CN112118176A - Service reliability-oriented comprehensive data network channel routing load optimization method - Google Patents

Service reliability-oriented comprehensive data network channel routing load optimization method Download PDF

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CN112118176A
CN112118176A CN202010881496.3A CN202010881496A CN112118176A CN 112118176 A CN112118176 A CN 112118176A CN 202010881496 A CN202010881496 A CN 202010881496A CN 112118176 A CN112118176 A CN 112118176A
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service
importance
risk
network
index
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CN112118176B (en
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崔鸣石
刘世民
孙毅
马德民
肖海龙
朱继阳
张勇生
李国强
王冲
王�华
杜娜
李彬
叶欣
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State Grid Corp of China SGCC
North China Electric Power University
Information and Telecommunication Branch of State Grid East Inner Mogolia Electric Power Co Ltd
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State Grid Corp of China SGCC
North China Electric Power University
Information and Telecommunication Branch of State Grid East Inner Mogolia Electric Power Co Ltd
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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
    • H04L45/12—Shortest path evaluation
    • H04L45/124—Shortest path evaluation using a combination of metrics
    • 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
    • H04L45/22—Alternate 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
    • H04L45/302—Route determination based on requested QoS
    • H04L45/306—Route determination based on the nature of the carried application

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Abstract

The invention belongs to the technical field of optimization and design of an integrated data network, and particularly relates to a traffic load optimization method of an integrated data network for service reliability. The invention provides a comprehensive data network route optimization mechanism based on service load balance aiming at the current situation that the service channels of a comprehensive data network are unevenly distributed and the operation risk of a power grid is increased, provides a TOPSIS method based on relative entropy for evaluating the service importance, analyzes the importance of the service borne by the network, synthesizes the conditions of equipment and links, the failure probability and the load characteristics, establishes an evaluation model of the service and the network risk, and provides a TOPSIS improvement method based on relative entropy for quantifying the service importance and a balanced service route selection algorithm. The invention can effectively balance the risk degree and the load of the network, effectively reduce the operation risk of the routing channel for a single service, and effectively relieve the risk problem caused by excessive concentration of the services in the whole network.

Description

Service reliability-oriented comprehensive data network channel routing load optimization method
Technical Field
The invention belongs to the technical field of optimization and design of an integrated data network, and particularly relates to a traffic load optimization method of an integrated data network for service reliability.
Background
With the rapid development of smart grids, the types and the number of electric power communication services are increased rapidly, and cooperative communication is required among all electric power subsystems, so that the service volume deployed in the network is more and more. As a main body of carrying a plurality of electric power services, an integrated data network (EPCN) is an important infrastructure of an electric power system, and services such as carrying scheduling, relay protection, security, telecontrol and the like are directly related to the safety of an electric power grid, and a plurality of core nodes or links carry a large amount of service data, so that the network resource allocation is uneven, and the failure risk is increased.
In the integrated data network, the key services are mainly on the transmission network composed of SDH, and are circuits using channels to carry different services to support the execution of services, including relay protection services, dc control services, automation, and safety control services. When the service is mapped to the transport layer and the medium layer, the service route is distributed in different channel layers. In such an environment, if the number of services carried on some channels is too large, the risk of too concentrated services is easily caused. The EPCN structure is increasingly complex, meanwhile, cases of breakage and failure of the power optical cable are frequent, the power grid service has typical industry specificity, and once the optical fiber in the integrated data network is broken and failed, the safety production and the stable operation of a power system are greatly influenced.
Therefore, it is necessary to optimize the routing of the power communication service, avoid multiple important services from being simultaneously carried on some bottleneck links, and comprehensively consider the load balancing strategy at the same time, so as to reduce the operation risk of the comprehensive data network and improve the reliability and throughput of the comprehensive data network.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a comprehensive data network access road load optimization method facing service reliability. The method aims to provide a TOPSIS service importance degree evaluation method based on relative entropy aiming at the characteristics of power services, and provides specific allocation and implementation schemes of working routes and protection routes of power communication services with different importance degrees meeting the requirements of resource utilization rate and reliability so as to improve the utilization rate of network resources and reduce the risk of service transmission.
