CN114615264A - Urban traffic network data transmission and distribution method under edge cloud cooperative computing environment - Google Patents

Urban traffic network data transmission and distribution method under edge cloud cooperative computing environment Download PDF

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CN114615264A
CN114615264A CN202210161069.7A CN202210161069A CN114615264A CN 114615264 A CN114615264 A CN 114615264A CN 202210161069 A CN202210161069 A CN 202210161069A CN 114615264 A CN114615264 A CN 114615264A
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urban traffic
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马捷
王牵莲
陈景旭
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Southeast University
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Abstract

The invention establishes an urban traffic network data transmission and distribution method under a side cloud collaborative computing environment, which is used for realizing real-time data transmission and computing tasks of a large-scale urban traffic network. The method establishes an urban traffic network data transmission distribution model based on the task completion time minimization principle in cooperation with cloud storage and edge computing technologies. The invention provides a calculation method capable of solving the data transmission distribution model. By applying the model, a data packet distribution scheme based on real-time data transmission and calculation tasks of a large-scale urban traffic network can be optimized, the calculation resources of the user mobile terminal in the Internet of things are fully utilized, and the time for data transmission and processing is minimized, so that the cloud-and-side collaborative calculation scenes (such as large-scale urban traffic network distribution problems, super-large-scale public traffic network optimization problems and the like) of various traffic travel demands of the large-scale urban traffic networks are efficiently realized, and a powerful support is provided for the construction of an urban intelligent network system.

Description

Urban traffic network data transmission and distribution method in edge cloud collaborative computing environment
Technical Field
The invention relates to a method for transmitting and distributing urban traffic network data, in particular to a method for transmitting and distributing urban traffic network data in a side cloud collaborative computing environment,
background
The intelligent network connection is a characteristic intelligent scene realized by urban intelligent agents, is a necessary way for promoting the modernization of urban management systems and management capabilities, and is also important competitiveness of future urban development. Behind the construction of urban intelligent networking systems is the "blowout" growing volume of data, which has not been able to provide real-time responses well by means of Mobile Cloud Computing (MCC) alone. For this purpose, a Mobile Edge Computing (MEC) mode for processing analysis data by using a network Edge device is developed. By applying edge calculation, response delay can be effectively shortened, network load can be reduced, and storage cost can be reduced. The cloud storage and the edge computing are cooperated, so that the advantages of the cloud storage and the edge computing are complementary, the advantages of safety and reliability of the cloud storage, low price, high computing power, easiness in expansion and the like of the edge computing are exerted, and the development trend of urban intelligent network connection in the future is developed. However, the edge device is often light, and therefore, how to more reasonably and efficiently utilize the computing resources of the edge device is an important challenge.
A resource management system (application number 202111110654.6) applied to a cloud collaborative computing environment at the edge of an Internet of things establishes a resource management system comprising an edge computing module, a cloud computing module and a task allocation module, and maintains a high-efficiency and stable system operation state by intelligently controlling the allocation of computing tasks of terminals and a cloud. The utility model provides an edge computing node's distribution and exit method (application number 202110920295.4) based on branch neural network, through three steps of neural network model training, branch neural network model at the deployment of edge computing node and the selection of model exit point, effectively strengthened the security of neural network model under the edge computing, improved the computational efficiency of model. Although both the two prior inventions relate to edge computing, the former balances the task allocation of each edge terminal in the internet of things by evaluating the load of computing tasks, and the latter deploys a neural network model by selecting a plurality of nodes mainly from the safety of edge computing, and the full utilization of the surplus computing power in the network is not considered.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the urban traffic network data transmission and distribution method under the edge cloud collaborative computing environment is provided, a data transmission and distribution model based on the task completion time minimization principle is established in cooperation with cloud storage and edge computing technologies, and a computing method for solving the model is provided. Therefore, various urban traffic network real-time calculation scenes are realized more efficiently, and powerful support is provided for the construction of an urban intelligent network system.
