CN111818168A - Self-adaptive joint calculation unloading and resource allocation method in Internet of vehicles - Google Patents

Self-adaptive joint calculation unloading and resource allocation method in Internet of vehicles Download PDF

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CN111818168A
CN111818168A CN202010670745.4A CN202010670745A CN111818168A CN 111818168 A CN111818168 A CN 111818168A CN 202010670745 A CN202010670745 A CN 202010670745A CN 111818168 A CN111818168 A CN 111818168A
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vehicle
unloading
mec
matrix
vehicles
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CN111818168B (en
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林峰
罗铖文
丁鹏举
王鹏
蒋建春
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Chongqing Mouyi Technology Co ltd
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]

Abstract

The invention relates to a self-adaptive joint calculation unloading and resource allocation method in a vehicle networking, and belongs to the field of vehicle networking edge calculation. The method comprises the step of considering the condition of concurrent unloading of a plurality of vehicles under the combination of a local server, an MEC server, a remote cloud server and an idle vehicle multi-computing platform. Comprehensively considering the size of the vehicle task, the maximum tolerance time delay, the computing resource under RSU and the network bandwidth factor, and unloading the computing task of the vehicle to a plurality of computing platforms; automatically adjusting the unloading platform and the unloading proportion of the vehicle calculation task according to the number of tasks under the RSU, and distributing the calculation resources of the MEC when the vehicle obtains the optimal unloading proportion; modeling a calculation unloading decision and resource allocation as a multi-constraint optimization problem; and optimizing unloading decision and resource allocation by using a compressed particle swarm optimization algorithm and providing a particle matrix coding mode. Experimental results show that the method can meet the maximum tolerant time delay and minimize the total cost of the system.

Description

Self-adaptive joint calculation unloading and resource allocation method in Internet of vehicles
Technical Field
The invention belongs to the field of vehicle networking computing, and relates to a self-adaptive joint computing unloading and resource allocation method in the vehicle networking.
Background
As the number of autonomous vehicles in C-V2X increases, various computationally intensive and delay sensitive applications, such as image-assisted navigation and augmented reality driving, are emerging that require significant computing resources for real-time processing and analysis of large amounts of sensory data, which presents significant challenges to vehicles with limited computing resources.
By offloading the computing tasks to other computing nodes, the problem of limited vehicle computing resources can be effectively solved. The remote cloud server has abundant computing resources, but is far away from the vehicle, which can generate huge transmission delay and energy consumption. The MEC server sinks the calculation to the roadside equipment unit, and is closer to the vehicle, so that the time delay and the energy consumption are lower, however, the computing resource of the MEC server is limited, and if the vehicle is too many in the current RSU, the time delay is increased.
In the existing research, the task unloading of a single vehicle is mostly considered, and the simultaneous unloading of the calculation tasks of a plurality of vehicles is rarely researched; most of them only consider delay and not energy consumption. Offloading decisions also basically only considers offloading to one platform rather than multiple computing platforms, and does not allocate resources while offloading decisions. In fact, most vehicles are unloaded concurrently, it is difficult for the MEC server with limited computing resources to meet the maximum tolerance delay, so that the unloading of multiple computing platforms and the reasonable allocation of resources need to be considered, and it is necessary for the green internet of vehicles to consider energy consumption.
Disclosure of Invention
In view of the above, the present invention provides a method for adaptive joint computation offloading and resource allocation in an internet of vehicles.
In order to achieve the purpose, the invention provides the following technical scheme:
under the scene of concurrent unloading of a plurality of vehicles, according to the bandwidth, the size of tasks, the maximum tolerance time delay and the vehicle transmitting power of the vehicles, constructing a network model, a task model and a calculation model of the vehicles comprises the following steps:
(1) vehicle network model: the channel of the vehicle upload link is a Rayleigh channel model, vehicle viThe data rate of upload/download with the BS is
Figure BDA0002582183530000011
Vehicle viThe data rate of uploading/downloading between the idle vehicle and the MEC is
Figure BDA0002582183530000012
Figure BDA0002582183530000013
Figure BDA0002582183530000014
Wherein h is1，h2，B1，B2，
Figure BDA0002582183530000015
Respectively representing the channel gain, channel bandwidth and transmitting power between the vehicle and the BS and between the vehicle and the MEC/idle vehicle; alpha is alpha2Representing the noise power, λ, ω ∈ (0,1) are bandwidth allocation factors。
(2) And (3) task model: vehicle viComputing task S requiring offloadingiIs divisible and defined
Figure BDA0002582183530000021
The calculation tasks of each vehicle are different, so the parameters are not completely the same. Herein IiDenotes SiThe workload size of (2); g represents the number of CPU cycles required for calculating 1bit data, and unit cycles/bit; f. ofiRepresents the computing power of the vehicle, in cycles/s;
Figure BDA0002582183530000022
denotes SiMaximum tolerated delay of; the computing resource of the MEC is fmecIs assigned to the vehicle viIs a computing resource of
Figure BDA0002582183530000023
The computing resource provided by the remote cloud server to the vehicle is fcloudWith free vehicles providing only certain computational resources fidle. The offload decision matrix is: d ═ D1,d2,…,dn]Wherein
Figure BDA0002582183530000024
Respectively represent SiAnd unloading to the local, MEC server, remote cloud server, and idle vehicle.
