CN112564966B - Service time sequence coupling congestion analysis method based on network cells - Google Patents
Service time sequence coupling congestion analysis method based on network cells Download PDFInfo
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract
The invention provides a service time sequence coupling congestion analysis method based on network cells, which comprises the following steps: step (ii) ofFirstly, acquiring network initial information of a network to be analyzed, and secondly, establishing a model of network cells in the network; step three, determining a cell operation rule; step four: deducing service congestion evolution based on the time sequence coupling of the network cells; step five, using the system sequence parameter based on the network cell as a statistical characteristic parameter for describing the network congestion state, step six, obtaining a variation process diagram of the sequence parameter under different newly-added user request flows, and obtaining a phase change condition X causing the network congestion to formc. The method and the device can support description and modeling of complex multi-service with time sequence coupling characteristics, are beneficial to researching the influence of the multi-service time sequence coupling characteristics on network congestion, and further can support guiding adjustment and planning of the time sequence relation of multi-service operation of the network so as to avoid network congestion.
Description
Technical Field
The invention relates to the technical field of reliability and system safety, in particular to a service time sequence coupling congestion analysis method based on network cells.
Background
Networks typically provide multiple services with tight timing coupling relationships to meet the diverse needs of users. In the multi-service operation process, the time sequence coupling relationship among the services can aggravate or slow down the congestion of part of nodes, so that the congestion evolution of the whole network is influenced significantly, and the research on the congestion evolution under the multi-service with the time sequence coupling characteristic has great significance for adjusting and planning the time sequence of the service in the network. Existing congestion evolution models, such as a queuing theory model, an information propagation dynamics model, a cascading failure model and the like, only concern about the transfer of traffic in a network, the traffic is the result of multi-service operation, the models are simple for service modeling, and the specific process of providing services for users by network multi-service is difficult to describe only from the traffic perspective. Therefore, a new model and method are needed to support analysis of congestion evolution under multi-service with time sequence coupling characteristics and support service optimization design oriented to network functions.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a service time sequence coupling congestion analysis method based on network cells, which can provide a theoretical research basis for further researching service characteristics and the influence rule of the multi-service coupling characteristics on network congestion evolution in the follow-up process, and further support adjustment and planning of network services of the multi-service characteristics considering the time sequence coupling characteristics so as to avoid network congestion.
The invention considers each service in the network as a cell of the network, and the multi-service on the network can be described as a network function formed by connecting a plurality of network cells through the cell. Further, the congestion evolution under the network multi-service time sequence coupling is regarded as a congestion evolution process under a network cell and a cell connection thereof based on a cell operation rule, and a service time sequence coupling congestion analysis method based on the network cell is provided, and the congestion analysis method specifically comprises the following two parts: 1) establishing a congestion evolution model based on network cells; 2) a congestion evaluation method based on network cells is provided. The method starts from complex multi-service requirements, network services are characterized as network cells with certain structure and functional attributes, evolution rules of network congestion are abstracted into local operation rules of the cells, a multi-service coupling congestion evolution model based on the network cells is innovatively provided, and sequence parameter parameters based on the network cells are provided to evaluate the congestion state of the whole network, so that an effective analysis method is provided for network congestion under multi-service time sequence coupling to support the design and planning of the network services.
The service time sequence coupling congestion analysis method based on the network cells comprises the following specific processes:
the method comprises the following steps: acquiring network initial information of a network to be analyzed, wherein the network initial information comprises network topology information and service information;
determining network initial information of a network to be analyzed, wherein the network is a double-layer network consisting of network topology and service, and the upper layer is a service layer consisting of service (APP); the lower layer is a network topology layer and is represented as G ═ V, E, wherein G is a network topology structure chart, V is a set of vertexes, and E is a set of edges; the network initial information comprises network topology information and service information, wherein the network topology information comprises network topology and network resources, such as the number of nodes and the number of links; the service information comprises a service flow, service starting time, service interval time and service capacity;
step two: establishing a model of network cells in a network;
step three: determining a cell operation rule;
defining cell operation rules in the network cell model, including an internal operation mechanism of the network cell, interaction rules between the network cell and an external environment and between the network cell and the network cell, so as to describe the transfer process of the flow among different service flows;
step four: the method for deducing service congestion evolution based on time sequence coupling of network cells specifically comprises the following steps:
4.1, inputting network initial information and new user request flow, wherein the network initial information comprises network topology information and service information;
4.2 establishing a programmed network cell model according to the second step;
4.3 initializing the network;
