CN112995032A - Segment routing traffic engineering method and device based on limited widest path - Google Patents

Segment routing traffic engineering method and device based on limited widest path Download PDF

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CN112995032A
CN112995032A CN202110549603.7A CN202110549603A CN112995032A CN 112995032 A CN112995032 A CN 112995032A CN 202110549603 A CN202110549603 A CN 202110549603A CN 112995032 A CN112995032 A CN 112995032A
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path
segment
network
segments
node
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CN112995032B (en
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郭得科
罗来龙
崔思晨
任棒棒
汪漪
郑龙
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National University of Defense Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/34—Source routing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/125—Shortest path evaluation based on throughput or bandwidth

Abstract

The invention provides a segment routing traffic engineering method and a device based on a limited widest path, wherein the traffic engineering method uses a series of segments to encode the widest path after searching the widest path of a stream, and selects a relay node while considering the shortest path in a network in the path. The traffic engineering method can bypass the most congested link to the maximum extent, can guarantee performance, needs less time, and can be suitable for three or more segment routing scenes.

Description

Segment routing traffic engineering method and device based on limited widest path
Technical Field
The invention belongs to the technical field of traffic engineering, and particularly relates to a segment routing traffic engineering method and device based on a limited widest path.
Background
With the rapid development of the internet, the network traffic has problems such as explosive growth, and is limited by the traditional routing algorithm and scheduling method, and the network traffic is not easily distributed uniformly on the link, thereby causing network congestion and reducing the network service quality. Traffic engineering, which targets the analysis, prediction, and regulation of network traffic and improves network performance, therefore, is of paramount importance. Traffic Engineering (TE) is a technology for optimizing network Traffic distribution, and can optimize and schedule network Traffic, thereby realizing network Traffic load balancing, reducing congestion, and improving the utilization rate of network resources.
Recently, segment routing techniques based on IPv6 (SRv 6) have been proposed to avoid network flows being routed through congested links. SRv6 the core idea is to divide a routing path into segments representing the end-to-end logical path, the segments being represented as a series of instructions encapsulated in a header so that each flow can be routed as needed. Fig. 1 depicts a simple example of SRv6, where a flow is transmitted from router R1 to R5. According to the default shortest-circuit method, the flow is transmitted according to the path of R1 → R4 → R5. In conventional routing protocols, in order to change the default routing path, we have to update the link weights and wait for the routing protocol to converge, but this approach is certainly complex and time consuming. In contrast, the SRv6 technique makes it easier and faster to change the default routing path. In fig. 1, R1 will add a Segment Routing Header (SRH) to the packet header. The SRH consists of two parts, a segment left indicator and a segment list. The target address of the current segment is the address of the segment pointed to by the left indicator in the segment list. When a packet is received by R2, the value of the left part of the segment is reduced, and the corresponding segment is activated, i.e. the current destination address is set to 2001:: 5. The packet will then be routed along the shortest path between R2 to R5.
Considering SRv6, the traffic engineering algorithm based on SRv6 has become a hot problem in traffic engineering research due to its advantages of simplicity, easy deployment, and strong scalability. However, existing methods of Segment Routing Traffic Engineering (SRTE) lack generality because they only consider cases involving two or three segments. As in the prior art document one ("Optimized Network Traffic Engineering using Segment Routing," in Proc. of IEEE INFOCOM, 2015, pp. 657-. In the two-stage model ("Segment routed routing with bound structure in software-defined Networks," in Proc. of IEEE Conference on Local Computer Networks, 2018, pp. 477-. In the prior art document three ("Traffic engineering segmentation routing and knowledge of a carrier IP network," IEEE/ACM Transactions on network, 2018, pp. 1851. the first 1864. Schuller et al extends the SRTE model to only three segments. Due to the tight constraints of two or three segments, the above-described prior approaches may lose the opportunity to improve performance when multiple intermediate segments are available.
Disclosure of Invention
In view of this, the present invention provides a segment routing traffic engineering method and device for a constrained widest path, so as to solve the problem that the existing traffic engineering algorithm can only adapt to strict constraints of two or three segments.
A segment routing traffic engineering method based on a limited widest path comprises the following steps:
step 1: searching and acquiring SRv6 a widest path of all paths between a source node and a target node corresponding to any flow in the network, where the widest path is a path with a minimum utilization rate among all paths, and a utilization rate of each path of all paths is a utilization rate of a maximum link among all links of each path,
step 2: first encoding said widest path with segments in said SRv6 network to obtain an encoded path represented by a combination of segments formed by said segments,
and step 3: and judging whether the number of the segments used in the segment combination of the coding path is greater than the number of the segments of SRv6, if so, recording the hop counts between each segment and an upstream segment in the coding path, comparing the hop counts corresponding to the segments, deleting the segment with the minimum hop count from the segment combination, and returning to the step 3 until the number of the segments in the segment combination does not exceed the number of the segments of SRv 6.