Figure BDA0002582183530000025
(3) Local vehicle calculation model: siThe amount of computational tasks performed at the local vehicle is
Figure BDA0002582183530000026
Defining local vehicle execution time delay as
Figure BDA0002582183530000027
The energy consumption is
Figure BDA0002582183530000028
Since the vehicle calculates only the calculation delay locally, there is no communication delay.
Figure BDA0002582183530000029
Figure BDA00025821835300000210
Wherein, PiIndicating a vehicle viThe device power of (1).
(4) MEC calculation model: siThe amount of computational tasks performed at the MEC is
Figure BDA00025821835300000211
Defining the execution delay at MEC as
Figure BDA00025821835300000212
Figure BDA00025821835300000213
A transmission delay of
Figure BDA00025821835300000214
The return delay is
Figure BDA00025821835300000215
From vehicle viTotal time delay to offload to MEC is
Figure BDA00025821835300000216
Total energy consumption of
Figure BDA00025821835300000217
Figure BDA00025821835300000218
Figure BDA00025821835300000219
Figure BDA00025821835300000220
Figure BDA00025821835300000221
Figure BDA00025821835300000222
Figure BDA00025821835300000223
Wherein, PmecIs the device power of the MEC server,
Figure BDA00025821835300000224
is a vehicle viThe upload power of (1) is an output data amount coefficient, which represents a relationship between an output data amount and an input data amount.
(5) A remote cloud server computing model: offloading to the remote cloud server requires offloading to the BS and then to the remote cloud server by the fiber. Defining an execution latency at a remote cloud server as
Figure BDA00025821835300000225
A transmission delay of
Figure BDA00025821835300000226
The calculated result returns to the transmission delay of
Figure BDA00025821835300000227
Calculating task average transmission waiting time delay t on optical fiber linecloudFrom vehicle viTotal time delay for offloading to the remote cloud server is
Figure BDA0002582183530000031
Total energy consumption of
Figure BDA0002582183530000032
Figure BDA0002582183530000033
Figure BDA0002582183530000034
Figure BDA0002582183530000035
Figure BDA0002582183530000036
Figure BDA0002582183530000037
Wherein, PcloudDevice Power, P, representing remote cloud ServerBSRepresenting the transmit power of the base station.
(6) Idle vehicle calculation model: the time delay of the unloading to the idle vehicle is
Figure BDA0002582183530000038
A transmission delay of
Figure BDA0002582183530000039
The return time of the calculation result is
Figure BDA00025821835300000310
Average relay time delay between vehicles is twFrom vehicle viTotal time delay for unloading to an idle vehicle is
Figure BDA00025821835300000311
Total energy consumption of
Figure BDA00025821835300000312
Figure BDA00025821835300000313
Figure BDA00025821835300000314
Figure BDA00025821835300000315
Figure BDA00025821835300000316
Figure BDA00025821835300000317
Wherein, PidleIndicating the device power of the idle vehicle.
Further, the time delay and the energy consumption of all vehicles are weighted to obtain the total system cost, and a constraint optimization problem and a resource allocation model which meet the maximum time delay tolerance and minimize the total system cost are established:
the total time delay T and the total energy consumption E of the combined unloading of the vehicles define the cost of the combined unloading system as H.
Figure BDA00025821835300000318
Figure BDA00025821835300000319
H＝γ·T+(1-γ)·E
Wherein, gamma is a time delay weight coefficient, and 1-gamma is an energy consumption weight coefficient.