4.4, according to the input new user request flow, obtaining the new user request flow added by each network cell, and loading the new user request flow to the current existing accumulated user request flow of the network cell;
4.5 according to the network cell function starting time t0, the network cell operation interval time b and the current time t, sequentially judging whether each network cell needs to add a new functional site in the time step, if so, adding the new functional site and enabling the real-time load of the functional site to be 0;
4.6 for each network cell, removing a certain proportion of alpha network flow from the real-time load of the functional site, removing a certain proportion of beta network flow from the real-time load of the functional site, and averagely transmitting the beta network flow to the neighbor network cells connected with the network cells;
4.7 for each network cell, on the premise of meeting the maximum service capacity, receiving the user request flow on the functional site and other cell forwarding flows transmitted to the network cell by other cells; correspondingly updating the user real-time load flow of each network cell, the accumulated user request flow and the flow transmitted to the network cell by other cells;
4.8 judging the functional sites on each network cell in turn, if the functional sites are at the target nodes of the business process in the network cells, removing the functional sites; otherwise, the functional site moves to the next node of the service flow according to the service flow;
4.9t ═ t +1, If t > Time, go to step 4.10; otherwise, go to step 4.4;
4.10, finishing;
step five: using the system sequence parameters based on the network cells as statistical characteristic parameters for describing the network congestion state;
the sequence parameter of the whole network in the network cell congestion evolution is expressed as:
wherein, the number of the network cells is M, and the accumulated user request flow on the network cell i is UiThe flow rate forwarded by the neighbor network cell of the network cell i is ViThe real-time load flow of the network cell i is LiAt each moment, the average value of newly generated request flow of each network cell is Z, (U)i+Vi+Li) Represents the network traffic, Δ (U), associated with the network cell ii+Vi+Li) To account for the change in cell i-related network traffic over the at time,
the sum of the related network flow changes of all cells in the whole network in delta t time, MZ is the sum of the user request flow newly generated by all cells on the network at each moment, and further when etacell→ 0, the network is in a 'free flow' state, and the traffic on the network can be transmitted in time; when etacellWhen the flow rate is more than 0, the network is in a congestion state, and the newly generated flow rate in the network is more than the transmitted flow rate; different taking of etaThe values describe different degrees of network congestion, when ηcell→ 1, full network congestion collapses;
step six: inputting different newly-added user request flow X under the condition that other parameters are the same in the step four, and obtaining the sequence parameter eta according to the step fiveceilObtaining the variation process diagram of the sequence parameters of different newly-added user request flows by using a point tracing method, and obtaining the phase change condition X causing the network congestion to formc。
Preferably, step two: the establishment of the network cell model in the network specifically comprises the following steps:
programming a specific model of network cell i:
wherein for Cellr(i)Denotes cell i, cell structure S of cell ir(i)Cell function Fr(i)And cell attachment Cr(i)The three parts are formed, i is a natural number; sr(i)Represents the cellular structure of cell i, line _ q represents link q; fr(i)And the cell function of the cell i is represented, and the cell function comprises the starting time of the network cell function, the running interval time of the network cell, the capacity of the network cell and the current real-time load of the network cell which are x, v, b and n in sequence. Cr(i)Cell junction representing Cell i, including other cells in coupled relation with Cell i, and coupling nodes or coupling links of Cell i with other cells, Cellr(h)Represents cells h, qmRepresents a coupling node between the cell i and the cell h, and q is a coupling node between the cell i and the cell hmIs a collection of coupled nodes, Cellr(j)Represents cell j and line _ k represents the coupling link k of cell i and cell j, where i, h and j are not equal.
Preferably, step three: determining a cell operation rule model, determining a cell operation rule in the network cell model, wherein the cell operation rule comprises an internal operation mechanism of the network cell, interaction rules between the network cell and an external environment and interaction rules between the network cell and the network cell, and the interaction rules specifically comprise the following steps:
the method comprises the following steps of network cell function self-updating:
1) the functional site of the network cell is transmitted to the next node in the service flow in turn from the source node O of the service flow at each time step according to the service flow of the network cell until the destination node D in the service flow disappears;
2) each interval TiIn each time step, a new functional site is added to the network cell, and the initial functional site is a source node O of the network cell business process;
secondly, the network cell reaction is carried out, namely, network flow with a certain proportion of alpha in the network cell load is removed at the functional site of the network cell; without loss of generality, at a certain moment, all the flow in the network cells is removed, so that the value range of alpha is more than or equal to 0 and less than or equal to 1;
the interaction among network cells:
1) at the network cell functional site, the network cell i sequentially receives the network flow V transmitted to the network cell by the neighbor network celliAnd accumulating the user request traffic UiInducing real-time loading of network cells LiWhen the real-time load L is increasedi＝DiWhen it is time to stop loading, DiThe maximum processing capacity of the cell i is shown, and the newly increased user request flow of the jth node of the ith network cell service process is xij，xijIs a random number, but in nodes of the overall network cell traffic flow xijObey [0, X]Uniformly distributing, wherein X is called the request flow of the newly added user;
2) at the network cell functional site, the network cell transmits a certain proportion of beta network flow on the network cell to the neighbor network cell, wherein beta is more than or equal to 0 and less than or equal to 1, and beta + alpha is less than or equal to 1.