Preferably, before the step 1, the method further comprises:
modeling SRv6 networks as undirected graphs
Figure 243627DEST_PATH_IMAGE001
Wherein, in the step (A),
Figure 930173DEST_PATH_IMAGE003
represented as a collection of network routers,
Figure 62077DEST_PATH_IMAGE004
representing physical links between routers, from network node
Figure 56578DEST_PATH_IMAGE005
To a network node
Figure 682863DEST_PATH_IMAGE006
Of a link
Figure 446419DEST_PATH_IMAGE008
All have a given capacity
Figure 749225DEST_PATH_IMAGE009
And weight
Figure 965442DEST_PATH_IMAGE010
∈WTo characterize the link
Figure 379106DEST_PATH_IMAGE011
The cost of transmitting one single-bit stream,
and networking said SRv6
Figure 246437DEST_PATH_IMAGE012
In a stream ofiA stream
Figure 985723DEST_PATH_IMAGE013
By using
Figure 954816DEST_PATH_IMAGE015
It is shown that,
Figure 172170DEST_PATH_IMAGE016
is a stream
Figure 926631DEST_PATH_IMAGE013
The source node of (a) is,
Figure 305660DEST_PATH_IMAGE017
is a stream
Figure 762049DEST_PATH_IMAGE013
The destination node of (2) is,
Figure 517515DEST_PATH_IMAGE018
is a stream
Figure 362368DEST_PATH_IMAGE013
The size of (a) is (b),
wherein the flow is in an n-segment SRv6 network
Figure 912298DEST_PATH_IMAGE013
Can follow a path
Figure 590404DEST_PATH_IMAGE020
Routing of, wherein
Figure 149561DEST_PATH_IMAGE021
At most have
Figure 596723DEST_PATH_IMAGE022
The number of the nodes is one,
Figure 333866DEST_PATH_IMAGE023
each sub-path in (a) isnSegment SRv6 is the shortest path between an upstream node and a downstream node in the network.
Preferably, the step 1 comprises:
order to
Figure 233689DEST_PATH_IMAGE024
Representing flows
Figure 330958DEST_PATH_IMAGE013
Of the source node
Figure 898205DEST_PATH_IMAGE016
And a target node
Figure 55517DEST_PATH_IMAGE017
The set of all the paths in between,
each path in the set
Figure 442636DEST_PATH_IMAGE025
Taking the maximum link utilization of all links in the path as the path
Figure 327285DEST_PATH_IMAGE025
Path utilization of
Figure 14618DEST_PATH_IMAGE026
By using
Figure 77252DEST_PATH_IMAGE028
Indicating a link
Figure 217246DEST_PATH_IMAGE029
Link utilization of
By comparing the
Figure 656318DEST_PATH_IMAGE026
Determining the widest path as the path in the set
Figure 683311DEST_PATH_IMAGE031
Wherein 1 is less than or equal toj≤n，
Figure 182425DEST_PATH_IMAGE032
。
Preferably, the widest path is found in the set at step 1 by an iterative method comprising:
setting an accessed list
Figure 544136DEST_PATH_IMAGE033
And an unaccessed list
Figure 786899DEST_PATH_IMAGE035
In combination with each other
Figure 917666DEST_PATH_IMAGE036
Is shown in
Figure 669957DEST_PATH_IMAGE037
The path in the sub-iterationq j Utilization of, said pathq j To be a slave source node
Figure 784544DEST_PATH_IMAGE035
To the network router setVTo (1)jA node
Figure 565418DEST_PATH_IMAGE038
Is detected by the optical sensor (c) and is,
initialization
Figure 816271DEST_PATH_IMAGE039
、
Figure 391608DEST_PATH_IMAGE040
、
Figure 744223DEST_PATH_IMAGE041
，
Update the table at each iteration
Figure 63209DEST_PATH_IMAGE042
Wherein
Figure 434148DEST_PATH_IMAGE044
，
Figure 180387DEST_PATH_IMAGE045
，
Then will be
Figure 269566DEST_PATH_IMAGE046
Node with minimum corresponding value
Figure 126663DEST_PATH_IMAGE035
Move to
Figure 211163DEST_PATH_IMAGE033
，
When in use
Figure 925041DEST_PATH_IMAGE033
Is empty, from
Figure 501516DEST_PATH_IMAGE042
Find out from
Figure 945660DEST_PATH_IMAGE016
To be at
Figure 291191DEST_PATH_IMAGE047
The widest path corresponding to any node in the set.
Preferably, in the step 2:
combining the segments
Figure 379232DEST_PATH_IMAGE048
Represents the abovef i The widest path coding path of (1), wherein
Figure 177424DEST_PATH_IMAGE049
，
Figure 658215DEST_PATH_IMAGE050
，
And order
Figure 592673DEST_PATH_IMAGE051
Is on the way
Figure 117195DEST_PATH_IMAGE052
In
Figure 402683DEST_PATH_IMAGE053
And
Figure 670853DEST_PATH_IMAGE054
sub-path between, let
Figure 974665DEST_PATH_IMAGE055
To represent
Figure 935667DEST_PATH_IMAGE056
To
Figure 974031DEST_PATH_IMAGE057
Shortest path therebetween, comparison
Figure 514733DEST_PATH_IMAGE051
And
Figure 440095DEST_PATH_IMAGE055
the size of (1) when
Figure 40841DEST_PATH_IMAGE058
Then use
Figure 300921DEST_PATH_IMAGE059
And
Figure 645314DEST_PATH_IMAGE054
address representation of
Figure 674450DEST_PATH_IMAGE051
，
Through each of said sub-paths represented by segments
Figure 958014DEST_PATH_IMAGE051
Representing the coding pathp i w 。
Preferably, thenGreater than or equal to 2, and thenPreferably greater than 3.
Preferably, in the step 1, the iterative method is carried out
Figure 439811DEST_PATH_IMAGE060
Sub-iterations, each iteration requiring execution
Figure 587896DEST_PATH_IMAGE061
A time complexity of the iterative method of
Figure 471538DEST_PATH_IMAGE062
In the step 2, the length of the widest path is not more than
Figure 945245DEST_PATH_IMAGE060
So the iterative method needs to compare the widest path and the shortest path
Figure 930650DEST_PATH_IMAGE063
In said step 3, the time complexity does not exceed
Figure 882425DEST_PATH_IMAGE062
All of the hop values of (a) are sorted.