The invention adopts the following technical scheme for solving the technical problems:
the urban traffic network data transmission and distribution method under the edge cloud collaborative computing environment comprises the following steps:
step1, under a side cloud collaborative computing environment, a user mobile terminal in an urban traffic network initiates a task request to a cloud end, the cloud end generates an original data packet according to the task request initiated by the user mobile terminal, and simultaneously requests to transmit the original data packet to the user mobile terminal with abundant computing power;
step2, when a certain calculation-power-surplus user mobile terminal receives a plurality of original data packets, calculating the time t used in the whole data transmission and processing processkThe following were used:
Figure BDA0003514714170000021
wherein the content of the first and second substances,
Figure BDA0003514714170000022
representing the delay of the user's mobile terminal k downloading the original data packet from the cloud,
Figure BDA0003514714170000023
representing the delay of uploading a result data packet to the cloud end by the user mobile terminal k;
Figure BDA0003514714170000024
and
Figure BDA0003514714170000025
respectively representing the speed of downloading an original data packet and the speed of uploading a result data packet by a user mobile terminal k; i and O respectively represent the sizes of an original data packet downloaded and an uploading result data packet of a user mobile terminal k; f. ofkThe number of original data packets for performing edge calculation by the user mobile terminal k is represented, namely the number of original data packets received by the user mobile terminal k;
Figure BDA0003514714170000026
representing the time required by the user mobile terminal k to complete the calculation task corresponding to the original data packet;
step3, based on the task completion time minimization principle, establishing an urban traffic network data transmission distribution model under the edge cloud collaborative computing environment, specifically as follows:
Figure BDA0003514714170000027
s.t.∑kfk＝M
Figure BDA0003514714170000028
wherein, omega is an integral variable, M is the number of user mobile terminals which send task requests to the cloud at the same time,
Figure BDA0003514714170000031
representing a set of natural numbers;
and 4, solving the data transmission distribution model established in the step3 to obtain the quantity of the original data packets distributed to each user mobile terminal, and distributing the original data packets to the corresponding user mobile terminals by the cloud according to the solving result.
As a preferred scheme of the present invention, in step1, M user mobile terminals in the urban transportation network all initiate task requests at the same time, where the task requests are path planning task requests, the network cloud packages travel time data required by each task request into original data packets, which form M original data packets, and each original data packet waits for transmission to a user mobile terminal with a certain computational power margin.
As a preferred embodiment of the present invention, in step2, when calculating the time used in the whole data transmission and processing process, it is set that the types of the task requests initiated by each user mobile terminal are the same, the sizes of the original data packets generated corresponding to each task request are the same, and the sizes of the result data packets corresponding to each original data packet are the same.
As a preferred embodiment of the present invention, the specific process of step4 is as follows:
step 1: setting x to 0, f to 1 and n to 1;
step 2: k is equal to {1,2, …, M }, and the corresponding k of each k is calculated
Figure BDA0003514714170000032
Find the shortest time from it
Figure BDA0003514714170000033
And replace it with
Figure BDA0003514714170000034
Namely, it is
Figure BDA0003514714170000035
Step 3: order to
Figure BDA0003514714170000036
fa：＝fa+1；n：＝n+1；
Step 4: if n < M, returning to Step 2; otherwise, finishing the calculation;
wherein the content of the first and second substances,
Figure BDA0003514714170000037
Figure BDA0003514714170000038
is a variable from 0 to 1, and is,
Figure BDA0003514714170000039
indicating that the nth original data packet is allocated to the user mobile terminal k for processing, otherwise
Figure BDA00035147141700000310
Figure BDA00035147141700000311
Figure BDA00035147141700000312
Figure BDA00035147141700000313
The shortest time required for each user mobile terminal to transmit and process the nth original data packet in the nth iteration is represented, a is the index of the user mobile terminal corresponding to the shortest time, and a belongs to {1,2, …, M };
Figure BDA00035147141700000314
indicating that the nth original data packet is allocated to the user mobile terminal a for processing, faThe number of original packets that the user mobile terminal a performs edge calculation is represented, that is, the number of original packets that the user mobile terminal a receives.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the invention establishes a data transmission distribution model based on the task completion time minimization principle in cooperation with cloud storage and edge computing technology, and provides a computing method for solving the model. The method can be applied to the side cloud collaborative computing scenes (such as urban traffic distribution, customized bus passenger matching and the like) of various urban traffic networks, helps to optimize the data packet distribution mode in the computing process, realizes the effective configuration of the computing resources of the user mobile terminal, and provides powerful support for the construction of an urban intelligent network interconnection system.