Preferably, step four: each step in the derivation of service congestion evolution based on time sequence coupling of network cells specifically comprises:
4.1 inputting network initial information and new user request flow, wherein the network initial information comprises network topology information and service information, the network topology information comprises parameters including the number of stations of a city subway line graph and the connection relation of the stations, the service information comprises the initial time of each subway line, the average driving time of adjacent stations of the subway line, the driving interval time and the service capacity, and the newly increased user request flow X is the user request flow which is newly increased in all nodes of the network cell service flow and needs to obey [0, X ] to be uniformly distributed;
4.2 establishing a programmed network cell model according to the formula (2) in the step two; each subway service is a programmed network cell, the subway service can be divided into 12 services according to a metro line and a running direction, the 12 services are modeled into 12 programmed network cells, the service line corresponding to each cell is the subway line, the service direction is the subway running direction, the cell structure of a cell i is the service line, the running interval time of the network cells is the average running time of adjacent stations, the network cell capacity is the maximum passenger capacity of the subway in the service capacity, and a coupling node q is connected with the network cellmA transfer station is embodied as a transfer station where a service line of a cell h and a service line of a cell i intersect, a coupling link line _ k is embodied as a service line of the cell h identical to the cell i, and the starting time of network cell functions is 1;
4.3, initializing the network, specifically including initializing:
(1) initializing a programmed network cell model according to network initial information;
(2) initializing network cell functional sites and real-time loads of the functional sites; the initial value of the network cell functional site is Null, and the real-time load of the functional site is initialized to 0;
(3) initializing the flow V transmitted by other network cells to the ith network celli＝0。
(4) Initializing cumulative user request traffic U on each network celliInitializing real-time load L of network cellsi＝0。
(5) Determining the total network cell number M, simulating the total Time Time by the network, and making the network system running Time t equal to 0 and the total network cell number M equal to 12; time 600;
4.4 according to the input new user request flow X value, randomly generating XijAccordingly, the newly increased user request flow of each network cell is obtained, and the current accumulated user request flow U of the network cell is updatedi；
4.5 according to the network cell function starting time t0 being 0, the network cell operation interval time b, and the current time t, sequentially judging whether each network cell needs to add a new functional site at the time step, if so, adding the new functional site, and making the real-time load of the functional site be 0;
4.6 for each network cell, removing a certain proportion of alpha network flow from the real-time load of the functional site, removing a certain proportion of beta network flow from the real-time load of the functional site, and evenly transmitting the beta network flow to the neighbor network cells connected with the network cells, wherein both alpha and beta are subjected to uniform distribution of [0,1 ];
4.7 for each network cell, on the premise of meeting the maximum service capacity, receiving the accumulated user request flow on the functional site and other cell forwarding flows transmitted to the network cell by other cells; corresponding user real-time load flow L for each network celliAccumulating user request flow UiAnd the flow rate V to which other cells are deliverediUpdating is carried out;
4.8 judging the functional sites on each network cell in turn, if the functional sites are at the target nodes of the business process in the network cells, removing the functional sites; otherwise, the functional site moves to the next node of the service flow according to the service flow;
4.9t ═ t +1, If t > Time, go to step 4.10; otherwise, go to step 4.4;
4.10 end.
Compared with the prior art, the invention has the following beneficial effects:
(1) the method and the device can support description and modeling of complex multi-service with time sequence coupling characteristics, and are favorable for researching the influence of the multi-service time sequence coupling characteristics on network congestion.
(2) The invention can support the time sequence relation of guiding adjustment and planning the multi-service operation of the network so as to avoid the network congestion.
Drawings
FIG. 1 is a diagram of a two-tier network formed by topology and services of the method of the present invention;
FIG. 2 is a flow chart of a network cell based traffic timing coupled congestion analysis method;
fig. 3(a) is a diagram of congestion change process during operation of a subway in rush hour;
FIG. 3(b) is a sequence parameter variation process diagram during operation of a subway at a peak time;
fig. 4(a) is a congestion variation process diagram during the operation of the subway in the peak leveling period;
FIG. 4(b) is a sequence parameter variation process diagram during the operation of subway at the peak level;
fig. 5(a) is a diagram of congestion change process when the subway runs at a low peak;
fig. 5(b) is a sequence parameter change process diagram during the operation of the subway in the low peak period.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is made with reference to the accompanying drawings and examples. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The invention provides a service time sequence coupling congestion analysis method based on network cells, which can be used for modeling and evaluating congestion under multiple services with time sequence coupling characteristics, provides theoretical support for network optimization design with service reliability as a target, and specifically comprises the following steps as shown in figure 2:
the method comprises the following steps: acquiring network initial information of a network to be analyzed, wherein the network initial information comprises network topology information and service information;
and determining network initial information of the network to be analyzed according to the engineering application and the user requirements. The network is a two-layer network composed of a network topology and services, as shown in fig. 1, wherein an upper layer is a service layer and is composed of Services (APPs), and each service is regarded as a network cell in the network and is called a network cell APP; the lower layer is a network topology layer and is represented as G ═ V, E, where G is a network topology structure diagram, V is a set of vertices, and E is a set of edges. The network initiation information thus includes network topology information and traffic information. The network topology information includes network topology and network resources, such as the number of nodes and the number of links; the service information includes a service flow, a service start time, a service interval time, and a service capacity.
In the embodiment, the network topology information is a Chengdu subway topology map, nodes of the network topology map represent subway stations, and edges of the network topology map represent that a subway runs between the two stations; the service information includes a service flow of the subway service, a service start time, a service interval time, a service capacity, and the like.
(1) Business process
The Chengdu subway has 6 lines, which are described in a table form, wherein the tables 1-6 are No. 1,2,3,4,7 and 10 lines of the Chengdu subway respectively, the numbers in the tables are station numbers, the characters are station names, all the stations are uniformly numbered, and the station numbers have uniqueness. For each subway line expressed by the table, a business process is formed in each table according to the forward sequence of the stations (from left to right to the last station). And taking the corresponding table as another business process according to the reverse order of the sites (from the last site to the first site in sequence from left). The service flow is composed of a service line and a service direction, in this embodiment, the service line is a metro line, and the service direction is a driving direction of a metro.
TABLE 1 Chengdu subway line No. 1
TABLE 2 Chengdu subway No. 2 line
TABLE 3 Chengdu subway No. 3 line
TABLE 4 Chengdu subway line No. 4
TABLE 5 Chengdu subway line No. 7
TABLE 6 Chengdu subway line No. 10
Each subway line can be regarded as an independent service process, 6 subway lines are formed in total, namely 6 service lines are formed, and 12 service processes are formed by combining the service direction, namely, the round-trip distinguishing treatment. Firstly, 6 forward service lines are defined as service processes 1-6, and the corresponding reverse service lines are defined as service processes 7-12, so that the service processes 1 and 7 are different directions of the subway No. 1 line, the service processes 2 and 8 are different directions of the subway No. 2 line, and the analogy is that the service processes 6 and 12 are different directions of the subway No. 10 line.