In meeting task SiMaximum tolerated delay
Figure BDA0002582183530000041
And under the resource limit, minimizing the total cost of the combined unloading system, and modeling the task unloading and resource allocation of the system as follows:
Figure BDA0002582183530000042
Figure BDA0002582183530000043
Figure BDA0002582183530000044
Figure BDA0002582183530000045
Figure BDA0002582183530000046
Figure BDA0002582183530000047
wherein D is an unloading decision matrix, F is a calculation resource allocation vector of the MEC server and is expressed as
Figure BDA0002582183530000048
In order to perform resource allocation while making an unloading decision, obtain a task proportion of each vehicle unloaded to each computing platform, and obtain computing resources allocated to the vehicle by the MEC, the particle matrix coding method is provided and comprises the following steps:
the optimized parameters of each vehicle are 5, respectively
Figure BDA0002582183530000049
And
Figure BDA00025821835300000410
if there are n vehicles under the RSU that need to be unloaded, then the encoding matrix M of a particle is an n × 5 matrix. The first 4 columns of the matrix are the offload decision matrix D for the vehicle and the 5 th column of the matrix is the computational resource allocation vector F for the MEC server. The whole particle group is stored by a matrix A, each particle encoding matrix M is firstly converted into a rowAnd stored in the matrix a. The matrix a is a matrix of N rows (N × 5) columns, where N is the particle group size.
In order to solve the integer constraint of the above model, the proposed particle correction algorithm comprises:
taking out each row of the matrix A, reducing the matrix into a particle coding matrix M, taking out each row of the particle coding matrix, and correcting the matrix so that the task of each vehicle meets the requirement
Figure BDA00025821835300000411
In order to solve the equality and inequality constraints in the task unloading and resource allocation model of the system, the constraint processing method by utilizing the compression particle swarm optimization and combining the penalty function method comprises the following steps:
the penalty function is:
P(q)＝θ(q)·qγ(q)
Figure BDA00025821835300000412
where q is a relative constraint penalty function, θ (q) is a piecewise assignment function, and γ (q) is a penalty index. The fitness function is the target function plus a penalty function:
Figure BDA0002582183530000051
the invention has the beneficial effects that: the invention comprehensively considers the size of each vehicle task, the maximum tolerant time delay, the computing resource under RSU and the network bandwidth. And the unloading platform and the optimal unloading proportion can be automatically adjusted according to the task number of the current RSU, and the computing resources of the MEC are distributed while the unloading proportion is obtained. Through experimental simulation, the invention is verified to be capable of effectively reducing the total cost of the system.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a system model of the present invention;
FIG. 2 is a flow chart of the solution of the present invention;
FIG. 3 is a schematic diagram of a particle encoding matrix according to the present invention;
FIG. 4 is a flow chart of the particle swarm algorithm improved by the present invention;
FIG. 5 is a graph of total system cost versus calculated task load per vehicle for different algorithms;
FIG. 6 is a graph of total system cost versus number of vehicles under different algorithms;
FIG. 7 is a graph of the total system cost versus the output data volume factor for different algorithms.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
The invention provides a self-adaptive joint calculation unloading and resource allocation method in an internet of vehicles, which comprises the following steps:
step 1: as shown in FIG. 1, the calculation task of the unloading vehicle can be calculated locally, and can be unloaded to the idle vehicle V under the current RSUidleThe computing can be unloaded to an RSU equipped with an MEC server, and can also be unloaded to a remote cloud server through a cellular network. FIG. 2 is a flow chart of the solution of the present invention; thus, a network model of the vehicle is constructed as follows:
the method comprises the steps that a plurality of vehicles unload tasks simultaneously, and a set of vehicles which have calculation tasks and need to unload under the coverage of a current RSU is defined as V ═ V1,v2,…,vnEach vehicle has a calculation task to be unloaded, and the corresponding task set is S ═ S1,S2,…,SnThe set of free vehicles is C ═ C1,c2,…,ck}. The channel of the vehicle upload link is a Rayleigh channel model, vehicle viThe data rate of upload/download with the BS is
Figure BDA0002582183530000061
Vehicle viThe data rate of uploading/downloading between the idle vehicle and the MEC is
Figure BDA0002582183530000062
Figure BDA0002582183530000063
Figure BDA0002582183530000064
Wherein h is1，h2，B1，B2，
Figure BDA0002582183530000065
Respectively representing the channel gain, channel bandwidth and transmitting power between the vehicle and the BS and between the vehicle and the MEC/idle vehicle; alpha is alpha2Representing the noise power, λ, ω ∈ (0,1) are bandwidth allocation factors.