Preferably, thennThe segment SRv6 network is modeled as:
Figure 886153DEST_PATH_IMAGE064
Figure 265182DEST_PATH_IMAGE065
Figure 970839DEST_PATH_IMAGE066
Figure 460726DEST_PATH_IMAGE067
Figure 53381DEST_PATH_IMAGE068
Figure 603311DEST_PATH_IMAGE070
wherein constraint equation (3) in the SRv6 network modeling ensures that all traffic is routed in the SRv6 network and that each traffic passes through only a single onenSegment routing path, in constraint equation (4) in said SRv6 network modeling, if flow
Figure 546996DEST_PATH_IMAGE013
Is a through path
Figure 591307DEST_PATH_IMAGE071
Routed, then
Figure 304048DEST_PATH_IMAGE072
To represent
Figure 24879DEST_PATH_IMAGE073
In (A) belong to
Figure 455861DEST_PATH_IMAGE013
And the constraint (4) indicates that the link utilization of each link is less than the maximum link utilization,
Figure 287550DEST_PATH_IMAGE074
representing flows
Figure 372575DEST_PATH_IMAGE013
At the source node
Figure 998728DEST_PATH_IMAGE016
And the target node
Figure 917005DEST_PATH_IMAGE017
The number of possible paths between the two,
Figure 817965DEST_PATH_IMAGE075
represents from
Figure 974140DEST_PATH_IMAGE076
The number of permutations of h different objects is found,
Figure 53086DEST_PATH_IMAGE077
representing a source node
Figure 193080DEST_PATH_IMAGE016
And
Figure 897731DEST_PATH_IMAGE078
all the shortest paths between the first and second nodes,
Figure 173991DEST_PATH_IMAGE079
to represent
Figure 922373DEST_PATH_IMAGE073
In that
Figure 549664DEST_PATH_IMAGE078
Appear at
Figure 526847DEST_PATH_IMAGE077
The total frequency of occurrence of (a) is,
Figure 923193DEST_PATH_IMAGE081
representing flows
Figure 593209DEST_PATH_IMAGE013
Whether or not to pass
Figure 458528DEST_PATH_IMAGE082
And (4) path routing.
A terminal comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, performs the segment routing traffic engineering method as claimed in any one of the preceding claims.
A computer readable storage medium storing a computer program which, when executed by a processor, performs any of the segment routing traffic engineering methods described above.
From the above, it can be seen that the method and apparatus for routing traffic engineering based on the segment of the constrained widest path according to the present invention, after searching out the widest path of a stream, encodes the widest path using a series of segments, and selects a relay node while considering the shortest path in the network within the path. The process engineering method can bypass the most congested link to the maximum extent, can guarantee performance, needs less time, and can be suitable for three or more segment routing scenes.
Drawings
FIG. 1 is a SRv6 network routing diagram;
FIG. 2 is a schematic diagram of a network constructed in accordance with the present invention;
FIG. 3 is a graph showing a comparison of the performance of N-SR, RWP and SP in 400 flow comparison experiments in topology rand 20;
FIG. 4 is a graphical representation of a comparison of the performance of N-SR, RWP and SP in 400 flow comparison experiments in four topologies;
FIG. 5 is a graphical representation of a comparison of the performance of N-SR, RWP and SP in different numbers of comparative experiments in topology rf 1221.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without any creative effort, shall fall within the protection scope of the present invention. It should be noted that "…" in this description of the preferred embodiment is only for technical attributes or features of the present invention.
In the present invention, we model the network as an undirected graph
Figure 973823DEST_PATH_IMAGE083
Wherein
Figure 490255DEST_PATH_IMAGE003
Represented as a collection of network routers,
Figure 65593DEST_PATH_IMAGE084
representing the physical links between routers. Slave node
Figure 667476DEST_PATH_IMAGE005
To the node
Figure 498379DEST_PATH_IMAGE006
Of a link
Figure 603738DEST_PATH_IMAGE085
All have a given capacity
Figure 615556DEST_PATH_IMAGE086
And weight
Figure 173576DEST_PATH_IMAGE087
Which is shown in
Figure 561832DEST_PATH_IMAGE088
The cost of transmitting one single-bit stream. Suppose there is
Figure 272431DEST_PATH_IMAGE089
A stream
Figure 455150DEST_PATH_IMAGE090
. Free stream
Figure 766046DEST_PATH_IMAGE013
Abstracted as triples having three attributes, i.e.
Figure 692414DEST_PATH_IMAGE091
Wherein
Figure 506786DEST_PATH_IMAGE092
In order to be the source node of the network,
Figure 375254DEST_PATH_IMAGE093
in order for the destination node to be able to,
Figure 907866DEST_PATH_IMAGE018
is the size of the stream. The n-segment routing problem is defined as follows:
n segment routing
Figure 637925DEST_PATH_IMAGE095
: on a network
Figure 306803DEST_PATH_IMAGE097
Each stream of
Figure 96905DEST_PATH_IMAGE098
Can follow a path
Figure 133125DEST_PATH_IMAGE099
Routing of, wherein
Figure 135716DEST_PATH_IMAGE021
At most have
Figure 190260DEST_PATH_IMAGE100
A node, the first
Figure 151263DEST_PATH_IMAGE013
Corresponding path
Figure 392888DEST_PATH_IMAGE102
。
Figure 716947DEST_PATH_IMAGE023
Each sub-path in (a) is
Figure 891576DEST_PATH_IMAGE097
Shortest path between intermediate upstream node and downstream node.