The start time of each subway line is further given, as well as the average travel time of adjacent stations of the subway line, as shown in table 7.
TABLE 7 Chengdu subway station average time
Meanwhile, the inter-driving time of the Chengdu subway is obtained through investigation and is shown in a table 8.
TABLE 8 Chengdu subway train operation interval time
(2) Traffic capacity
Maximum passenger capacity: seats were full, 9 people per square meter of station, reaching train design limits with an average degree of congestion (train load) of around 1.45 (up to 1.5, overcrowding is generally not possible). The maximum passenger capacity of current capital subway designs is as follows.
Chengdu subway No. 1 wire car type: 6B maximum speed per hour: 80km/h maximum passenger capacity: 1880 human.
Chengdu subway No. 2 wire car type: 6B maximum speed per hour: 80km/h maximum passenger capacity: 1880 human.
Chengdu subway No. 3 wire car type: 6B maximum speed per hour: 80km/h maximum passenger capacity: 1880 human.
Chengdu subway No. 4 wire car type: 6B maximum speed per hour: 80km/h maximum passenger capacity: 1880 human.
Chengdu subway No. 7 wire car type: 6A maximum speed per hour: 80km/h maximum passenger capacity: 2564 people.
Chengdu subway No. 10 line vehicle type: 6A maximum speed per hour: 100km/h maximum passenger capacity: 2488 and is suitable for human body.
Step two: establishing a model of network cells in a network;
the network cell is a basic functional unit of the network, each service is regarded as one network cell in the network, and the network function is realized by time sequence coupling of a plurality of services, so that the network function can be described as a network function organization formed by connecting a plurality of network cells.
The general model of network cell APP is:
APP＝{Process；D；Loadt；T0；TH} (1)
among them, Process, D, Loadt,T0,THThe cell business process, the cell maximum processing capacity, the real-time cell load, the cell start time, and the cell function implementation interval are described separately.
According to the general model of the network cell APP, a concrete model of a programmed network cell i (hereinafter referred to as cell i) can be obtained, wherein programming refers to a service type that a path from a source node to a destination node is fixed and does not change at will.
Wherein for Cellr(i)Denotes cell i, cell structure S of cell ir(i)Cell function Fr(i)And cell attachment Cr(i)The three parts are formed, i is a natural number; sr(i)Represents the cellular structure of cell i, line _ q represents link q; fr(i)And the cell function of the cell i is represented, and the cell function comprises the starting time of the network cell function, the running interval time of the network cell, the capacity of the network cell and the current real-time load of the network cell which are x, v, b and n in sequence. Cr(i)Cell junction representing Cell i, including other cells in coupled relation with Cell i, and coupling nodes or coupling links of Cell i with other cells, Cellr(h)Represents cells h, qmRepresents a coupling node between the cell i and the cell h, and q is a coupling node between the cell i and the cell hmIs a collection of coupled nodes, Cellr(j)Represents cell j and line _ k represents the coupling link k of cell i and cell j, where i, h and j are not equal.
Programmed network cells incorporating the inventionAnd the model is used for carrying out abstract modeling on each subway service into a programmed network cell according to the model (2) based on the simplified subway service information collected by the invention. In a specific simulation process, each subway service is a programmed network cell, and the subway service can be divided into 12 services according to the formation subway line and the running direction, so that the 12 services are modeled into 12 programmed network cells, and the service line and the service direction corresponding to each cell are shown in tables 1-6. The cell structure of the cell i is a service line, the running interval time of the network cell is the average running time of adjacent stations, and the capacity of the network cell is the maximum passenger capacity of the subway in the service capacity. Coupling node qmIn the transfer station where the service line of the cell h intersects with the service line of the cell i, the coupling link line _ k is embodied as the same service line of the cell h as the cell i, and it is obvious that in this embodiment, if the service lines of two different cells are the same, the two cells must correspond to the same service line, but the service directions are different. Meanwhile, although the start time of the subways of different lines is different, the time difference is smaller, and the condition of adjusting departure time exists in the running process of the subways, so that the influence of the running start time of the subways on the congestion of the subways is not considered temporarily in the process of analyzing the congestion phase change point of the subways in a simulation mode, and the running start time of the subways is made to be the first time step, namely the starting time of the network cell function is 1. In combination with the above assumptions:
TABLE 9
The specific expression of cell 1 and cell 2 is given below as an example:
cell 1:
Cellr(1)＝{Sr(1)；Fr(1)；Cr(1)}
Sr(1)＝P1＝{line_1}
Fr(1)＝{1,2,1880,0}
Cr(1)＝{(Cellr(2),7)；(Cellr(8),7)；(Cellr(7),line_1)}
cell 2:
Cellr(2)＝{Sr(2)；Fr(2)；Cr(2)}
Sr(2)＝P2＝{line_2}
Fr(2)＝{1,2,1880,0}
Cr(2)＝{(Cellr(1),7)；(Cellr(8),7)；(Cellr(3),39)；(Cellr(9),39)；(Cellr(4),34)；(Cellr(10),34)(Cellr(8),line_2)}
specifically, for the cell 1, the cell structure of the cell 1 corresponds to the service Line _1, the specific network cell function start time in the cell function, the cell operation interval time, the cell capacity, and the current real-time load of the cell are sequentially initialized to 1,2,1880, and 0, wherein the network cell function start time starts from the first time step, and thus the network cell function start time is set to 1, the network cell operation interval time is obtained from table 7, the network cell capacity is obtained according to the maximum passenger capacity of the subway No. 1 Line, and the current real-time load is generally initially 0. For network cell connections, it can be seen from the different service lines corresponding to tables 1 to 6 that network cell 1 and network cell 2, network cell 8 and network cell 7
The network cell 1 and the network cell 2 are coupled at a node 7, the network cell 8 is the same as the network cell 2, and therefore the network cell 1 and the network cell 8 are also coupled at the node 7, and the network cell 1 and the network cell 7 have different directions of the same service line, and therefore the entire network cell structure is coupled, further giving a network cell structure C of the network cell 1r(1)＝{(Cellr(2)，7)；(Cellr(8)，7)；(Cellr(7)Line _ 1). The concrete expression of the cell model of the rest network cells is completed by combining the processes.