Drawings
Fig. 1 is a flowchart of an urban traffic network data transmission and distribution method in a cloud-and-edge collaborative computing environment according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The invention provides a method for transmitting and distributing urban traffic network data in a side cloud collaborative computing environment, which comprises the following processes as shown in figure 1:
1) a user mobile terminal in the urban traffic network initiates a task request to a cloud, and the cloud generates an original data packet according to the task request and transmits the request to the user mobile terminal.
Under the scenes of large data volume and high data safety, main data required by the edge cloud collaborative computing task are stored in the cloud. The cloud end of the urban traffic network edge cloud collaborative computing system stores travel schedules of the whole city. The travel schedule is composed of current travel time information among all nodes in a road network topological structure and is a core data basis of a plurality of large-scale urban traffic network real-time calculation tasks such as real-time traffic distribution, navigation path selection, co-riding travel matching and the like. Suppose that the topological structure of the urban road network has N nodes, and each node is respectively represented as N1,n2,…,nN. The specific form of the travel schedule is shown in table 1. Wherein, tijRepresenting a node niAnd njThe travel time in between, i, j ∈ {1,2, …, N } and i ≠ j.
TABLE 1 travel time table of urban traffic network
Figure BDA0003514714170000041
In order to fully utilize the computing power of the user mobile terminal, computing tasks and required data are distributed to the user mobile terminals in the network. When a certain user mobile terminal sends a request to the cloud terminal, the cloud terminal packs the travel time data required by the user mobile terminal to generate an original data packet, and the original data packet waits for being transmitted to a proper user mobile terminal at the edge of the network for calculation.
2) And calculating the data transmission and processing time of the user mobile terminal.
Assuming that M user mobile terminals in the network initiate requests at the same time, M original data packets waiting for transmission are formed in the edge cloud cooperative computing system. The request types initiated by each user mobile terminal are the same, the size of an original data packet required to be downloaded by each request is similar, and the size of an uploaded result data packet is also similar. The edge calculation process is mainly divided into three parts: firstly, transmitting an original data packet from a cloud to a mobile terminal; secondly, the user mobile terminal completes calculation; and thirdly, transmitting the result data packet from the mobile terminal to the cloud. For the kth user mobile terminal, the time t used in the whole data transmission and processing processkIs calculated as follows:
Figure BDA0003514714170000051
wherein the content of the first and second substances,
Figure BDA0003514714170000052
representing the delay of the user mobile terminal k downloading data from the cloud,
Figure BDA0003514714170000053
representing the delay of the data transmission from the user mobile terminal k to the cloud end;
Figure BDA0003514714170000054
and
Figure BDA0003514714170000055
representing the speed of downloading and uploading data packets by the user mobile terminal i; i and O respectively represent the sizes of data packets downloaded and uploaded by a user mobile terminal k; f. ofkRepresenting the number of original data packets for the edge calculation of the user mobile terminal k;
Figure BDA0003514714170000056
indicating that the user mobile terminal k completes the calculation corresponding to one data packetThe time required for the task, for example, to plan the fastest path between the origin and the destination.
3) And establishing an urban traffic network data transmission distribution model under the edge cloud collaborative computing environment based on the task completion time minimization principle.
Figure BDA0003514714170000057
s.t.∑kfk＝M
Figure BDA0003514714170000058
In the formula
Figure BDA0003514714170000059
Representing a natural number set, and omega is an integral variable. The purpose of this model is to minimize the actual time required for the edge cloud collaborative computing system to process M simultaneously issued user mobile terminal requests.
4) And solving the data transmission distribution model by using a calculation method.
The specific flow of the calculation method is as follows:
step 1: let x be 0 and f be 1.
Step 2: computing
Figure BDA00035147141700000510
Find the shortest time
Figure BDA00035147141700000511
Step 3：
Figure BDA0003514714170000061
fa：＝fa+1；n：＝n+1。
Step 4: if n < M, returning to Step 2; otherwise, the calculation is ended.
Wherein the content of the first and second substances,
Figure BDA0003514714170000062
Figure BDA0003514714170000063
is a variable from 0 to 1.