It is to be noted that it is preferable that,
Figure 492322DEST_PATH_IMAGE103
should not contain
Figure 752402DEST_PATH_IMAGE104
And
Figure 96795DEST_PATH_IMAGE017
else, path
Figure 876664DEST_PATH_IMAGE023
There will be one cycle. Let
Figure 648311DEST_PATH_IMAGE105
To represent
Figure 395687DEST_PATH_IMAGE106
And
Figure 809350DEST_PATH_IMAGE017
the number of possible paths in between, then we have:
Figure 427414DEST_PATH_IMAGE107
Figure 150388DEST_PATH_IMAGE108
represents from
Figure 853902DEST_PATH_IMAGE109
The number of permutations of h different objects is found. Thus, for one stream
Figure 805677DEST_PATH_IMAGE013
Is provided with
Figure 543826DEST_PATH_IMAGE110
A possible path, i.e.
Figure 922855DEST_PATH_IMAGE111
，
Figure 113665DEST_PATH_IMAGE071
Representing a path
Figure 619864DEST_PATH_IMAGE113
To (1) ajOne possible path (candidate path), 1 ≦j≤
Figure 212519DEST_PATH_IMAGE105
。
It should be noted that if there are multiple shortest paths between a pair of nodes, the flows are typically evenly distributed between these paths according to the ECMP protocol. Due to the fact that
Figure 28028DEST_PATH_IMAGE113
Candidate sub-path in (1)
Figure 706134DEST_PATH_IMAGE071
Only intermediate nodes that have to be walked are restricted, so that there will be multiple satisfactions
Figure 999712DEST_PATH_IMAGE071
The shortest path of (2). When selecting
Figure 693212DEST_PATH_IMAGE071
After that, we can calculate
Figure 679623DEST_PATH_IMAGE088
The ratio of the carried traffic is as follows:
Figure 845025DEST_PATH_IMAGE114
wherein
Figure 942294DEST_PATH_IMAGE116
To represent
Figure 509541DEST_PATH_IMAGE016
And
Figure 152006DEST_PATH_IMAGE078
all the shortest paths between the first and second nodes,
Figure 70284DEST_PATH_IMAGE117
to represent
Figure 440085DEST_PATH_IMAGE088
In that
Figure 127418DEST_PATH_IMAGE078
Appear at
Figure 190052DEST_PATH_IMAGE116
The total frequency of occurrence of (a).
Setting a binary variable
Figure 579314DEST_PATH_IMAGE081
Representing flows
Figure 18386DEST_PATH_IMAGE013
Whether or not to pass
Figure 560226DEST_PATH_IMAGE071
Path routing and Maximum Link Utilization (MLU) routing in the network
Figure 793761DEST_PATH_IMAGE118
And (4) showing. Then we solve the n-segment routing problem (which can be modeled as:
Figure 421051DEST_PATH_IMAGE119
Figure 132655DEST_PATH_IMAGE120
Figure 545313DEST_PATH_IMAGE121
Figure 949750DEST_PATH_IMAGE122
constraint equation (3) ensures that all traffic is routed in the network and that each traffic passes through only oneA single n-segment routing path. In constraint equation (4), if flow
Figure 64336DEST_PATH_IMAGE013
Is a through path
Figure 579631DEST_PATH_IMAGE071
Routed, then
Figure 96063DEST_PATH_IMAGE123
To represent
Figure 189178DEST_PATH_IMAGE124
In (A) belong to
Figure 525481DEST_PATH_IMAGE013
The flow rate of (c). Constraint equation (4) indicates that the link utilization for each link should be less than the maximum link utilization.
In the present invention, the n-segment routing problem is an NP-hard problem. We prove by way of example of a network shown in the figure that the n-segment routing problem is indeed an NP-hard problem, and the proving process is as follows:
as shown in fig. 2, assume that
Figure 110046DEST_PATH_IMAGE125
To
Figure 215405DEST_PATH_IMAGE126
Exist in a size of
Figure 227224DEST_PATH_IMAGE127
Is/are as follows
Figure 535976DEST_PATH_IMAGE128
A stream from
Figure 720970DEST_PATH_IMAGE129
To
Figure 680836DEST_PATH_IMAGE130
Exist in a size of
Figure 112823DEST_PATH_IMAGE132
Is/are as follows
Figure 423719DEST_PATH_IMAGE133
And (4) each stream. Attributes of each link
Figure 350086DEST_PATH_IMAGE134
Represents its weight and capacity, wherein
Figure 164459DEST_PATH_IMAGE135
. From FIG. 2, it can be seen that
Figure 783659DEST_PATH_IMAGE136
And
Figure 332583DEST_PATH_IMAGE137
would be the most congested link. To minimize the network MLU, we need to split the flow into two parts to equalize the traffic of the two parts. A part is passed through
Figure 531483DEST_PATH_IMAGE136
E.g. path
Figure 731520DEST_PATH_IMAGE138
And
Figure 256043DEST_PATH_IMAGE139
the other part is passed through
Figure 275951DEST_PATH_IMAGE137
E.g. path
Figure 544122DEST_PATH_IMAGE140
And
Figure 868441DEST_PATH_IMAGE141
. If we can solve the 2-SR problem, we will put the number set
Figure 298285DEST_PATH_IMAGE143
Can be divided into twoSubsets, the sum of each subset being equal. However, this Subset and problem is a classical NP-hard problem, as the relevant principles are explained in detail in the document four "Subset sum NP-complete", according to common general knowledge, which is clearly known to the person skilled in the art. The fourth document can be obtained by a network link http:// www.cs.cornell.edu/courses/cs4820/2018 fa/alternatives/subset sum. The 2-SR problem is NP-hard, as is the n-SR problem.
Since the n-SR problem is np-hard, especially as the topology scale increases, commercial solvers cannot solve quickly. To this end, the present invention proposes a segment routing traffic engineering method based on a limited widest path (RWP) to minimize the maximum link utilization.
The segment routing traffic engineering method based on the limited widest path (RWP) mainly comprises the following steps:
step 1: searching for the Widest Path (WP)
And searching and acquiring SRv6 a widest path in all paths between a source node and a target node corresponding to any flow in the network, where the widest path is a path with a minimum utilization rate in all paths, and the utilization rate of each path in all paths is a maximum utilization rate in all links in each path.