Step three: determining a cell operation rule;
this step defines the rules of cell operation in the network cell model. Therefore, the transfer process of the traffic among different business processes can be described.
In the implementation process of the network function, the network cell interaction of the whole network is realized based on certain operation rules, including the internal operation mechanism of the network cell, the interaction rules between the network cell and the external environment, and between the network cell and the network cell. Considering the network cell operation rules that may be involved in the three aspects of the network cell operation process, the operation rules are described as the following 3 basic rules:
the method comprises the following steps of network cell function self-updating:
1) the functional site of the network cell is transmitted to the next node in the service flow in turn from the source node O of the service flow at each time step according to the network cell service flow until the destination node D in the service flow disappears. In this embodiment, the source node O and the destination node D are an originating station and a destination station of each subway line. The functional site refers to a node causing flow change and interaction, and in the embodiment, the functional site is a station where a subway train is currently parked, and the flow change and interaction are caused by the boarding and disembarking and transfer operations of passengers at the parked station.
2) Each interval TiAnd at each time step, adding a new functional site to the network cell, wherein the initial functional site is a source node O of the network cell business process. In this embodiment, the step size of each time step is set to 2min according to the average travel time of adjacent stations, and the newly added functional sites are obtained according to the travel time interval of the metro in table 8, for example, for a programmed network cellr(2)Programming the network cell during rush hour according to table 8r(2)Adding a new functional site every 4 time steps, programming network cell every 5 time steps in the peak-flattening period or the peak-lowering periodr(2)A new functional site is added.
The method comprises the following steps of carrying out a reaction in the network cells, and removing a certain proportion of alpha network flow in the load of the network cells at the functional sites of the network cells. Without loss of generality, at a certain moment, all the flow in the network cells is removed, so that the value range of alpha is more than or equal to 0 and less than or equal to 1. In this example, α follows a uniform distribution of [0,1 ].
The interaction among network cells:
1) at the network cell functional site, the network cell i sequentially receives the network flow V transmitted to the network cell by the neighbor network celliAnd accumulating the user request traffic UiInducing real-time loading of network cells LiWhen the real-time load L is increasedi＝DiWhen it is time to stop loading, DiRepresents the maximum processing capacity of the cell i, i.e. the network cell capacity of the programmed network cell i. The embodiment is embodied as follows: when i is 1,2,3,4,7,8,9,10, Di1880, when i is 5,11, Di2564, when i is 6,12, Di2488. In the simulation, the newly increased user request flow of the jth node of the ith network cell service process is xij，xijIs a random number, but in nodes of the overall network cell traffic flow xijObey [0, X]The uniform distribution refers to X as new user request traffic (note here that X is not actually new user request traffic, and all network cells actually new user request traffic approaches X/2).
2) At the network cell functional site, the network cell transmits a certain proportion of beta network flow on the network cell to the neighbor network cell, wherein beta is more than or equal to 0 and less than or equal to 1, and beta + alpha is less than or equal to 1. Assuming that m neighbor cells are coupled and connected through the node where the network cell functional site is located, the beta network traffic is evenly distributed to the m neighbor cells. In this example, β follows a uniform distribution of [0,1 ].
Step four: the method for deducing service congestion evolution based on time sequence coupling of network cells specifically comprises the following steps:
and 4.1, inputting network initial information and newly-added user request flow, wherein the network initial information comprises network topology information and service information.
And parameterizing and inputting network initial information into the network initial information, wherein the network topology information comprises parameters of the number of stations of the metro line map and the connection relation of the stations, and the service information comprises the starting time of each metro line, the average driving time of adjacent stations of the metro line, the driving interval time, the service capacity and the like.
Inputting a newly-added user request flow X set by a user, and indicating that the newly-added user request flow in all nodes of the network cell service flow in each circulation of the simulation process needs to obey [0, X ] uniform distribution.
4.2 establishing a programmed network cell model according to the formula (2) in the step two;
4.3, initializing the network, specifically including initializing:
(1) initializing a programmed network cell model according to network initial information;
(2) initializing network cell functional sites and real-time loads of the functional sites; the initial value of the network cell functional site is Null, and the real-time load of the functional site is initialized to 0;
(3) initializing the flow V transmitted by other network cells to the ith network celli＝0。
(4) Initializing cumulative user request traffic U on each network celliInitializing real-time load L of network cellsi＝0。
(5) Determining the total network cell number M, simulating the total Time by the network, and making the running Time t of the network system equal to 0, wherein in the embodiment, the total network cell number M is equal to 12; time 600;
4.4 according to the input new user request flow X value, randomly generating XijAccordingly, the newly increased user request flow of each network cell is obtained, and the current accumulated user request flow U of the network cell is updatedi；
4.5 according to the network cell function starting time t0 being 0, the network cell operation interval time b, and the current time t, sequentially judging whether each network cell needs to add a new functional site at the time step, if so, adding the new functional site, and making the real-time load of the functional site be 0.