Step 2: constructing a task model of the vehicle as follows:
vehicle viComputing task S requiring offloadingiIs divisible and defined
Figure BDA0002582183530000066
The calculation tasks of all vehicles are different, and the parameters are not completely the same. Herein IiDenotes SiThe workload size of (2); g represents the number of CPU cycles required for calculating 1bit data, and unit cycles/bit; f. ofiRepresents the computing power of the vehicle, in cycles/s;
Figure BDA0002582183530000067
denotes SiMaximum tolerated delay of; the computing resource of the MEC is fmecIs assigned to the vehicle viIs a computing resource of
Figure BDA0002582183530000068
The computing resource provided by the remote cloud server to the vehicle is fcloudWith free vehicles providing only certain computational resources fidle。
Since tasks can be computed locally, at the MEC server, at the remote cloud server, at the idle vehicle, the tasks need to be divided, andit is determined which platforms to offload to, and how much to offload. Defining an offload decision matrix as: d ═ D1,d2,…,dn]Wherein
Figure BDA0002582183530000071
Respectively represent SiAnd unloading to the local, MEC server, remote cloud server, and idle vehicle.
Figure BDA0002582183530000072
And step 3: according to the network model and the task model, the calculation models of the four calculation platforms are established as follows:
(1) local vehicle calculation model: siThe amount of computational tasks performed at the local vehicle is
Figure BDA0002582183530000073
Defining local vehicle execution time delay as
Figure BDA0002582183530000074
The energy consumption is
Figure BDA0002582183530000075
Since the vehicle calculates only the calculation delay locally, there is no communication delay.
Figure BDA0002582183530000076
Figure BDA0002582183530000077
Wherein, PiIndicating a vehicle viThe device power of (1).
(2) MEC calculation model: siThe amount of computational tasks performed at the MEC is
Figure BDA0002582183530000078
Defining the execution delay at MEC as
Figure BDA0002582183530000079
Figure BDA00025821835300000710
A transmission delay of
Figure BDA00025821835300000711
The return delay is
Figure BDA00025821835300000712
From vehicle viTotal time delay to offload to MEC is
Figure BDA00025821835300000713
The total energy consumption is e.
Figure BDA00025821835300000714
Figure BDA00025821835300000715
Figure BDA00025821835300000716
Figure BDA00025821835300000717
Figure BDA00025821835300000718
Wherein, PmecIs the device power of the MEC server,
Figure BDA00025821835300000719
is a vehicle viThe upload power of (1) is an output data amount coefficient, which represents a relationship between an output data amount and an input data amount.
(3) A remote cloud server computing model: offloading to the remote cloud server requires offloading to the BS and then to the remote cloud server by the fiber. Is defined at the far endExecution time delay of cloud server is
Figure BDA00025821835300000720
A transmission delay of
Figure BDA00025821835300000721
The calculated result returns to the transmission delay of
Figure BDA00025821835300000722
Calculating task average transmission waiting time delay t on optical fiber linecloudFrom vehicle viTotal time delay for offloading to the remote cloud server is
Figure BDA00025821835300000723
Total energy consumption of
Figure BDA00025821835300000724
Figure BDA0002582183530000081
Figure BDA0002582183530000082
Figure BDA0002582183530000083
Figure BDA0002582183530000084
Figure BDA0002582183530000085
Wherein, PcloudDevice Power, P, representing remote cloud ServerBSRepresenting the transmit power of the base station.
(4) Idle vehicle calculation model: the time delay of the unloading to the idle vehicle is
Figure BDA0002582183530000086
A transmission delay of
Figure BDA0002582183530000087
The return time of the calculation result is
Figure BDA0002582183530000088
Average relay time delay between vehicles is twFrom vehicle viTotal time delay for unloading to an idle vehicle is
Figure BDA0002582183530000089
Total energy consumption of
Figure BDA00025821835300000810
Figure BDA00025821835300000811
Figure BDA00025821835300000812
Figure BDA00025821835300000813
Figure BDA00025821835300000814
Figure BDA00025821835300000815
Wherein, PidleIndicating the device power of the idle vehicle.
