CN108768874B - Method for relieving blocking performance of avionics network based on command efficiency loss - Google Patents

Method for relieving blocking performance of avionics network based on command efficiency loss Download PDF

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CN108768874B
CN108768874B CN201810542775.XA CN201810542775A CN108768874B CN 108768874 B CN108768874 B CN 108768874B CN 201810542775 A CN201810542775 A CN 201810542775A CN 108768874 B CN108768874 B CN 108768874B
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任齐凤
崔亮
曲国远
刘永根
李左民
张建东
史国庆
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China Aeronautical Radio Electronics Research Institute
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Abstract

The invention provides a research method for relieving blocking performance of an avionics network based on command efficiency loss. Based on the theory of the complex network, the method combines the principle of message balanced flow distribution and identifies the bottleneck of the avionic network by searching the minimum support tree and the minimum support cluster. On the basis of finding out network bottlenecks, a better scheme is found to improve the message transmission capability of the avionic network, so that a borrowable decision scheme is provided for effectively relieving network congestion. A more comprehensive analysis method is provided for the design of the avionics network.

Description

Method for relieving blocking performance of avionics network based on command efficiency loss
Technical Field
The invention relates to a research method for relieving blocking performance of an avionic network based on command efficiency loss. Belonging to the field of network modeling and analysis of avionics systems.
Background
In the process of studying the congestion of a communication network, the communication capacity of the whole network is usually measured by using the critical message packet generation rate. When the number of message packets generated in the network and the number of message packets arriving at the destination are offset, the network is illustrated as being in a free communication state. However, if the information processing capability of the node is limited or the length of the node buffer queue is limited, the number of the accumulated message packets increases with time, so that the transmission time of the message packets increases rapidly, and at this time, if the system is not controlled in time, the system is in a blocking state or even a breakdown state.
The integrated avionics system comprises a plurality of subsystems between which there is continuous data communication, including the reception or transmission of messages. Modern avionics systems often use bus commands to accomplish the scheduling of message flows throughout the network. The command efficiency reflects the effect of the transmission of message streams in the avionics network. The low message transmission delay means that the command efficiency is high, otherwise, the command efficiency is reduced due to the blocking of the message transmission. With the increase of functional subsystems, the amount of information needing to be transmitted by the avionic network is increased, so that the network load is increased, and the complexity of command scheduling is greatly improved. How to avoid message retention and loss caused by message increase is an important subject of the optimization of the avionic network structure performance. The network message transmission bottleneck is a very important factor influencing the network message smoothness, and most network congestion is directly or indirectly related to the network bottleneck.
The invention analyzes key structural characteristics influencing command efficiency and information transmission and avionics network bottleneck from the characteristics of an avionics network structure so as to solve the problem of avionics network blockage and optimize the system performance of the avionics network.
Disclosure of Invention
The invention aims to provide a research method for relieving the congestion of an avionic network based on command efficiency loss, and the method is used for researching the influence of the command efficiency loss in the avionic network on the network congestion so as to optimize the network and improve the message transmission efficiency of the avionic network.
The method is mainly characterized in that the avionic network bottleneck is identified by searching a minimum support tree and a minimum support cluster in an avionic network structure based on a correlation theory of a complex network and combining a message balance flow distribution principle. Aiming at the bottleneck of the avionic network, a corresponding scheme is provided to improve the message transmission capability of the avionic network under different network structures, so that a borrowable decision-making scheme is provided for effectively relieving network congestion, and a more comprehensive analysis method based on network dynamics is provided for the design of the avionic network.
According to the invention, from the characteristics of the message balance behavior and the system optimal behavior in the avionic network embodied in different avionic networks, the influence of the topological characteristic on the message transmission characteristic of the avionic network is found, and a corresponding solution is provided to improve the command efficiency of the avionic network.