4.6 for each network cell, remove a proportion of the network flow from the real-time load of its functional site, remove a proportion of the beta network traffic from the real-time load of its functional site and evenly pass to the neighbor network cells to which its network cells are connected. In this embodiment, α and β are uniformly distributed by [0,1 ].
4.7 for each network cell, on the premise of meeting the maximum service capacity, receiving the user request flow on the functional site and other cell forwarding flows transmitted to the network cell by other cells; corresponding user real-time load flow L for each network celliAccumulating user request flow UiAnd the flow rate V to which other cells are deliverediAnd (6) updating.
4.8 judging the functional site on each network cell in turn, and if the functional site is at the destination node of the business process in the network cell, removing the functional site. Otherwise, the functional site moves to the next node of the business process according to the business process.
4.9t ═ t +1, If t > Time, go to step 4.10; otherwise, go to step 4.4.
4.10 end.
Step five: using the system sequence parameters based on the network cells as statistical characteristic parameters for describing the network congestion state;
the system sequence parameter describes the statistical characteristic of the dynamic change of the network state along with the time from the perspective of the change of the data packetiThe flow rate forwarded by the neighbor network cell of the network cell i is ViThe real-time load flow of the network cell i is Li. At each moment, the average value of the newly generated request flow of each network cell is Z. The sequence parameter of the whole network based on the network cell congestion evolution can be expressed as:
wherein (U)i+Vi+Li) Representing network traffic associated with network cell i, accumulating user request traffic UiThe flow V of other cells forwarded to the ith network celliReal-time load flow L of ith network celli。Δ(Ui+Vi+Li) Is the change in network traffic associated with cell i over the Δ t time.
Is the sum of the relevant network flow changes of all cells in the whole network in the delta t time. MZ is the sum of the user request traffic newly generated by all cells on the network at each moment. Further, when etacell→ 0, the network is in a 'free flow' state, and the traffic on the network can be transmitted in time; when etacellWhen the traffic is greater than 0, the network is in a "congested" state, and the newly generated traffic in the network is greater than the transmitted traffic. Different values of eta can describe different degrees of network congestion, when etacellOn → 1, the full network congestion collapses.
Step six: inputting different newly-added user request flow X under the condition that other parameters are the same in the step four, and obtaining the sequence parameter eta according to the step fiveceilObtaining the variation process diagram of the sequence parameters of different new user request flows by using a point tracing method, and obtaining the phase change condition X causing the network congestion to formc。
Fig. 3(a) shows the congestion change process when a metro at peak hours is operating. The simulation is performed by using different user request flows according to a network formed by junior subway lines, fig. 3(a) depicts the change process of w (t) in the network along with the time when the newly added user request flows are respectively X-400,500,580,620,660, and W (t) represents the accumulated user request flow U in the network at the time tiThe sum of (a) and (b), the network of this embodiment is a metro, and therefore w (t) is actually the number of users accumulated in the metro network, and it can be seen that, when X is 400,500,580, the number of users accumulated in the network is a smooth process, which indicates that the number of users served in the network and the number of users requested in the network can reach a balanced state under the user request traffic, and the network does not form congestion. When X is 620,660, the cumulative number of users used by the network is a continuously increasing process, illustratingAs time goes by, the number of users accumulated in the network increases, and the congestion of the network becomes more serious. According to the simulation of the network formed by the Chengdu subway line, the parameter value in the formula (3) can be obtained, the sequence parameter η ceil when different user request flows are obtained according to the formula (3), and the change process of the sequence parameter under the different user request flows is obtained according to a point tracing method, as shown in fig. 3(b), it can be seen that obvious jump occurs to the sequence parameter when X580, which indicates that the phase change condition formed by network congestion when the network operates in a peak period is Xc＝580。
Fig. 4(a) shows the congestion change process when the junior subway runs in the peak balance period. Wherein fig. 4(a) describes the variation process of the number of users accumulated in the network when the user request traffic X is 400,440,460,560,600, it can be seen that when X is 400,440,460, the number of users accumulated in the network is a smooth process, which indicates that the number of users served in the network and the number of users requested in the network can reach a balanced state under the user request traffic, and the network does not form congestion. When X is 560,600, the number of users accumulated by the network is a process that continuously increases, which shows that the number of users accumulated in the network is more and the congestion of the network is more and more serious with the increase of time. Further, given the change process of the network sequence parameter under different user requests, as shown in fig. 4(b), it can be seen that when X is 460, the sequence parameter has an obvious jump, which indicates that the phase change condition formed by the network congestion during the peak period is X460.