Step 2: the widest path is encoded with a series of segments.
First encoding said widest path with segments in said SRv6 network to obtain an encoded path represented by a combination of segments formed by said segments
And step 3: to meet the maximum segment number constraint, RWP further refines the encoded SR path in step 3.
And judging whether the number of the segments used in the segment combination of the coding path is greater than the number of the segments of SRv6, if so, recording the hop counts between each segment and an upstream segment in the coding path, comparing the hop counts corresponding to the segments, deleting the segment with the minimum hop count from the segment combination, and returning to the step 3 until the number of the segments in the segment combination does not exceed the number of the segments of SRv 6.
Before describing the RWP algorithm in detail, the definition of the widest path is given first. Is provided with
Figure 71069DEST_PATH_IMAGE145
Indicating a link
Figure 142930DEST_PATH_IMAGE124
Link utilization of.
Widest path: for the arbitrary stream
Figure 786401DEST_PATH_IMAGE013
Let us order
Figure 669037DEST_PATH_IMAGE146
Representing the corresponding source node
Figure 397959DEST_PATH_IMAGE016
And the corresponding target node
Figure 273511DEST_PATH_IMAGE017
All paths in between, the widest path being the source node
Figure 37068DEST_PATH_IMAGE016
And the target node
Figure 74294DEST_PATH_IMAGE017
The widest path in between. For each corresponding said path
Figure 805358DEST_PATH_IMAGE025
We define the path utilization as the maximum link utilization of all links in the path, i.e., the maximum link utilization
Figure 953443DEST_PATH_IMAGE147
. The widest path is the set
Figure 837085DEST_PATH_IMAGE148
In (1)
Figure 779634DEST_PATH_IMAGE031
Wherein
Figure 748727DEST_PATH_IMAGE149
According to the above definition, the RWP algorithm follows the following three steps.
Step 1: find the widest path.
Figure 716814DEST_PATH_IMAGE013
The widest path of (a) can be found iteratively. We set the accessed list
Figure 189383DEST_PATH_IMAGE033
And an unaccessed list
Figure 833991DEST_PATH_IMAGE035
。
Figure 24801DEST_PATH_IMAGE151
Is shown in
Figure 780268DEST_PATH_IMAGE037
In a sub-iteration from
Figure 372923DEST_PATH_IMAGE016
To
Figure 440630DEST_PATH_IMAGE038
The path utilization of. First of all, initializing
Figure 118736DEST_PATH_IMAGE039
、
Figure 412314DEST_PATH_IMAGE153
、
Figure 859476DEST_PATH_IMAGE154
. In each iteration, we update
Figure 845886DEST_PATH_IMAGE155
Wherein
Figure 762021DEST_PATH_IMAGE156
，
Figure 859290DEST_PATH_IMAGE157
. Then will be
Figure 426537DEST_PATH_IMAGE158
Node with the smallest value
Figure 318270DEST_PATH_IMAGE035
Move to
Figure 970968DEST_PATH_IMAGE033
. When in use
Figure 855616DEST_PATH_IMAGE033
When it is empty, we go through backtracking
Figure 542950DEST_PATH_IMAGE158
Find out from
Figure 605584DEST_PATH_IMAGE092
To be at
Figure 745578DEST_PATH_IMAGE047
The widest path of any node in the tree.
Step 2: and (4) path coding. In finding out
Figure 184649DEST_PATH_IMAGE013
After the widest path we need to represent it using a combination of segments. Let us leave it without loss of generality
Figure 211642DEST_PATH_IMAGE159
To represent
Figure 710757DEST_PATH_IMAGE013
Of the widest path of (1), wherein
Figure 72468DEST_PATH_IMAGE160
、
Figure 315231DEST_PATH_IMAGE161
。
Figure 711577DEST_PATH_IMAGE162
Is on the way
Figure 627931DEST_PATH_IMAGE163
In
Figure 742517DEST_PATH_IMAGE056
And
Figure 788970DEST_PATH_IMAGE054
the sub-path in between. Let
Figure 39823DEST_PATH_IMAGE164
To represent
Figure 631473DEST_PATH_IMAGE056
To
Figure 702197DEST_PATH_IMAGE054
The shortest path between them. If it is not
Figure 286762DEST_PATH_IMAGE165
Then we can use
Figure 392121DEST_PATH_IMAGE056
And
Figure 403940DEST_PATH_IMAGE054
address representation of
Figure 227539DEST_PATH_IMAGE162
。
And step 3: the encoded path is refined. The above steps may result in a path violating the constraint of the maximum number of segments
Figure 599483DEST_PATH_IMAGE166
. An example is given hereTo illustrate this problem more clearly. Suppose we find the widest path at step 1
Figure 824928DEST_PATH_IMAGE167
Then it is represented by 4 segments in step 2, resulting in
Figure 7648DEST_PATH_IMAGE168
. Assuming that the maximum number of stages is fixed to 3, the generated path
Figure 318544DEST_PATH_IMAGE169
Is not feasible. To this end, we assign an attribute to each non-destination segment, which is recorded in
Figure 979332DEST_PATH_IMAGE170
The number of hops between it and the upstream segment. In the case of this example of the present invention,
Figure 75595DEST_PATH_IMAGE171
，
Figure 163637DEST_PATH_IMAGE172
，
Figure 961829DEST_PATH_IMAGE174
. Deletion with lowest
Figure 426308DEST_PATH_IMAGE175
The segments are valued until the constraint is satisfied because the segments have less of a control effect on bypassing the flow over the congested link. Since the maximum number of segments allowed in this example is 3, we only need to delete nodes from the path
Figure DEST_PATH_IMAGE177
To obtain
Figure 144122DEST_PATH_IMAGE178
。
In order to further describe and clarify the algorithmic processes of the process engineering method of the present invention, a specific programming example is provided below, but the process engineering method of the present invention is not limited to be implemented by the following implementation methods:
Require：G=(V,E,W,C) A plurality of streamsf1,f2,…fmThe maximum number of stagesN.