The invention aims to be realized by the following technical scheme:
a method for relieving blocking performance of an avionics network based on command efficiency loss comprises the following specific steps:
the method comprises the following steps: analyzing the influence of the selection behavior of the message on each edge in the avionic network on the avionic network message flow, specifically:
1.1) calculate the total system impedance under message equalization behavior:
Figure BDA0001679364740000021
wherein x isaIndicating the traffic on the network under normal conditions, taRepresenting the impedance on edge a;
1.2) uniformly scheduling and controlling all messages of the avionic network to form a message flow state which enables the total impedance of the system to be minimum, namely the optimal behavior of the system;
1.3) characteristics of message balance behaviors and system optimal behaviors reflected on different avionic network topologies are researched, the influence of the network topology characteristics on message transmission characteristics is found, and the general rules of the message transmission characteristics and network congestion on a minimum support tree are disclosed:
t＝t0[1+α*(q/c)β]
where t is the time required for the message to actually pass through the edge, t0The unit is pcu/h, c is the actual traffic capacity of the edge, and the unit is pcu/h, alpha and beta are undetermined parameters of the model;
step two: respectively carrying out message balance flow distribution on each network topology structure in the avionic network by utilizing the first step, and further obtaining command efficiency loss caused by message balance under each topology;
Figure BDA0001679364740000031
wherein, TC represents the total impedance of the system, subscripts SO and UE represent the optimal behavior and message equalization state of the system, respectively, and the larger L, the larger the efficiency loss, and vice versa.
Step three: establishing a minimum support tree of a minimum support tree zero-flow impedance, a minimum flow support tree and a minimum support cluster of the avionic network;
computing a zero flow impedance minimum support tree MCThe flow rate of the upper part accounts for the total flow rate of the system:
Figure BDA0001679364740000032
compute flow minimum support Tree MFThe flow rate of the upper part accounts for the total flow rate of the system:
Figure BDA0001679364740000033
calculating the minimum support cluster IIC and the minimum support tree M of zero flow impedanceCFlow ratio of (pi):
Figure BDA0001679364740000034
wherein the content of the first and second substances,
Figure BDA0001679364740000035
minimum support tree M representing zero flow impedanceCThe set of the middle edges is selected,
Figure BDA0001679364740000036
support tree M representing minimum flowFSet of middle edges, TF (I) denotes the flow of IIC, TF (M)C) Representing a minimum support tree MCThe flow rate of (c).
Step four: identifying the bottleneck of the avionic network, and giving a blocking parameter:
total degree of occlusion J:
Figure BDA0001679364740000037
degree of blocking J on edge aa：
Figure BDA0001679364740000038
Transmission on edge aForce transmission Ua：
Figure BDA0001679364740000041
Zero flow impedance t0a：t0a＝γ×(ki+kj)
OD requirement qrs：
Figure BDA0001679364740000042
Step five: determining the correlation omega of the blocking degree of the avionic network and the blocking degree of the zero-flow impedance minimum support tree:
Figure BDA0001679364740000043
j (O) represents the degree of blocking of each edge on the avionics network, J (M)C) Indicating the degree of blocking of each edge on the zero flow impedance minimum support tree.
Step six: analyzing the performances of the following strategies to provide a final strategy for relieving the avionic network congestion:
strategy 1: the traffic capacity of the side with the maximum ratio of the flow to the capacity on the minimum support tree is improved;
strategy 2: the traffic capacity of the side with the maximum capacity on the minimum support tree is improved;
strategy 3: the ability to minimally support the top of the cluster is improved.
The invention has the beneficial effects that: based on the theory of the complex network, the method combines the principle of message balance flow distribution and identifies the bottleneck of the avionic network by searching the minimum support tree and the minimum support cluster. On the basis of finding out network bottlenecks, a better scheme is found to improve the message transmission capability of the avionic network, so that a borrowable decision scheme is provided for effectively relieving network congestion. A more comprehensive analysis method is provided for the design of the avionics network.
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FIG. 1 is a flow chart of a research method for mitigating avionics network congestion based on command efficiency loss.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The method comprises the following specific steps of:
the method comprises the following steps: analyzing the impact of individual selection behavior of an avionics network on avionics network message traffic
The structural complexity study for complex networks stems from two basic ideas:
1) giving an underlying topology through a general network generation mechanism, and performing various dynamic evolutions on the topology;
2) the dynamic behavior on the network in turn affects the network topology evolution.
The invention focuses on researching the characteristics of message balance behavior and system optimal behavior reflected on different avionic networks, finds the influence of topological characteristics on the avionic network message transmission characteristics and reveals the message transmission characteristics and the general rule of network congestion in the minimum support tree.