The technical scheme adopted by the invention for realizing the purpose is as follows:
a comprehensive data network access road routing load optimization method facing service reliability comprises the following steps:
step 1, inputting network topology information and service requirement information, and initializing network parameters;
step 2, waiting for a service s request; the s refers to a service;
step 3, a service request s arrives; the s refers to a service;
step 4, solving the service carried by the comprehensive data network according to the importance;
step 5, judging whether the capacity of each link in the network is smaller than the bandwidth capacity required by service transmission; if yes, setting the link distance to be infinite, and then executing the step 6; otherwise, directly executing the step 6;
step 6, selecting the first shortest paths from the source node to the destination node in the network for the arrived service;
step 7, arranging the links in an ascending order to form a path set;
step 8, calculating the link risk, the node risk and the overall network risk of each path of the paths in sequence;
step 9, selecting two paths with the minimum overall risk of the network as a working path and a standby path;
step 10, calculating the average business risk of the working path and the standby path;
step 11, updating the free resource capacity and the working capacity of the network;
and step 12, repeating the process for the newly arrived service until the service is completely arrived.
Further, the calculating of the importance of the service carried by the integrated data network is calculated by using a TOPSIS service importance evaluation method based on relative entropy.
Further, in the step 4, the TOPSIS service importance evaluation method based on relative entropy includes the following steps:
step 4.1 index normalization processing;
step 4.2, determining the weight of the relative entropy;
and 4.3, calculating the service importance.
Further, the step 4.1 index normalization processing includes:
assuming that the set of performance indicators affecting the service is C ═ C1,c2，...,cnIn total, n, xijFor a service siAt the service index cjThe following parameter values, the normalization formula of the parameters are as follows:
Figure BDA0002654255500000021
wherein: i ∈ {1, …, m }, j ∈ {1, …, n }.
Further, the step 4.2 of determining the weight of the relative entropy includes:
let index cjAt service siDown value is x'ijHas a probability of yijAnd is represented as follows:
Figure BDA0002654255500000031
wherein: skRepresents: the kth service, s, denotes: traffic set, x'kjRepresents: service skAt the service index cjThe following parameter values;
at this time have
Figure BDA0002654255500000032
Further, each performance index cjThe decision information of (a) may be its entropy value hjTo show that:
Figure BDA0002654255500000033
wherein: siAs a service, yijIs probability, n is the number of performance index sets;
index cjThe degree of dispersion of the evaluation data was gj＝1-hjEasy to know the index cjAnd xijThe more the difference, gjThe larger the value, the more dispersed the evaluation data, and the index cjThe more information contained in the entropy measure, the higher the importance, so the entropy measure measures the index cjWeight w in the overall evaluationj：
Figure BDA0002654255500000034
Further, the step 4.3 of calculating the service importance includes:
calculating business importance di(ii) a For a certain performance index cjTo solve X in good thought+And negative ideal solution X-Respectively constructed as follows:
Figure BDA0002654255500000035
Figure BDA0002654255500000036
wherein x is1jRepresents: service s1At the service index cjPositive ideal solution of, x2jRepresents: for a service s2At the service index cjPositive ideal solution of, xkjRepresents: service skAt the service index cjA positive ideal solution;
Figure BDA0002654255500000037
and
Figure BDA0002654255500000038
is a service siThe distance between the importance of (a) and the positive ideal solution and the distance between the importance of (b) and the negative ideal solution are respectively calculated by the following formula:
Figure BDA0002654255500000041
Figure BDA0002654255500000042
service siBusiness importance of diThe calculation is as follows:
Figure BDA0002654255500000043
further, in the step 8, the link risk RE(x,y)Defined as the sum of the service importance of n services carried by the link (x, y) and the link failure rate 1-AEProduct of (x, y):
Figure BDA0002654255500000044
wherein d isiImportance of the ith service, AE(x, y) is fiber link availability with end point x, y and length d (x, y),
Figure BDA0002654255500000045
Figure BDA0002654255500000046
is the availability of mean-fault equipment or systems; the MTBF is an average fault interval time, which represents an average working time between two adjacent faults, i.e., how long the device or system can normally operate on average to cause a fault, and the higher the reliability of the system is, the longer the MTBF is; MTTR (mean Time To recovery) is the average recovery Time, which comprises the Time required for confirming the occurrence of failure and the Time required for maintenance, and the smaller the MTTR is, the better the recoverability is;
node risk RN(x)Defined as the ratio of the product of the sum of the service importance of n services carried by node x and the node failure rate 1-a to the node traffic admission value λ (x):
Figure BDA0002654255500000047
wherein, the traffic admission value λ (x) is the ratio of the idle capacity to the total link capacity on all links connected to x:
Figure BDA0002654255500000051
Figure BDA0002654255500000052
f (x, y) and W (x, y) are respectively idle capacity and total capacity on a link x-y, D (x, y) is the distance between two nodes, lambda (x) is the flow admission value of the node x, and lambda (x) is more than or equal to 0 and less than or equal to 1;
the overall network risk R is the sum of the link risk and the node risk in the network:
Figure BDA0002654255500000053
further, in step 10, calculating an average business risk of the working path and the backup path includes:
Figure BDA0002654255500000054
where e and v are the number of links and the number of nodes in the network, respectively.
