setting the backup next hop as g;
Step 27, the stack T is not empty, and step 28 is executed;
Step 28, taking out the stack top element f;
the adjacency node of step 29:f is c, g;
step 30, setting the optimal next hop of f as c, and setting the backup next hop as g;
Step 27, the stack T is not empty, and step 28 is executed;
Step 28, taking out the stack top element e;
the adjacency node of step 29:e is b, g;
step 30, setting the optimal next hop of e as b, and setting the backup next hop as g;
Step 27, the stack T is not empty, and step 28 is executed;
step 28, taking out the stack top element c;
the adjacency node of step 29:c is a, f;
Step 30, setting the optimal next hop of c as a, and setting the backup next hop as f;
Step 27, the stack T is not empty, and step 28 is executed;
Step 28, taking out the stack top element b;
The adjacency node of step 29:b is d, a;
Step 30, setting the optimal next hop of b as d, and setting the backup next hop as a;
Step 27, the stack T is not empty, and step 28 is executed;
step 28, taking out the stack top element a;
the adjacency node of step 29: a is d, b;
step 30, setting the optimal next hop of a as d, and setting the backup next hop as b;
The algorithm RPBFG of the invention is compared with NPC, U-turn, MARA-MA and MARA-SPE on 11 real network topologies respectively, the fault protection rate is shown in a bar chart of figure 9, and the path stretching degree of RPBFG compared with the other four algorithms is shown in figure 10. Through simulation experiments, the invention achieves 100% of fault protection rate in terms of fault protection rate, and the scheme is better in terms of path stretching degree, and has stronger coping capability obviously when coping with single link faults, and can cope with the influence caused by most single link faults.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (1)
1. The intra-domain route protection method based on the forwarding graph comprises the following steps:
step 1, putting all nodes in the network into a stack In (a) and (b);
Step 2, judging the stack If not, executing the step 3, otherwise, ending the algorithm;
Step 3, in the stack Taking out the trestle top node d;
step 4, constructing a reverse shortest path tree taking d as a destination node by using Di Jie Style algorithm ；
Step 5, according to the reverse shortest path treeConstructing a forwarding graph/>, using genetic algorithms；
Step 6, connectingAll nodes except destination node d are put in the stack/>Step 7 is executed;
step 7, judging the stack If not, executing the step 8, otherwise, executing the step 2;
step 8, in the stack Taking out the trestle top node v;
Step 9, forwarding the graph In (2), two nodes a and b adjacent to v are found, and step 10 is executed;
Step 10. In reverse shortest Path Tree Judging whether a is a father node of v, if so, setting a as the optimal next hop of v, b as the backup next hop of v, otherwise, setting b as the optimal next hop of v, and setting a as the backup next hop of v, and executing the step 7;
wherein in step 5, a forwarding graph is constructed by utilizing a genetic algorithm The method of (1) is as follows:
setting parameters of population scale n, evolution round number m, variation probability p and crossover probability q in a genetic algorithm;
Step 5.2, initializing a list c;
Step 5.3. Map is drawn All edges of (1) are put in the stack/>Step 5.4 is executed;
Step 5.4 judgment stack If not, executing the step 5.10, otherwise, executing the step 5.5;
step 5.5, taking out the top edge of the trestle ；
Step 5.6, judgingWhether or not it belongs to the reverse shortest path tree/>If yes, executing the step 5.7, otherwise, executing the step 5.8;
step 5.7 in reverse shortest Path Tree In judging/>Whether or not it is/>If true, adding 10 to list c, otherwise adding 01 to sequence c, and executing step 5.4;
step 5.8, adding 00 to the list c;
Step 5.9, executing step 5.4;
Step 5.10, copying the list c n times to obtain ，/>，.../>Step 5.11 is performed;
step 5.11, initializing a variable i=1, and executing step 5.12;
step 5.12, judging whether i is less than or equal to m, if so, executing step 5.13, otherwise, executing step 5.18;
Step 5.13, calculating fitness of each chromosome to obtain ，/>，.../>Step 5.14 is performed;
step 5.14, sorting all chromosomes according to the fitness, eliminating half of chromosomes with lower fitness, and retaining half of chromosomes with higher fitness, namely Performing step 5.15 on the chromosomes;
Step 5.15, generating by mutation and crossover New chromosomes;
Step 5.16, reassigning i=i+1 to i;
Step 5.17, executing step 5.12;
Step 5.18, sorting all chromosomes according to the fitness, and picking out the chromosome with highest fitness Step 5.19 is performed;
step 5.19 initializing forwarding graph Only the graph/>, is retainedStep 5.20 is executed;
step 5.20 chromosome is subjected to Two-by-one group of codes in a computer, and the corresponding edges are put on a stack/>Step 5.21 is performed;
Step 5.21 judgment stack If not, executing step 5.22, otherwise, ending the algorithm;
step 5.22, taking out the stack top element Wherein/>，/>For edge correspondence/>Is encoded, step 5.23 is performed;
Step 5.23 judgment Whether or not it is 1, if so, at/>Adding edges/>Executing the step 5.24, otherwise, executing the step 5.24;
step 5.24 judgment Whether or not it is 1, if so, at/>Adding edges/>Executing the step 5.25, otherwise executing the step 5.25;
step 5.25, executing step 5.21.
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Citations (4)
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|---|---|---|---|---|
| CN108429688A (en) * | 2018-03-28 | 2018-08-21 | 山西大学 | A kind of single node failure route protection method based on section routing |
| LU102400A1 (en) * | 2019-08-06 | 2021-02-09 | Nanjing Seawolf Ocean Tech Co Ltd | Path planning method and system for unmanned surface vehicle based on improved genetic algorithm |
| CN115065634A (en) * | 2022-05-26 | 2022-09-16 | 山西大学 | Loop-free efficient route protection method based on DC rule |
| CN115079704A (en) * | 2022-08-01 | 2022-09-20 | 中国电信股份有限公司 | Path planning method and device, storage medium and electronic equipment |
-
2022
- 2022-11-22 CN CN202211468481.XA patent/CN115801665B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108429688A (en) * | 2018-03-28 | 2018-08-21 | 山西大学 | A kind of single node failure route protection method based on section routing |
| LU102400A1 (en) * | 2019-08-06 | 2021-02-09 | Nanjing Seawolf Ocean Tech Co Ltd | Path planning method and system for unmanned surface vehicle based on improved genetic algorithm |
| CN115065634A (en) * | 2022-05-26 | 2022-09-16 | 山西大学 | Loop-free efficient route protection method based on DC rule |
| CN115079704A (en) * | 2022-08-01 | 2022-09-20 | 中国电信股份有限公司 | Path planning method and device, storage medium and electronic equipment |
Non-Patent Citations (3)
| Title |
|---|
| Sufficient conditions for protection routing in IP networks(Article);Tapolcai, J.;《Optimization Letters》;20131231;全文 * |
| 基于逐跳方式的单链路故障保护算法;耿海军;施新刚;王之梁;尹霞;尹少平;;计算机科学;20170715(第07期);全文 * |
| 面向网络虚拟化的资源分配算法研究;张标;《优秀硕士论文》;20201015;全文 * |
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