Due to the blocking effect of the avionic network, if each message reaches a destination terminal through the shortest route, the situation that a plurality of message routes have a common edge occurs, so that the message traffic of the common edge is more, and the possibility of congestion is generated. As the message traffic in the network increases, the message traffic on one or more edges in the network may exceed the maximum load, thereby causing congestion. And repeatedly redistributing the message flow on the network, and if each message is selected to be the path with the shortest transmission time, finally achieving a message balance state, namely meeting the first principle of Wardrop.
In the message balance state, each message is searching for the minimum impedance path, the transmission between the messages is not coordinated, after continuous internal adjustment, a balance is achieved, and the corresponding total system impedance is shown as formula (1)
Figure BDA0001679364740000051
But not necessarily the smallest. Wherein xaIndicating the traffic on the network under normal conditions, taRepresenting the impedance on edge a. From the point of view of the control of the entire avionics network system path, it is desirable to minimize the total impedance of the system, a condition known as the system optimum condition. Assuming that all messages of the avionics network are uniformly scheduled and controlled, the resulting message flow state can minimize the total impedance of the system.
System message balancing is substantially similar to system optimization, except that the objective function is different. Message balancing is the same as system optimization when the congestion effect of the network is omitted. When the message demand of the network is large to a certain extent, congestion occurs, and thus a large difference occurs between message balance and system optimization. The invention reflects the influence of individual selection in the message transmission path on the message flow of the avionic network through the impedance function of the messages on each edge.
The correction of the message transmission time in the avionic network can be determined according to the relation between the transmission time and the message quantity passing through the edge, namely the road resistance function. The BPR is usually characterized by a nonlinear function form, and the specific formula is as follows:
t＝t0[1+α*(q/c)β] (2)
where t is the time required for the message to actually pass through the edge, t0The time required for the message to freely pass through the edge is q, the message amount actually existing on the edge is pcu/h, c is the actual traffic capacity of the edge, pcu/h is a unit, alpha and beta are model undetermined parameters, and the suggested values are 0.15 and 4 respectively.
And calculating the time impedance on each edge in the avionic network based on the defined road resistance function, and further analyzing the influence of the selection of the edges in the avionic network on the avionic network message flow.
Step two: analyzing avionics network message balance efficiency losses for different network topologies
According to different network abstraction methods, the avionic network presents various network topology forms which can be mainly divided into a regular network, a random network, a small-world network and a scale-free network. And (3) respectively carrying out message balance flow distribution on the four different network topologies based on the method described in the step one, and obtaining the flow distribution condition of each edge in the network when the flow reaches a balanced state, thereby obtaining the total impedance of the network. The efficiency ratio L of different topologies can be determined by the formula (3), and then the upper bound of efficiency loss is obtained by analysis:
Figure BDA0001679364740000061
where TC represents the total system impedance. The subscripts SO and UE indicate system-optimal and message-optimal behavior, respectively. When the system is optimal, the impedance is minimal, therefore, L ≧ 1, L-1 is referred to as the command efficiency loss due to message balancing behavior. The greater L, the greater the efficiency loss, and vice versa.
First, consider the case where the impedance function is linear (consider a simple case t)a＝t0a+2xa) And obtaining a message balance efficiency loss table on the regular network, the small-world network, the random network and the scale-free network topology, and knowing that the efficiency loss is about 25.8% at most for the four network topologies.
And respectively establishing the relationship between the efficiency ratio L and the blocking degree J in different network topologies and the network scale N, and analyzing the relationship between the blocking degree and the efficiency ratio of the avionic network in different topologies and the network scale thereof.
Step three: computing minimum support trees and minimum support clusters for avionics networks
The avionic network system is used as a weighting network and mainly bears the transmission task of message flows, but the flow carrying capacity of each side in the avionic network has respective upper limit, the message flow of a certain side cannot be infinitely increased, the side with large accommodating impedance is easy to become a bottleneck due to unsmooth message flow transmission, and once the side is formed, the side can be diffused and transferred to an adjacent side, so that the efficiency of the whole avionic network is influenced. To reveal the role of these edges in the network, the concept of a minimal support tree is introduced. The invention adopts Kruskal algorithm to calculate the minimum support tree.