A computer storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of the method for integrated data network access route-by-route load optimization for business reliability.
The invention has the following beneficial effects and advantages:
compared with the prior art, the invention provides a comprehensive data network routing optimization mechanism based on service load balancing aiming at the current situation that the service channels of the comprehensive data network are unevenly distributed to increase the operation risk of a power grid, so as to improve the utilization rate of network resources and reduce the service transmission risk.
The invention firstly provides a TOPSIS method based on relative entropy for evaluating the service importance, analyzes the importance of the service borne by the network, then integrates the conditions of equipment and links, failure probability and load characteristics, establishes an evaluation model of the service and the network risk, and then provides a TOPSIS improvement method based on relative entropy to quantify the service importance; finally, a balanced service routing algorithm is proposed.
The invention selects the first k shortest paths from the source node to the destination node in the network by using the KSP algorithm, calculates the link risk, the node risk and the overall network risk of each path of the k paths, selects two paths with the minimum overall network risk and respectively uses the two paths as the working paths PwAnd a backup path PbThe invention provides a comprehensive data network access road load optimization method facing service reliability.
The invention can effectively balance the risk degree and the load of the network, effectively reduce the operation risk of the routing channel for a single service, and effectively relieve the risk problem caused by excessive concentration of the services in the whole network.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic overall step diagram of the present invention;
fig. 2 is a schematic diagram of service distribution in the present invention;
fig. 3 is a schematic diagram of the occupation of link capacity in a certain network state according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
The solution of some embodiments of the invention is described below with reference to fig. 1-3.
Example 1
The invention relates to a comprehensive data network access road load optimization method facing service reliability, as shown in figure 1, figure 1 is an overall step schematic diagram of the invention, mainly comprising the following steps:
step 1, inputting network topology information and service requirement information, and initializing network parameters;
the network topology information specifically includes: network structure and node distribution information.
The service requirement information specifically includes: service level and service reliability requirement information.
Step 2, waiting for a service s request; the s refers to a service;
step 3, a service request s arrives; the s refers to a service;
step 4, calculating the importance of the service carried by the comprehensive data network by using a TOPSIS service importance evaluation method based on the relative entropy;
in the step 4, the TOPSIS service importance degree evaluation method based on the relative entropy comprises the following steps:
step 4.1 index normalization processing;
because the value range and the dimension of each service performance index are different, normalization processing needs to be performed when indexes such as time delay, bit error rate, reliability, bandwidth and the like of the service are compared and analyzed.
First, assume that the set of performance indicators affecting the service is C ═ C1,c2，...,cnIn total, n, xijFor a service siAt the service index cjThe value of the parameter is normalized by the formulaThe following:
Figure BDA0002654255500000071
wherein: i ∈ {1, …, m }, j ∈ {1, …, n }.