Ensure: segment routing pathp 1 r ,p 2 r ,…,p n r }
1:
Figure 668644DEST_PATH_IMAGE179
2:for all f i ∈{f1,f2,…fm} do
3: p i w =widestpath(G, W′,s i ,d i )
4: p i r =Encodepath(G, p i w , f i )
5: p i r =Refinepath(G, p i w , f i ,n)
6: flow usingf i Along the pathp i r Occupied bandwidth updateW′
7：return{ p 1 r ,p 2 r ,…,p n r }
p i w =widestpath(G, W′,s i ,d i )
1 initializationS=s i ,S′=V-{s i },t=0,B ij (0)=+∞
2:while S′≠Ø do
3:t= t +1
4: for all
Figure 954132DEST_PATH_IMAGE180
5: B ij (t)=min{ B ij (t-1),max{ B ik (t-1),w′ kj }}
6, handleB ij (t) Having a minimum value
Figure 222302DEST_PATH_IMAGE038
FromS′Move toS
7: by backtrackingB sidi To obtainp i w
p i r =Encodepath(G, p i w , f i )
1:p i w =Encodepath(a 0=s i ,a 1 ,a 2 ,,a n-1,a n =d i )
2:j=0,k=1, p i r =[ s i ]
3:while k≠n do
4: if p i w [j,k]= p s [j,k] then
5: k= k +1
6:else
7: j=k-1, p i r =[ p i r →a j ]
p i r =Refinepath(G, p i w , f i ,n)
By observation ofp i w Andp i r find out the pi value of each segment
2: deleting the segment with the minimum value of pi value untilp i r Not exceedingnSegment of
In step 1, we have
Figure 276846DEST_PATH_IMAGE182
Sub-iterations, each iteration requiring execution
Figure 457423DEST_PATH_IMAGE183
A sub-comparison, thus having a time complexity of
Figure 230207DEST_PATH_IMAGE184
. In step 2, the length of the widest path is not greater than
Figure 302068DEST_PATH_IMAGE182
So the algorithm only needs to compare the widest path with the shortest path
Figure DEST_PATH_IMAGE185
Next, the process is carried out. In step 3, the algorithm does not exceed the time complexity
Figure 476697DEST_PATH_IMAGE184
All of
Figure 857869DEST_PATH_IMAGE175
The values are sorted. The time complexity of one iteration can be obtained as
Figure 586790DEST_PATH_IMAGE184
. Due to the fact that
Figure DEST_PATH_IMAGE186
Flow, can obtainThe total temporal complexity of the RWP algorithm is
Figure 727922DEST_PATH_IMAGE187
。
To better contrast the adapted 3-segment or more segment routing of the traffic engineering method provided by the present invention, we performed experiments in four network topologies, including three composite networks (rand20, rand50 and rand80) and one real network (rf 1221). The topologies rand50 and rf1221 are provided by DEFO (an introduction to this particular related knowledge can be obtained from "clear and express forward optimizer" in the link http:// sites. ucluvain. be/info /). The topologies rand20 and rand80 are composite networks with an average degree of 3. We run real streams from DEFO in rand50 and rf1221, while synthetic streams generated by gravity models (m. Roughan, "simple the synthesis of internet traffic," inproc. of ACM sigcomp, October 2005, pp. 93-96) were used in rand20 and rand80 networks. Table I summarizes the details of these topologies. The main indicators of our experiments include MLU and time consumption of the overall network. We compared our algorithm with the Shortest Path (SP) based solution and the results from solving the n-SR model using CPLEX. To this end, in our experiments we changed the maximum number of segments, the network size and the number of flows in the network. We randomly selected streams from the stream data set for 10 runs and found the mean and standard deviation results.
Table I topology and flow information
Figure DEST_PATH_IMAGE188
In topology rand20, we quantify the impact of the maximum number of segments by changing its value on the MLU. As shown in fig. 3, n-SR and RWP achieve better MLU than shortest path. Specifically, the MLU of the 2-SR is half that of the SP, and the 2-RWP can provide nearly 40% performance improvement over the SP. Notably, as shown in FIG. 3, the performance of 3-SR and 4-SR is very close to that of 2-SR. This phenomenon indicates that the 2-SR model can provide an optimal or sub-optimal solution in a small network. Furthermore, the performance of RWP improves with increasing maximum number of stages, and the difference between the result of the RWP algorithm and the theoretical result (n-SR) decreases with increasing number of stages.
In the case of 400 flows, we increase the size of the topology and compare the MLUs for SP, 2-SR and RWP, with the results shown in FIG. 4. In particular, 2-SR achieves the lowest MLU over other methods. In contrast, an SP always has the highest MLU. Our RWP algorithm implements one intermediate MLU but close to 2-SR. FIG. 5 shows the results of MLU when the number of rf1221 streams was varied. Similar to FIG. 4, 2-SR and RWP outperform the SP method. As the flow increases, the MLU of all methods increases.
From the results of the above three experiments, it can be observed that the increase in the segments helps to reduce the MLU in the RWP. The reason for this is that as the number of segments increases, the algorithm will explore more paths and thus hopefully disperse traffic in the network. In addition, given the network size and flow cardinality, the number of paths that the algorithm can explore does not grow as fast as the number of segments. Thus, increasing the number of stages has the marginal effect of reducing the MLU.