Kruskal algorithm
Each time N-1 edges are selected, the greedy criterion used is: the edge with the least cost that does not generate a loop is selected from the remaining edges and added to the set of selected edges. Note that the selected edges cannot form a spanning tree if they create loops. The Kruskal algorithm is divided into N steps, where N is the number of edges in the avionics network. The N edges are considered in order of increasing impedance on the edges, one edge at a time. When an edge is considered, if a loop occurs by adding it to the set of selected edges, it is discarded, otherwise it is selected.
Constructing a zero flow impedance minimum support tree M based on the Kruskal algorithm and the definition of each edge impedance under the condition of message equalization described in the step oneC. Each time a support tree is found, several smallest support trees of the same weight may appear, but all with equal smallest weights. If this occurs, a support tree is randomly selected from among them for study. When the flow reaches the balance state, the proportion of the zero-flow-impedance minimum support tree in the total flow of the system is obtained, and the definition is shown in the formula (4).
Zero flow impedance minimum support tree MCAnd minimum flow support tree MFThe ratio of the flow of (a) to the total flow of the system can be expressed as follows:
Figure BDA0001679364740000081
and
Figure BDA0001679364740000082
wherein the content of the first and second substances,
Figure BDA0001679364740000083
Figure BDA0001679364740000084
and
Figure BDA0001679364740000085
respectively representing a zero-flow impedance minimum support tree MCAnd flowMinimum support tree MFA set of medium edges.
Similar to the minimal support tree, minimal support clustering (IIC) is also a structural feature that describes avionics network messaging. Compared with the minimum support tree, the minimum support cluster is smaller, and the most important characteristic is that the minimum support cluster has extremely high clustering coefficient. The message transmission characteristics of the minimum support cluster are researched on the basis of the minimum support tree, so that the blocking range can be more clearly limited. The method for removing the edge can be used for obtaining the minimum support cluster of the avionics network, and the specific process is as follows:
firstly, the sides of the network are arranged in descending order according to the weight, and then the arranged sides are removed in sequence. Calculating the minimum support clustering coefficient kappa ≡ k when removing one edge2>/< k >. Obviously, κ will gradually decrease as edges are removed. The process is repeated continuously until the algorithm is stopped when k is less than 2, and the maximum component reserved in the network is the minimum support cluster.
Defining IIC and M based on concept of minimum support clusteringCHas a flow rate ratio of pi, and
Figure BDA0001679364740000086
wherein TF (I) represents the flow of IIC, TF (M)C) Representing a minimum support tree MCThe flow rate of (c).
Step four: bottleneck identification and blocking parameter setting of avionics network
The bottleneck in the avionics network is a weak link in the avionics network. Bottlenecks refer to the phenomenon that the supply capacity of an edge in a network is less than the requirements of an existing network. An important feature is that congestion often occurs, thereby increasing the time for message delivery on the path and deteriorating the avionics network environment. There are two kinds of bottlenecks: static and dynamic; when the message traffic of an edge or node of the network exceeds the transmission capability, the message packet speed passing through the edge or node is reduced, and the congestion degree is increased, which is called that a bottleneck exists at the position of the edge or node. Based on a macroscopic complex network theory, on the basis of identifying the bottleneck of the avionic network, an effective strategy for improving overall and local congestion is provided for different network topologies.
In general, networks can be divided into a uniform network and a non-uniform network, and a random network can be regarded as a uniform network, while a scale-free network is non-uniform.
Bottlenecks in an avionics network refer to the phenomenon that the supply capacity of the edges in the network is less than the requirements of the existing network. An important feature is that congestion often occurs, thereby increasing the time for message delivery on the path and reducing the efficiency of the avionics network.
The bottleneck is characterized by the occurrence of blockage, and the invention defines the following parameters to describe the blockage in the avionics network from different angles:
the blocking degree J indicates the overall congestion degree of the network, and is defined as the blocking degree J according to the definition of the impedance in the step one
Figure BDA0001679364740000091
Wherein xaIndicating the traffic on the network under normal conditions, taRepresenting the impedance at edge a, t0aRepresenting zero flow impedance on edge a.