Figure BDA0003514714170000064
Indicating that the nth original data packet is distributed to the kth user mobile terminal for processing, otherwise
Figure BDA0003514714170000065
x is a group consisting of
Figure BDA0003514714170000066
And the formed vector represents the processing condition of each user mobile terminal in the urban traffic network edge cloud cooperative computing system for each data packet.
Figure BDA0003514714170000067
Indicating the number of data packets processed by each user's mobile terminal in the current computing system.
Figure BDA0003514714170000068
Indicating the minimum time required for each mobile terminal in the current computing system to transmit and process the nth original data packet, i.e., the minimum time required for each mobile terminal to transmit and process the nth original data packet
Figure BDA0003514714170000069
a is the index of the user mobile terminal corresponding to the shortest time, and a is ∈ {1,2, …, M }.
Figure BDA00035147141700000610
Means of
Figure BDA00035147141700000611
Same, faIs defined as meaning ofkThe same is true.
Examples
1) The cloud end of the target urban traffic network edge cloud collaborative computing system stores a travel time table of the whole city, and the travel time table is dynamically updated according to travel time information among all nodes in a road network topological structure. Currently in the morning rush hour commute hours of the city. At a certain moment, a total of 200 users initiate path planning requests through mobile terminals such as a smart phone or a vehicle navigation system, the network cloud respectively packages travel time data required by the requests to form 200 original data packets, and each original data packet waits for being transmitted to a user mobile terminal with surplus computing power.
2) Suppose that the kth user mobile terminal received fkThe time t used by the whole data transmission and processing process of each original data packetkComprises the following steps:
Figure BDA00035147141700000612
the size I of each original packet is 5 and the size O of the resulting packet is 0.005. It is assumed that the mobile terminals of the users all have equal time consumption for completing the fastest path planning task of one data packet, that is to say
Figure BDA00035147141700000613
The specific path Planning task can be completed by applying mainstream path Planning algorithms such as ch (connectionhierarchy), a-star, CRP (custom Route Planning), and the like.
Suppose that the speed at which the user mobile terminal k downloads and uploads the data packets is the same, i.e. the speed is the same
Figure BDA00035147141700000614
Is provided with
Figure BDA00035147141700000615
Representing the total delay of the user mobile terminal k in downloading and uploading the data packets. R of 200 user mobile terminalskAnd lkThe values of (A) are shown in Table 2:
table 2 partial example data
Figure BDA00035147141700000616
Figure BDA0003514714170000071
Figure BDA0003514714170000081
Figure BDA0003514714170000091
Figure BDA0003514714170000101
Figure BDA0003514714170000111
3) And establishing a data transmission distribution model of the real-time path planning scene based on a task completion time minimization principle.
4) According to the calculation method for solving the data transmission allocation model, a program is written by pycharm software and runs in the environment of conda 4.9.2, python 3.8.5. The results obtained were as follows:
value of objective function
Figure BDA0003514714170000112
0,2.0,2.0,2.0,2.0,1.0, 1.0,2.0,3.0,2.0,3.0,1.0,2.0,2.0,1.0,2.0,2.0,1.0,1.0,2.0,3.0,2.0, 3.0,3.0,3.0,1.0, 2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,3.0,3.0,2.0, 2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0, 1.0,1.0,2.0, 2.0,3.0,3.0,2.0, 0,1.0, 0,2.0,0, 1.0,2.0,3.0, 0,2.0,0, 1.0, 0,2.0,0, 3.0, 0,2.0,0, 3.0, 0,2.0,0, 2.0,0, 3.0,1.0, 0,2.0,0, 1.0,2.0, 0,3.0, 0,2.0,3.0, 0,2.0,0, 3.0, 0,2.0,2.0,2.0,3.0, 0,3.0, 0,2.0,2.0,3.0,2.0, 3.0,3.0, 0,2.0,2.0, 0,3.0,2.0,0, 2.0,0, 2.0,3.0, 0,2.0,0, 3.0, 0,3.0,3.0, 0,2.0,3.0, 0,2.0,2.0,3.0,3.0, 3.0,3.0, 0,2.0,2.0, 0,1.0, 0,3.0,3.0, 0,1.0, 0,3.0,3.0,3.0, 0,1.0, 0,2.0,3.0, 0,3.0,3.0, 0,3.,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]T。
the above embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical solution according to the technical idea of the present invention fall within the protective scope of the present invention.