Step 4.2, determining the weight of the relative entropy;
in order to avoid the influence of subjective factors, the concept of entropy is introduced in the calculation of the evaluation index weight formula, and the weight of the evaluation index is determined according to the amount of information provided by each index. At this time, the index c can be setjAt service siDown value is x'ijHas a probability of yijAnd is represented as follows:
Figure BDA0002654255500000072
wherein: skRepresents: the kth service, s, denotes: traffic set, x'kjRepresents: service skAt the service index cjThe following parameter values.
At this time have
Figure BDA0002654255500000073
Further, each performance index cjThe decision information of (a) may be its entropy value hjTo show that:
Figure BDA0002654255500000074
wherein: siAs a service, yijAnd n is the number of the performance index sets.
Thus index cjThe degree of dispersion of the evaluation data was gj＝1-hjEasy to know the index cjAnd xijThe more the difference, gjThe larger the value, the more dispersed the evaluation data, and the index cjThe more information contained in the entropy measure, the higher the importance, and thus the entropy measure cjWeight w in the overall evaluationj：
Figure BDA0002654255500000081
And 4.3, calculating the service importance.
The idea of calculating the importance of each service of the integrated data network is to construct a positive ideal solution and a negative ideal solution, thereby calculating the importance d of the servicei. For a certain performance index cjTo solve X in good thought+And negative ideal solution X-Respectively constructed as follows:
Figure BDA0002654255500000082
Figure BDA0002654255500000083
wherein x is1jRepresents: service s1At the service index cjPositive ideal solution of, x2jRepresents: for a service s2At the service index cjPositive ideal solution of, xkjRepresents: service skAt the service index cjThe following positive ideal solution.
Figure BDA0002654255500000084
And
Figure BDA0002654255500000085
is a service siThe distance between the importance of (a) and the positive ideal solution and the distance between the importance of (b) and the negative ideal solution are respectively calculated by the following formula:
Figure BDA0002654255500000086
Figure BDA0002654255500000087
service siBusiness importance of diThe calculation is as follows:
Figure BDA0002654255500000088
step 5, judging whether the capacity of each link in the network is smaller than the bandwidth capacity required by service transmission; if yes, setting the chain distance to be infinite, and then executing the step 6; otherwise, step 6 is directly executed.
And 6, selecting the first k shortest paths (k is more than or equal to 2) from the source node x to the destination node y in the network by using the KSP algorithm according to the physical distance as the weight of the arrived service.
The KSP algorithm is a k shortest path algorithm and is the prior art.
And 7, arranging the k links according to the ascending order of the physical distance to form a path set P ═ { P ═1,p2,……,pk}。
Step 8, calculating the link risk R of each path of the k paths in sequenceE(x,y)Node risk RV(x)And overall risk of the network R.
In said step 8, the link risk RE(x,y)Defined as the sum of the service importance of n services carried by the link (x, y) and the link failure rate 1-AEProduct of (x, y):
Figure BDA0002654255500000091
wherein d isiImportance of the ith service, AE(x, y) is fiber link availability with end point x, y and length d (x, y),
Figure BDA0002654255500000092
Figure BDA0002654255500000093
is the availability of mean-fault equipment or systems; MTBF is Mean Time Between Failures (MTBF) and represents two adjacent MTBFThe average work time between failures, i.e. how long a device or system can operate normally on average, is a failure, the higher the system reliability, the longer the MTBF. MTTR (mean Time To recovery) is an average recovery Time including Time required for confirming the occurrence of a failure and Time required for maintenance, and the smaller the MTTR, the better the recoverability.
Node risk RN(x)Defined as the ratio of the product of the sum of the service importance of n services carried by node x and the node failure rate 1-a to the node traffic admission value λ (x):
Figure BDA0002654255500000094
wherein, the traffic admission value λ (x) is the ratio of the idle capacity to the total link capacity on all links connected to x:
Figure BDA0002654255500000095
Figure BDA0002654255500000096
f (x, y) and W (x, y) are respectively idle capacity and total capacity on a link x-y, D (x, y) is the distance between two nodes, lambda (x) is the traffic admission value of the node x, and lambda (x) is more than or equal to 0 and less than or equal to 1.