Degree of blocking J on a certain side aa；
Figure BDA0001679364740000092
Transmission capability U on edge aaThere should be some relation to the degree of the node to which the edge is connected, defining that equation (7) is satisfied:
Figure BDA0001679364740000093
where ψ is a control parameter, and i and j are nodes at both ends of the connecting edge a, respectively.
The zero flow impedance is given by equation (8),
t0a＝γ×(ki+kj) (8)
wherein the parameter gamma is a random number on (0, 1).
OD requirement qrsGiven by the modified gravity model, satisfy
Figure BDA0001679364740000101
Wherein χ is a control parameter.
Step five: determining a correlation between a blockage level of an avionics network and a blockage level of a zero flow impedance minimum support tree
Based on a macroscopic complex network theory, on the basis of identifying the bottleneck of the avionic network, an effective strategy for improving overall and local congestion is provided for different network topologies.
Carrying out message balanced flow distribution on different avionic network topologies according to the method described in the first step, the second step and the third step to obtain the minimum zero flow impedance support tree M of the different avionic network topologies under the condition of uniform messagesCRatio P (M) of flow to total flow of systemC) And (4) a graph of the variation relationship with the network size N. And determining the correlation omega of the blocking degree of the avionic network and the blocking degree of the zero-flow impedance minimum support tree according to the Pearson correlation coefficient, wherein the correlation omega is defined as the following formula (10).
Figure BDA0001679364740000102
J (O) and J (M)C) Respectively representing the blockage degree of the avionic network and the blockage degree of each side of the zero-flow-impedance minimum support tree.
The calculation result shows that the zero flow impedance minimum support tree M for the scale-free networkCAnd minimum support cluster MFA large amount of traffic transmission work is borne, and therefore, clogging is most easily generated to cause a sharp increase in impedance. In particular, the blocking factor of the smallest supporting cluster is about 5 times more than the blocking factor of the whole system. It can be considered as a major bottleneck for the network to create congestion. For random networks, the blocking degree of the minimum support tree of the zero flow impedance is much greater than that of the minimum support cluster, so that the blocking degree of the minimum support cluster of the zero flow impedance can be greatly increasedThe minimum support tree is considered to be the main bottleneck for random network blocking.
Step six: providing a strategy for relieving the blocking of the avionic network, analyzing the characteristics of the strategy and giving a use suggestion
For the three proposed strategies for alleviating network congestion, the effect of these strategies on alleviating congestion is compared.
1) The traffic capacity of the side with the maximum ratio of the flow to the capacity on the minimum support tree is improved (strategy 1);
2) the traffic capacity of the side with the maximum capacity on the minimum support tree is improved (strategy 2);
3) the ability to minimally support the top of the cluster is improved (strategy 3);
a common method for alleviating local network congestion is to improve the message transmission capability of the blocking edge, and the method is divided into two categories:
1) from a system perspective, it is desirable to improve the performance of avionics systems, reducing the congestion level of the avionics network as much as possible, but in some cases at the expense of increased local network congestion;
2) from some local reasons, the message transmission capability of one or some key edges is improved on the premise of not deteriorating the system environment. Therefore, it is also one of the common methods to alleviate network congestion of critical edges by improving local reasoning ability.
Relieving the blocking performance of the avionic network, firstly improving the capacity of all edges in the minimum support cluster according to a strategy 3, comparing the capacity with the strategies (1) and (2), obtaining the cost ratio of the improved minimum support cluster and the improved minimum support tree, and obtaining the minimum support tree M of the network under the conditions of different network topologies and different improvement strategiesCAnd analyzing the effect of three different strategies on the blockage relieving effect by using a change relation graph between the blockage degree of the minimum support cluster I and the coefficient tau.
It can be seen that the congestion improvement effect of the three strategies on the whole network, the minimum support tree M and the minimum support cluster I is different. For non-uniform networks, the impact of strategy 1 and strategy 2 on the total impedance of the system is almost the same, but strategy 1 has a better degree of blocking mitigation for the global and minimum support trees than strategy 2, compared to strategy 2, which is the optimal solution to improve the degree of blocking for the minimum support cluster.