And 4, step 4: through the steps, the time delay and the energy consumption of all vehicles are further weighted to obtain the total system cost, and a constraint optimization problem and a resource allocation model which meet the maximum time delay tolerance and minimize the total system cost are established:
the total time delay T and the total energy consumption E of the combined unloading of the vehicles define the cost of the combined unloading system as H.
Figure BDA00025821835300000816
Figure BDA00025821835300000817
H＝γ·T+(1-γ)·E
Wherein, gamma is a time delay weight coefficient, and 1-gamma is an energy consumption weight coefficient.
In meeting task SiMaximum tolerated delay
Figure BDA00025821835300000818
And under the resource limit, minimizing the total cost of the combined unloading system, and modeling the task unloading and resource allocation of the system as follows:
Figure BDA0002582183530000091
Figure BDA0002582183530000092
Figure BDA0002582183530000093
Figure BDA0002582183530000094
Figure BDA0002582183530000095
Figure BDA0002582183530000096
wherein D is an unloading decision matrix, F is a calculation resource allocation vector of the MEC server and is expressed as
Figure BDA0002582183530000097
Figure BDA0002582183530000098
C1 denotes a vehicle viDetermining task SiAdding the task proportions of the idle vehicles into the whole task by the MEC server, the remote cloud server and the local vehicle; c2 indicates that the time to complete each vehicle's mission should not exceed the maximum tolerated delay; c3 represents the maximum number of vehicles that can be unloaded to spare; c4 indicates that the computational resources allocated for each vehicle cannot exceed the total resources of the MEC server; c5 indicates that the total sum of computing resources allocated for vehicle tasks cannot exceed the total resources of the MEC server.
And 5: in order to perform resource allocation while making an unloading decision, obtain a task proportion of each vehicle unloaded to each computing platform, and obtain computing resources allocated to the vehicle by the MEC, a particle matrix coding mode is proposed as follows:
the optimized parameters of each vehicle are 5, respectively
Figure BDA0002582183530000099
And
Figure BDA00025821835300000910
assuming that there are n vehicles under one RSU that need to be task unloaded, the encoding matrix M for one particle is an n × 5 matrix. As shown in fig. 3, the first 4 columns of the matrix are the vehicle offload decision matrix D, and the 5 th column of the matrix is the computing resource allocation vector F of the MEC server. The whole particle swarm is stored by using a matrix A, and each particle encoding matrix M is converted into a row and stored in the matrix A. The matrix a is a matrix of N rows (N × 5) columns, where N is the particle group size.
Step 6: in order to solve the integer constraint in the task unloading and resource allocation model of the system established in step 4, the proposed particle correction algorithm is as follows:
(1) and taking out each row of the matrix A and reducing the matrix A into a particle coding matrix M.
(2) From M momentTake out each row one by one in the array if
Figure BDA00025821835300000911
Adjusting the row
Figure BDA00025821835300000912
(3) The idealcar largest numbers are selected from the fourth column of M, and the rest are assigned values of 0.
(4) The 5 parameters of each vehicle are taken out from the row taken out in turn
Figure BDA00025821835300000913
And
Figure BDA00025821835300000914
if it is not
Figure BDA00025821835300000915
(5)
Figure BDA00025821835300000916
Then the
Figure BDA00025821835300000917
(6) The 2 nd column of the matrix M is added to obtain all the task volumes sum offloaded to the MEC server.
(7) Computing resources per vehicle obtained from MEC server
Figure BDA0002582183530000101
(8) The matrix M is then converted into 1 row and placed in the corresponding row in the matrix a.
Among them, the number of idle vehicles in the ideal.
And 7: in order to solve the equality and inequality constraints in the task unloading and resource allocation model of the system, the constraint is processed by utilizing a compression particle swarm algorithm and combining a penalty function method as follows:
the penalty function is:
P(q)＝θ(q)·qγ(q)
Figure BDA0002582183530000102
where q is a relative constraint penalty function, θ (q) is a piecewise assignment function, and γ (q) is a penalty index. The fitness function is the target function plus a penalty function:
Figure BDA0002582183530000103
and finally, obtaining an improved particle swarm algorithm flow chart as shown in the figure 4.
As can be seen from fig. 5, as the amount of computing tasks increases, the total system cost of the five algorithms also increases. However, the magnitude of the increase of the algorithm proposed by the present invention is minimal, and the total cost of the system is significantly lower than the other four algorithms. Approximately 22.09% of the total local offload algorithm, 38.66% of the total MEC offload algorithm, 27.8% of the random offload algorithm, and 68.80% of the conventional joint offload algorithm.