The overall network risk R is the sum of the link risk and the node risk in the network:
Figure BDA0002654255500000101
step 9, selecting the network integral risk in k paths
Figure BDA0002654255500000102
Two minimum paths, respectively as working paths PwAnd a backup path Pb；
Step 10, calculating a working path PwAnd a backup path PbAverage business risk R ofave。
In the step 10, the network average business risk RaveThe sum of the mean of link risk and node risk:
Figure BDA0002654255500000103
where e and v are the number of links and the number of nodes in the network, respectively.
And 11, updating the free resource capacity and the working capacity of the network.
And step 12, repeating the above processes for the newly arrived service until the service is completely arrived.
As shown in fig. 2, fig. 2 is a schematic view of service distribution in the present invention.
The definition graph G (V, E) represents the topology of the network, where V represents the set of nodes (devices), E represents the set of edges (links), the set of traffic is S, let S1For relay protection service, S2For energy management service, S3For substation automation service, S4For wide area phasor measurement traffic, S5For managing the service for a fault, wherein S1、S3、S4、S5The bandwidth requirement is 64kbit/s, and the service importance is 5; service S2The bandwidth requirement of (2 Mbit/s) and the service importance of (2), wherein the dotted line is the service channel. It can be seen from the figure that at Ev7v8There are too many services, and once the channel is abnormal or fails, there is a significant risk to the services of the integrated data network. If the link which carries important services in a relatively centralized manner fails, the stable operation of the power system is greatly influenced.
Example 2
The invention relates to a comprehensive data network access road load optimization method facing service reliability, as shown in fig. 3, fig. 3 is a schematic diagram of link capacity occupation under a certain network state in the invention.
The underlying medium of the transport network is a cable routing network, the labels on the links in the figure indicate the available free capacity, and the labels on the links indicate the total capacity that can be provided by the links, and assuming that the capacity occupancy of a certain link in the integrated data network is as shown in the figure, the node x is connected to the nodes a, b, c, y, and the links (x, a), (x, b), (x, c), (x, y) are (51, 84), (42, 64), (30, 72), (34, 96), respectively, so that the traffic admission value λ (x) of the node x is (51+42+30+34)/(84+64+72+96) is 0.50, and the traffic admission value λ (y) of the node y is 0.35.
Example 3
Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, where a computer program is stored on the computer storage medium, and when the computer program is executed by a processor, the method for optimizing routing load of an integrated data network access path facing service reliability according to embodiment 1 or 2 is implemented.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (9)

1. A comprehensive data network access road routing load optimization method facing service reliability is characterized by comprising the following steps:
step 1, inputting network topology information and service requirement information, and initializing network parameters;
step 2, waiting for a service s request; the s refers to a service;
step 3, a service request s arrives; the s refers to a service;
step 4, solving the service carried by the comprehensive data network according to the importance;
step 5, judging whether the capacity of each link in the network is smaller than the bandwidth capacity required by service transmission; if yes, setting the link distance to be infinite, and then executing the step 6; otherwise, directly executing the step 6;
step 6, selecting the first shortest paths from the source node to the destination node in the network for the arrived service;
step 7, arranging the links in an ascending order to form a path set;
step 8, calculating the link risk, the node risk and the overall network risk of each path of the paths in sequence;
step 9, selecting two paths with the minimum overall risk of the network as a working path and a standby path;
step 10, calculating the average business risk of the working path and the standby path;
step 11, updating the free resource capacity and the working capacity of the network;
and step 12, repeating the process for the newly arrived service until the service is completely arrived.
2. The method for optimizing traffic routing for business reliability according to claim 1, wherein the determining the traffic-oriented traffic carried by the integrated data network is determined by using a relative entropy based TOPSIS traffic importance evaluation method.
3. The method for optimizing the routing load of the integrated data network access road facing the business reliability as claimed in claim 1, wherein in the step 4, the TOPSIS business importance degree evaluation method based on the relative entropy comprises the following steps:
step 4.1 index normalization processing;
step 4.2, determining the weight of the relative entropy;
and 4.3, calculating the service importance.