Claims (4)

1. The urban traffic network data transmission and distribution method under the edge cloud collaborative computing environment is characterized by comprising the following steps:
step1, under a side cloud collaborative computing environment, a user mobile terminal in an urban traffic network initiates a task request to a cloud end, the cloud end generates an original data packet according to the task request initiated by the user mobile terminal, and simultaneously requests to transmit the original data packet to the user mobile terminal with abundant computing power;
step2, when a certain user mobile terminal with surplus calculation power receives a plurality of original data packets, calculating the time t used in the whole data transmission and processing processkThe following were used:
Figure FDA0003514714160000011
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003514714160000012
representing the delay of the user's mobile terminal k downloading the original data packet from the cloud,
Figure FDA0003514714160000013
representing the delay of uploading a result data packet to the cloud end by the user mobile terminal k;
Figure FDA0003514714160000014
and
Figure FDA0003514714160000015
respectively representing the speed of downloading an original data packet and the speed of uploading a result data packet by a user mobile terminal k; i and O respectively represent the sizes of an original data packet downloaded and an uploading result data packet of a user mobile terminal k; f. ofkThe number of original data packets for performing edge calculation by the user mobile terminal k is represented, namely the number of original data packets received by the user mobile terminal k;
Figure FDA0003514714160000016
representing the time required by the user mobile terminal k to complete the calculation task corresponding to the original data packet;
step3, based on the task completion time minimization principle, establishing an urban traffic network data transmission distribution model under the edge cloud collaborative computing environment, specifically as follows:
Figure FDA0003514714160000017
s.t.∑kfk＝M
Figure FDA0003514714160000018
wherein, omega is an integral variable, M is the number of user mobile terminals which send task requests to the cloud end at the same time,
Figure FDA0003514714160000019
representing a set of natural numbers;
and 4, solving the data transmission distribution model established in the step3 to obtain the quantity of the original data packets distributed to each user mobile terminal, and distributing the original data packets to the corresponding user mobile terminals by the cloud according to the solving result.
2. The method for transmitting and allocating the urban traffic network data in the edge cloud collaborative computing environment according to claim 1, wherein in step1, M user mobile terminals in the urban traffic network all initiate task requests at the same time, the task requests are path planning task requests, the network cloud packs travel time data required by each task request into original data packets, the original data packets form M original data packets, and each original data packet waits for being transmitted to a user mobile terminal with a certain computational power margin.
3. The method for transmitting and allocating the urban traffic network data under the edge cloud collaborative computing environment according to claim 1, wherein in the step2, when the time used in the whole data transmission and processing process is calculated, the task requests initiated by each user mobile terminal are set to be of the same type, the size of the original data packet generated corresponding to each task request is the same, and the size of the result data packet corresponding to each original data packet is the same.
4. The method for transmitting and distributing the urban traffic network data in the edge cloud collaborative computing environment according to claim 1, wherein the specific process of the step4 is as follows:
step 1: setting x to 0, f to 1 and n to 1;
step 2: k belongs to {1, 2.. eta., M }, and the corresponding k is calculated
Figure FDA0003514714160000021
Find the shortest time from it
Figure FDA0003514714160000022
And replace it with
Figure FDA0003514714160000023
Namely, it is
Figure FDA0003514714160000024
Step 3: order to
Figure FDA0003514714160000025
fa：＝fa+1；n：＝n+1；
Step 4: if n is less than M, returning to Step 2; otherwise, finishing the calculation;
wherein the content of the first and second substances,
Figure FDA0003514714160000026
Figure FDA0003514714160000027
is a variable from 0 to 1, and is,
Figure FDA0003514714160000028
indicating that the nth original data packet is allocated to the user mobile terminal k for processing, otherwise
Figure FDA0003514714160000029
Figure FDA00035147141600000210
Figure FDA00035147141600000211
Figure FDA00035147141600000212
Representing the shortest time required by each user mobile terminal for the nth iteration to transmit and process the nth original data packet, wherein a is the index of the user mobile terminal corresponding to the shortest time, and a belongs to {1, 2.., M };
Figure FDA00035147141600000213
indicating that the nth original data packet is allocated to the user mobile terminal a for processing, faThe number of original packets that the user mobile terminal a performs edge calculation is represented, that is, the number of original packets that the user mobile terminal a receives.
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