Furthermore, by further comparing the time consumption of the SR and RWP in tables II and III, it can be seen that the average execution time of RWP continues to increase as traffic and network size increase. In general, the RWP algorithm provided by the invention is obviously superior to the 2-SR method. As shown in table II, the 2-SR algorithm searches for an optimal path longer and longer (from 2.671s to 488.314s) as the network size increases. In contrast, the time consumption of the RWP algorithm increases only from 1 second to less than 8 seconds as the network size grows. The reason is that the SR model is and the temporal complexity of RWP is O (m × | V | 2). RWP can provide 5-10% more solution in MLU than 2-SR in 10 seconds in most experiments.
Table II run time of 400 streams in four topologies
Figure DEST_PATH_IMAGE189
TABLE III runtime in topology RF1221
Figure DEST_PATH_IMAGE190
In the invention, the existing 2-SR or 3-SR problem is popularized to the situation of n sections, wherein n is more than or equal to 2, after an integer programming model is constructed, the n-SR problem is proved to be np difficult, therefore, a flow engineering method based on the limited widest path section is designed to solve the problem, and experimental results show that the method can provide a solution equivalent to the MLU of the most advanced calculation method while reducing the calculation time.
In addition, the present invention also provides a terminal, which includes a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to execute any one of the segment routing traffic engineering methods provided by the present invention.
In addition, the present invention also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to execute any segment routing traffic engineering method provided by the present invention.
While embodiments in accordance with the invention have been described above, these embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments described. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated. The invention is limited only by the claims and their full scope and equivalents.

Claims (10)

1. A segment routing traffic engineering method based on a limited widest path is characterized by comprising the following steps:
step 1: searching and acquiring SRv6 a widest path of all paths between a source node and a target node corresponding to any flow in the network, where the widest path is a path with a minimum utilization rate among all paths, and a utilization rate of each path of all paths is a utilization rate of a maximum link among all links of each path,
step 2: first encoding said widest path with segments in said SRv6 network to obtain an encoded path represented by a combination of segments formed by said segments,
and step 3: and judging whether the number of the segments used in the segment combination of the coding path is greater than the number of the segments of SRv6, if so, recording the hop counts between each segment and an upstream segment in the coding path, comparing the hop counts corresponding to the segments, deleting the segment with the minimum hop count from the segment combination, and returning to the step 3 until the number of the segments in the segment combination does not exceed the number of the segments of SRv 6.
2. The segment routing traffic engineering method according to claim 1, further comprising, before the step 1:
modeling SRv6 networks as undirected graphs
Figure 231799DEST_PATH_IMAGE001
Wherein, in the step (A),
Figure 2309DEST_PATH_IMAGE002
represented as a collection of network routers,
Figure 97304DEST_PATH_IMAGE003
representing physical links between routers, from network node
Figure 953264DEST_PATH_IMAGE004
To a network node
Figure 791907DEST_PATH_IMAGE005
Of a link
Figure 666191DEST_PATH_IMAGE006
All have a given capacity
Figure 932087DEST_PATH_IMAGE007
∈CAnd weight
Figure 275344DEST_PATH_IMAGE008
∈WTo characterize the link
Figure 917678DEST_PATH_IMAGE009
The cost of transmitting one single-bit stream,
and networking said SRv6
Figure 662780DEST_PATH_IMAGE010
In a stream ofiA stream
Figure 348845DEST_PATH_IMAGE011
By using
Figure 179398DEST_PATH_IMAGE012
It is shown that,
Figure 94264DEST_PATH_IMAGE013
is a stream
Figure 959452DEST_PATH_IMAGE014
The source node of (a) is,
Figure 567151DEST_PATH_IMAGE015
is a stream
Figure 865758DEST_PATH_IMAGE011
The destination node of (2) is,
Figure 849895DEST_PATH_IMAGE016
is a stream
Figure 569589DEST_PATH_IMAGE011
The size of (a) is (b),
whereinStreaming in an n-segment SRv6 network
Figure 348189DEST_PATH_IMAGE011
Can follow a path
Figure 887755DEST_PATH_IMAGE017
Routing of, wherein
Figure 924850DEST_PATH_IMAGE018
At most have
Figure 233472DEST_PATH_IMAGE019
The number of the nodes is one,
Figure 448552DEST_PATH_IMAGE020
each sub-path in (a) isnSegment SRv6 is the shortest path between an upstream node and a downstream node in the network.
3. The segment routing traffic engineering method according to claim 2, comprising, at step 1:
order to
Figure 209835DEST_PATH_IMAGE021
Representing flows
Figure 801353DEST_PATH_IMAGE011
Of the source node
Figure 213749DEST_PATH_IMAGE013
And a target node
Figure 599731DEST_PATH_IMAGE015
The set of all the paths in between,
each path in the set
Figure 113889DEST_PATH_IMAGE022
Maximum link utilization for all links in a pathRate as the path
Figure 243519DEST_PATH_IMAGE022
Path utilization of
Figure 261154DEST_PATH_IMAGE023
By using
Figure 83616DEST_PATH_IMAGE024
Indicating a link
Figure 68759DEST_PATH_IMAGE025
The utilization of the links of (a) a,
by comparing the
Figure 736500DEST_PATH_IMAGE023
Determining the widest path as the path in the set
Figure 874221DEST_PATH_IMAGE026
Wherein 1 is less than or equal toj≤n，
Figure 867584DEST_PATH_IMAGE027
。
4. The segment-routed traffic engineering method according to claim 3, characterized in that the widest path is found in the set in step 1 by an iterative method comprising:
setting an accessed list
Figure 825176DEST_PATH_IMAGE028
And an unaccessed list
Figure 548806DEST_PATH_IMAGE029
In combination with each other
Figure 806612DEST_PATH_IMAGE030
Is shown in
Figure 439719DEST_PATH_IMAGE031
The path in the sub-iterationq j Utilization of, said pathq j To be a slave source node
Figure 415765DEST_PATH_IMAGE032
To the network router setVTo (1)jA node
Figure 425309DEST_PATH_IMAGE033
Is detected by the optical sensor (c) and is,
initialization
Figure 786889DEST_PATH_IMAGE034
、
Figure 856476DEST_PATH_IMAGE035
、
Figure 54240DEST_PATH_IMAGE036
，
At each iteration, updating the
Figure 867475DEST_PATH_IMAGE037
Wherein
Figure 99873DEST_PATH_IMAGE038
，
Figure 324050DEST_PATH_IMAGE039
，
Then will be
Figure 274688DEST_PATH_IMAGE037
Node with minimum corresponding value
Figure 626035DEST_PATH_IMAGE029
Move to
Figure 712940DEST_PATH_IMAGE028
，
When in use
Figure 858750DEST_PATH_IMAGE028
Is empty, from
Figure 31106DEST_PATH_IMAGE037
Find out from
Figure 435411DEST_PATH_IMAGE013
To be at
Figure 111243DEST_PATH_IMAGE040
The widest path corresponding to any node in the set.