Fig. 5(a) shows a congestion change process when a metro is operating in a low peak period. Wherein fig. 5(a) describes the process of changing the number of users accumulated in the network when the user requests X is 320,360,380,460,500, it can be seen that when X is 320,360,380, the number of users accumulated in the network is a smooth process, which indicates that the number of users served in the network and the number of users requested in the network can reach a balanced state under the user request, and the network will not form congestion. When X is 460,500, the network is a process that continuously increases with the number of accumulated users, meaning that the number of accumulated users in the network increases with timeThe more, the more the congestion of the network gets worse and worse. Further, given the change process of the network sequence parameter under different user requests, as shown in fig. 5(b), it can be seen that when X is 380, the sequence parameter undergoes an obvious jump, which indicates that the phase change condition formed by the network congestion during the peak period is Xc＝380。
In combination with the above analysis, as cells are newly added at each cycle, the user requests xijObey [0, X]When the phase change points are uniformly distributed, the phase change points formed by the congestion of the subway system analyzed by the model of the invention are sequentially reduced under different operation intervals of a peak period, a peak-flattening period and a peak-lowering period, wherein the operation intervals are respectively Xc＝580，Xc＝460，X c380, which conforms to the characteristics of the actual operation of the subway. From the specific modeling and analyzing process, the model effectively describes the important attributes of subway operation based on the cell model, and simultaneously describes the interaction process between the subway and the user through the cell rule, and the related modeling and analyzing process has good guiding function on the modeling and analyzing of the congestion phase change point under the subsequent similar service. It should be noted that, the congestion phase change point currently analyzed by the present invention is assumed that the request of the user is uniformly distributed, and the maximum capacity of the subway is used as a precondition for analyzing the congestion phase change of the subway, and the evaluation criteria of passengers and subway operators on congestion may be different in the actual operation of the subway, for example, the assumption that 80% of the maximum capacity is used as the congestion phase change point is analyzed, and the analysis of these specific scenes needs to be further expanded in combination with specific actual data, and the modeling and analyzing process of case application can be used as an important reference for the subsequent modeling analysis.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (2)
1. A service time sequence coupling congestion analysis method based on network cells is characterized in that: which comprises the following steps:
the method comprises the following steps: acquiring network initial information of a network to be analyzed, wherein the network initial information comprises network topology information and service information;
determining network initial information of a network to be analyzed, wherein the network is a double-layer network consisting of network topology and services, and the upper layer is a service layer consisting of services; the lower layer is a network topology layer and is represented as G ═ V, E, wherein G is a network topology structure chart, V is a set of vertexes, and E is a set of edges; the network initial information comprises network topology information and service information, wherein the network topology information comprises network topology and network resources; the service information comprises a service flow, service starting time, service interval time and service capacity;
step two: establishing a model of network cells in a network;
programming a specific model of network cell i:
wherein for Cellr(i)Denotes cell i, cell structure S of cell ir(i)Cell function Fr(i)And cell attachment Cr(i)The three parts are formed, i is a natural number; sr(i)Represents the cellular structure of cell i, line _ q represents link q; fr(i)The method comprises the steps of representing the cell function of a cell i, wherein the cell function comprises the network cell function starting time, the network cell operation interval time, the network cell capacity and the current real-time load of the network cell which are x, v, b and n in sequence; cr(i)Cell junction representing Cell i, including other cells in coupled relation with Cell i, and coupling nodes or coupling links of Cell i with other cells, Cellr(h)Represents cells h, qmRepresents a coupling node between the cell i and the cell h, and q is a coupling node between the cell i and the cell hmIs a collection of coupled nodes, Cellr(j)Represents a cell j, and line _ k represents a coupling link k of a cell i and a cell j, wherein i,h and j are not equal;
step three: determining a cell operation rule;
defining cell operation rules in the network cell model, including an internal operation mechanism of the network cell, interaction rules between the network cell and an external environment and between the network cell and the network cell, so as to describe the transfer process of the flow among different service flows;
the cell operation rule in the network cell model is specifically as follows:
the method comprises the following steps of network cell function self-updating:
1) the functional site of the network cell is transmitted to the next node in the service flow in turn from the source node O of the service flow at each time step according to the service flow of the network cell until the destination node D in the service flow disappears;
2) each interval TiIn each time step, a new functional site is added to the network cell, and the initial functional site is a source node O of the network cell business process;
secondly, the network cell reaction is carried out, namely, network flow with a certain proportion of alpha in the network cell load is removed at the functional site of the network cell; at a certain moment, the flow in the network cells is completely removed, so that the value range of alpha is more than or equal to 0 and less than or equal to 1;
the interaction among network cells:
1) at the network cell functional site, the network cell i sequentially receives the network flow V transmitted to the network cell by the neighbor network celliAnd accumulating the user request traffic UiInducing real-time loading of network cells LiWhen the real-time load L is increasedi＝DiWhen it is time to stop loading, DiThe maximum processing capacity of the cell i is shown, and the newly increased user request flow of the jth node of the ith network cell service process is xij，xijIs a random number, but in nodes of the overall network cell traffic flow xijObey [0, X]Uniformly distributing, wherein X is called the request flow of the newly added user;
2) at the network cell functional site, the network cell transmits a certain proportion of beta network flow on the network cell to a neighbor network cell, wherein beta is more than or equal to 0 and less than or equal to 1, and beta + alpha is less than or equal to 1;
step four: the method for deducing service congestion evolution based on time sequence coupling of network cells specifically comprises the following steps:
4.1, inputting network initial information and new user request flow, wherein the network initial information comprises network topology information and service information;
4.2 establishing a programmed network cell model according to the second step;
4.3 initializing the network;