4. The method for optimizing routing load of integrated data network access road facing service reliability according to claim 3, wherein the step 4.1 index normalization processing comprises:
assuming that the set of performance indicators affecting the service is C ═ C1,c2，...,cnIn total, n, xijFor a service siAt the service index cjThe following parameter values, the normalization formula of the parameters are as follows:
Figure FDA0002654255490000021
wherein: i ∈ {1, …, m }, j ∈ {1, …, n }.
5. The method for optimizing integrated data network access road by load according to claim 3, wherein the step 4.2 of determining the weight of the relative entropy comprises:
let index cjAt service siDown value is x'ijHas a probability of yijAnd is represented as follows:
Figure FDA0002654255490000022
wherein: skRepresents: the kth service, s, denotes: traffic set, x'kjRepresents: service skAt the service index cjThe following parameter values;
at this time have
Figure FDA0002654255490000023
Further, each performance index cjThe decision information of (a) may be its entropy value hjTo show that:
Figure FDA0002654255490000024
wherein: siAs a service, yijIs probability, n is the number of performance index sets;
index cjThe degree of dispersion of the evaluation data was gj＝1-hjEasy to know the index cjAnd xijIs poor to getThe more, gjThe larger the value, the more dispersed the evaluation data, and the index cjThe more information contained in the entropy measure, the higher the importance, so the entropy measure measures the index cjWeight w in the overall evaluationj：
Figure FDA0002654255490000025
6. The method for optimizing routing load of integrated data network for service reliability according to claim 3, wherein the step 4.3 of calculating the service importance degree comprises:
calculating business importance di(ii) a For a certain performance index cjTo solve X in good thought+And negative ideal solution X-Respectively constructed as follows:
Figure FDA0002654255490000026
Figure FDA0002654255490000031
wherein x is1jRepresents: service s1At the service index cjPositive ideal solution of, x2jRepresents: for a service s2At the service index cjPositive ideal solution of, xkjRepresents: service skAt the service index cjA positive ideal solution;
Figure FDA0002654255490000032
and
Figure FDA0002654255490000033
is a service siThe distance between the importance of (a) and the positive ideal solution and the distance between the importance of (b) and the negative ideal solution are respectively calculated by the following formula:
Figure FDA0002654255490000034
Figure FDA0002654255490000035
service siBusiness importance of diThe calculation is as follows:
Figure FDA0002654255490000036
7. the method for optimizing integrated data network access road by service reliability according to claim 1, wherein in the step 8,
link risk RE(x,y)Defined as the sum of the service importance of n services carried by the link (x, y) and the link failure rate 1-AEProduct of (x, y):
Figure FDA0002654255490000037
wherein d isiImportance of the ith service, AE(x, y) is fiber link availability with end point x, y and length d (x, y),
Figure FDA0002654255490000038
Figure FDA0002654255490000039
is the availability of mean-fault equipment or systems; the MTBF is an average fault interval time, which represents an average working time between two adjacent faults, i.e., how long the device or system can normally operate on average to cause a fault, and the higher the reliability of the system is, the longer the MTBF is; MTTR (mean Time To restore) isThe average recovery time comprises the time required for confirming the occurrence of failure and the time required for maintenance, and the smaller the MTTR is, the better the easy recovery is;
node risk RN(x)Defined as the ratio of the product of the sum of the service importance of n services carried by node x and the node failure rate 1-a to the node traffic admission value λ (x):
Figure FDA0002654255490000041
wherein, the traffic admission value λ (x) is the ratio of the idle capacity to the total link capacity on all links connected to x:
Figure FDA0002654255490000042
Figure FDA0002654255490000043
f (x, y) and W (x, y) are respectively idle capacity and total capacity on a link x-y, D (x, y) is the distance between two nodes, lambda (x) is the flow admission value of the node x, and lambda (x) is more than or equal to 0 and less than or equal to 1;
the overall network risk R is the sum of the link risk and the node risk in the network:
Figure FDA0002654255490000044
8. the method for optimizing integrated data network access road by load according to claim 1, wherein the step 10 of calculating the average service risk of the working path and the backup path comprises:
Figure FDA0002654255490000045
where e and v are the number of links and the number of nodes in the network, respectively.
9. A computer storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of a method for integrated data network traffic routing optimization for business reliability according to claims 1-9.
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