Employing strategies 1 and 2 may result in the least supported clustering creating a more serious congestion problem, which also indicates that the bottleneck has shifted. Strategies 1 and 2 are therefore effective in alleviating the global blocking problem.
It should be understood that equivalents and modifications of the technical solution and inventive concept thereof may occur to those skilled in the art, and all such modifications and alterations should fall within the scope of the appended claims.

Claims (1)

1. A method for relieving blocking performance of an avionics network based on command efficiency loss comprises the following specific steps:
the method comprises the following steps: analyzing the influence of the selection behavior of the message on each edge in the avionic network on the avionic network message flow, specifically:
1.1) calculate the total system impedance under message equalization behavior:
Figure FDA0003356033900000011
wherein x isaIndicating the traffic on the network under normal conditions, taRepresenting the impedance on edge a;
1.2) uniformly scheduling and controlling all messages of the avionic network to form a message flow state which enables the total impedance of the system to be minimum, namely the optimal behavior of the system;
1.3) characteristics of message balance behaviors and system optimal behaviors reflected on different avionic network topologies are researched, the influence of the network topology characteristics on message transmission characteristics is found, and the general rules of the message transmission characteristics and network congestion on a minimum support tree are disclosed:
t＝t0[1+α*(q/c)β]
where t is the time required for the message to actually pass through the edge, t0Required for free passage of messages on that edgeQ is the message quantity actually existing on the edge, the unit is pcu/h, c is the actual traffic capacity of the edge, the unit is pcu/h, and alpha and beta are undetermined parameters of the model;
step two: respectively carrying out message balance flow distribution on each network topology structure in the avionic network by utilizing the first step, and further obtaining command efficiency loss caused by message balance under each topology;
Figure FDA0003356033900000012
wherein, TC represents the total impedance of the system, subscripts SO and UE represent the optimal behavior and message equilibrium state of the system respectively, and the larger L is, the larger the efficiency loss is, otherwise, the smaller L is;
step three: establishing a minimum support tree of a minimum support tree zero-flow impedance, a minimum flow support tree and a minimum support cluster of the avionic network;
computing a zero flow impedance minimum support tree MCThe flow rate of the upper part accounts for the total flow rate of the system:
Figure FDA0003356033900000021
compute flow minimum support Tree MFThe flow rate of the upper part accounts for the total flow rate of the system:
Figure FDA0003356033900000022
calculating the minimum support cluster IIC and the minimum support tree M of zero flow impedanceCFlow ratio of (pi):
Figure FDA0003356033900000023
wherein the content of the first and second substances,
Figure FDA0003356033900000024
Figure FDA0003356033900000025
minimum support tree M representing zero flow impedanceCThe set of the middle edges is selected,
Figure FDA0003356033900000026
support tree M representing minimum flowFSet of middle edges, TF (I) denotes the flow of IIC, TF (M)C) Representing a minimum support tree MCThe flow rate of (a);
step four: identifying the bottleneck of the avionic network, and giving a blocking parameter:
total degree of occlusion J:
Figure FDA0003356033900000027
degree of blocking J on edge aa：
Figure FDA0003356033900000028
Transmission force U on edge aa：
Figure FDA0003356033900000029
Zero flow impedance t0a：
Figure FDA00033560339000000210
OD requirement qrs：
Figure FDA00033560339000000211
Wherein psi is a control parameter, i and j are nodes at two ends of the connecting edge a respectively, the parameter gamma is a random number on (0, 1), and χ is a control parameter;
step five: determining the correlation omega of the blocking degree of the avionic network and the blocking degree of the zero-flow impedance minimum support tree:
Figure FDA0003356033900000031
j (O) represents the degree of blocking of each edge on the avionics network, J (M)C) Representing the blocking degree of each edge on the minimum support tree of the zero-flow impedance;
step six: analyzing the performances of the following strategies to provide a final strategy for relieving the avionic network congestion:
strategy 1: the traffic capacity of the side with the maximum ratio of the flow to the capacity on the minimum support tree is improved;
strategy 2: the traffic capacity of the side with the maximum capacity on the minimum support tree is improved;
strategy 3: the ability to minimally support the top of the cluster is improved.
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