4.4, according to the input new user request flow, obtaining the new user request flow added by each network cell, and loading the new user request flow to the current existing accumulated user request flow of the network cell;
4.5 sequentially judging whether each network cell needs to add a new functional site at the time step according to the network cell function starting time t0, the network cell operation interval time v and the current time t, if so, adding the new functional site and enabling the real-time load of the functional site to be 0;
4.6 for each network cell, removing a certain proportion of alpha network flow from the real-time load of the functional site, removing a certain proportion of beta network flow from the real-time load of the functional site, and averagely transmitting the beta network flow to the neighbor network cells connected with the network cells;
4.7 for each network cell, on the premise of meeting the maximum service capacity, receiving the user request flow on the functional site and other cell forwarding flows transmitted to the network cell by other cells; correspondingly updating the user real-time load flow of each network cell, the accumulated user request flow and the flow transmitted to the network cell by other cells;
4.8 judging the functional sites on each network cell in turn, if the functional sites are at the target nodes of the business process in the network cells, removing the functional sites; otherwise, the functional site moves to the next node of the service flow according to the service flow;
4.9t ═ t +1, If t > Time, go to step 4.10; otherwise, go to step 4.4; wherein the Time is the total network simulation duration;
4.10, finishing;
step five: using the system sequence parameters based on the network cells as statistical characteristic parameters for describing the network congestion state;
the sequence parameter of the whole network in the network cell congestion evolution is expressed as:
wherein, the number of the network cells is M, and the accumulated user request flow on the network cell i is UiThe flow rate forwarded by the neighbor network cell of the network cell i is ViThe real-time load flow of the network cell i is LiAt each moment, the average value of newly generated request flow of each network cell is Z, (U)i+Vi+Li) Represents the network traffic, Δ (U), associated with the network cell ii+Vi+Li) To account for the change in cell i-related network traffic over the at time,
the sum of the related network flow changes of all cells in the whole network in delta t time, MZ is the sum of the user request flow newly generated by all cells on the network at each moment, and further when etacell→ 0, the network is in a free flow state, and the traffic on the network can be transmitted in time; when etacellWhen the traffic is more than 0, the network is in a congestion state, and the newly generated traffic in the network is more than the transmitted traffic; etacellThe different values of (a) describe the different degrees of network congestion, when etacell→ 1, full network congestion collapses;
step six: inputting different newly-added user request flow X under the condition that other parameters are the same in the step four, and obtaining the sequence parameter eta according to the step fivecellObtaining the variation process diagram of the sequence parameters of different newly-added user request flows by using a point tracing method, and obtaining the phase change condition X causing the network congestion to formc。
2. The method according to claim 1, wherein the method comprises: the fourth step is that: each step in the derivation of service congestion evolution based on time sequence coupling of network cells specifically comprises:
4.1 inputting network initial information and new user request flow, wherein the network initial information comprises network topology information and service information, the network topology information comprises parameters including the number of stations of a city subway line graph and the connection relation of the stations, the service information comprises the initial time of each subway line, the average driving time of adjacent stations of the subway line, the driving interval time and the service capacity, and the newly increased user request flow X is the user request flow which is newly increased in all nodes of the network cell service flow and needs to obey [0, X ] to be uniformly distributed;
4.2 establishing a programmed network cell model according to the formula (2) in the step two; each subway service is a programmed network cell, the subway service can be divided into 12 services according to a metro line and a running direction, the 12 services are modeled into 12 programmed network cells, the service line corresponding to each cell is the subway line, the service direction is the subway running direction, the cell structure of a cell i is the service line, the running interval time of the network cells is the average running time of adjacent stations, the network cell capacity is the maximum passenger capacity of the subway in the service capacity, and a coupling node q is connected with the network cellmA transfer station is embodied as a transfer station where a service line of a cell h and a service line of a cell i intersect, a coupling link line _ k is embodied as a service line of the cell h identical to the cell i, and the starting time of network cell functions is 1;
4.3, initializing the network, specifically including initializing:
(1) initializing a programmed network cell model according to network initial information;
(2) initializing network cell functional sites and real-time loads of the functional sites; the initial value of the network cell functional site is Null, and the real-time load of the functional site is initialized to 0;
(3) initiating other network cell deliveryFlow V on ith network celli＝0；
(4) Initializing cumulative user request traffic U on each network celliInitializing real-time load L of network cellsi＝0；
(5) Determining the total network cell number M, simulating the total Time Time by the network, and making the network system running Time t equal to 0 and the total network cell number M equal to 12; time 600;
4.4 according to the input new user request flow X value, randomly generating XijAccordingly, the newly increased user request flow of each network cell is obtained, and the current accumulated user request flow U of the network cell is updatedi；
4.5 according to the network cell function starting time t0 being 0, the network cell operation interval time v, and the current time t, sequentially judging whether each network cell needs to add a new functional site at the time step, if so, adding the new functional site, and making the real-time load of the functional site be 0;
4.6 for each network cell, removing a certain proportion of alpha network flow from the real-time load of the functional site, removing a certain proportion of beta network flow from the real-time load of the functional site, and evenly transmitting the beta network flow to the neighbor network cells connected with the network cells, wherein both alpha and beta are subjected to uniform distribution of [0,1 ];
4.7 for each network cell, on the premise of meeting the maximum service capacity, receiving the accumulated user request flow on the functional site and other cell forwarding flows transmitted to the network cell by other cells; corresponding user real-time load flow L for each network celliAccumulating user request flow UiAnd the flow rate V to which other cells are deliverediUpdating is carried out;
4.8 judging the functional sites on each network cell in turn, if the functional sites are at the target nodes of the business process in the network cells, removing the functional sites; otherwise, the functional site moves to the next node of the service flow according to the service flow;
4.9t ═ t +1, If t > Time, go to step 4.10; otherwise, go to step 4.4;
4.10 end.
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