5. The segment routing traffic engineering method according to claim 4, characterized in that in step 2:
combining the segments
Figure 959113DEST_PATH_IMAGE041
Represents the abovef i The widest path coding path of (1), wherein
Figure 353186DEST_PATH_IMAGE042
、
Figure 311914DEST_PATH_IMAGE043
，
And order
Figure 190108DEST_PATH_IMAGE044
Is on the way
Figure 943300DEST_PATH_IMAGE045
In
Figure 824668DEST_PATH_IMAGE046
And
Figure 321509DEST_PATH_IMAGE047
sub-path between, let
Figure 237512DEST_PATH_IMAGE048
To represent
Figure 410873DEST_PATH_IMAGE049
To
Figure 45117DEST_PATH_IMAGE050
Shortest path therebetween, comparison
Figure 80069DEST_PATH_IMAGE051
And
Figure 850579DEST_PATH_IMAGE048
the size of (1) when
Figure 945574DEST_PATH_IMAGE052
Then use
Figure 50802DEST_PATH_IMAGE049
And
Figure 889445DEST_PATH_IMAGE047
address representation of
Figure 780041DEST_PATH_IMAGE051
，
Through each of said sub-paths represented by segments
Figure 45937DEST_PATH_IMAGE053
Representing the coding pathp i w 。
6. The segment routing traffic engineering method according to claim 2,characterized in that thenGreater than or equal to 2.
7. The segment routing traffic engineering method according to claim 4, characterized in that in step 1, the iterative method is performed
Figure 123614DEST_PATH_IMAGE054
Sub-iterations, each iteration requiring execution
Figure 15216DEST_PATH_IMAGE055
A time complexity of the iterative method of
Figure 494739DEST_PATH_IMAGE056
In the step 2, the length of the widest path is not more than
Figure 197116DEST_PATH_IMAGE054
So the iterative method needs to compare the widest path and the shortest path
Figure 762089DEST_PATH_IMAGE057
In said step 3, the time complexity does not exceed
Figure 208114DEST_PATH_IMAGE058
All of the hop values of (a) are sorted.
8. The segment-routed traffic engineering method of claim 4, characterized in that, thennThe segment SRv6 network is modeled as:
Figure 73302DEST_PATH_IMAGE059
Figure 933198DEST_PATH_IMAGE060
Figure 719888DEST_PATH_IMAGE061
Figure 969604DEST_PATH_IMAGE062
Figure 423719DEST_PATH_IMAGE063
Figure 467898DEST_PATH_IMAGE064
wherein constraint equation (3) in the SRv6 network modeling ensures that all traffic is routed in the SRv6 network and that each traffic passes through only a single onenSegment routing path, in constraint equation (4) in said SRv6 network modeling, if flow
Figure 991153DEST_PATH_IMAGE011
Is a through path
Figure 778980DEST_PATH_IMAGE065
Routed, then
Figure 87602DEST_PATH_IMAGE066
To represent
Figure 302682DEST_PATH_IMAGE067
In (A) belong to
Figure 329544DEST_PATH_IMAGE011
And the constraint (4) indicates that the link utilization of each link is less than the maximum link utilization,
Figure 904751DEST_PATH_IMAGE068
representing flows
Figure 333458DEST_PATH_IMAGE069
At the source node
Figure 719440DEST_PATH_IMAGE070
And the target node
Figure 968019DEST_PATH_IMAGE015
The number of possible paths between the two,
Figure 97649DEST_PATH_IMAGE071
represents from
Figure 630130DEST_PATH_IMAGE072
The number of permutations of h different objects is found,
Figure 187014DEST_PATH_IMAGE073
representing a source node
Figure 922888DEST_PATH_IMAGE013
And
Figure 856209DEST_PATH_IMAGE074
all the shortest paths between the first and second nodes,
Figure 443530DEST_PATH_IMAGE075
to represent
Figure 905735DEST_PATH_IMAGE076
In that
Figure 394485DEST_PATH_IMAGE074
Appear at
Figure 865918DEST_PATH_IMAGE077
The total frequency of occurrence of (a) is,
Figure 123724DEST_PATH_IMAGE078
representing flows
Figure 6098DEST_PATH_IMAGE011
Whether or not to pass
Figure 982144DEST_PATH_IMAGE079
And (4) path routing.
9. A terminal, characterized in that it comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, performs the segment routing traffic engineering method according to any of claims 1-8.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the method of segment routing traffic engineering according to any one of claims 1 to 8.
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