As can be seen from fig. 6, the total system cost of the five algorithms all show an upward trend as the number of vehicles increases. Compared with other algorithms, the algorithm provided by the invention has the minimum total system cost. Where all MEC offload algorithms are spiked at a vehicle count of 20, this is higher than the total system cost for local computation, since when the vehicle count exceeds a certain value, the resulting computing resources allocated by the MEC to each vehicle are not yet locally high.
As can be seen from fig. 7, as the amount of output data increases, the total system cost increases for all algorithms except for all local offload algorithms. Since the local computation has no return time delay of the computation result, the total system cost of all local unloading algorithms remains unchanged. And the influence of other algorithms is small because the result return time delay is only realized when the result is unloaded to the MEC, the remote cloud server and the idle vehicle, and the return quantity of the calculation result is relatively small, so that the influence on the system cost is small.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (6)

1. A self-adaptive joint calculation unloading and resource allocation method in the Internet of vehicles is characterized in that: the method comprises the following steps:
s1: under the scene of concurrent unloading of a plurality of vehicles, a vehicle network model, a task model and a calculation model are constructed according to the bandwidth, the size of a task, the maximum tolerance time delay and the vehicle transmitting power of the vehicles;
s2: weighting the time delay and the energy consumption of all vehicles to obtain the total system cost, and establishing a constraint optimization problem and a resource allocation model which meet the maximum time delay tolerance and minimize the total system cost;
s3: providing a particle matrix coding mode on the basis of a compressed particle swarm algorithm serving as a basic algorithm, performing resource allocation while making an unloading decision, obtaining a task proportion of each vehicle unloaded to each computing platform, and obtaining computing resources allocated to the vehicles by an MEC;
s4: the proposed particle correction algorithm utilizes a compressed particle swarm algorithm in combination with a penalty function method to solve the problem of constraint optimization.
2. The method for adaptive joint computation offload and resource allocation in the internet of vehicles according to claim 1, wherein: the method for constructing the network model, the task model and the calculation model of the vehicle comprises the following steps:
s21: vehicle network model: the channel of the vehicle upload link is a Rayleigh channel model, vehicle viThe data rate of uploading/downloading with BS is Ri 1V. vehicleiThe data rate of uploading/downloading between the idle vehicle and the MEC is Ri 2；
Figure FDA0002582183520000011
Figure FDA0002582183520000012
Wherein h is1，h2，B1，B2，
Figure FDA0002582183520000018
Respectively representing the channel gain, channel bandwidth and transmitting power between the vehicle and the BS and between the vehicle and the MEC/idle vehicle; alpha is alpha2Representing the noise power, and lambda, omega epsilon (0,1) is a bandwidth allocation factor;
s22: and (3) task model: vehicle viComputing task S requiring offloadingiIs divisible and defined
Figure FDA0002582183520000013
Wherein the calculation tasks of all vehicles are different, and the parameters are not completely the same; i isiDenotes SiThe workload size of (2); g represents the number of CPU cycles required for calculating 1bit data, and unit cycles/bit; f. ofiRepresents the computing power of the vehicle, in cycles/s;
Figure FDA0002582183520000014
denotes SiMaximum tolerated delay of; the computing resource of the MEC is fmecIs assigned to the vehicle viIs a computing resource of
Figure FDA0002582183520000015
The computing resource provided by the remote cloud server to the vehicle is fcloudWith free vehicles providing only certain computational resources fidle(ii) a The offload decision matrix is: d ═ D1,d2,…,dn]Wherein d isi＝[ai 1,ai 2,ai 3,ai 4]，ai 1、ai 2、ai 3、ai 4Respectively represent SiThe proportion of the vehicle load to local, MEC server, remote cloud server and idle vehicle; a isi 1,ai 2,ai 3,ai 4∈[0,1]，ai 1+ai 2+ai 3+ai 4＝1；
S23: local vehicle calculation model: siThe amount of calculation task performed at the local vehicle is ai 1×IiDefining local vehicle execution time delay as
Figure FDA0002582183520000016
The energy consumption is
Figure FDA0002582183520000017
Because the vehicle only has calculation time delay in local calculation and no communication time delay;
Figure FDA0002582183520000021
Figure FDA0002582183520000022
wherein, PiIndicating a vehicle viThe device power of (1);
s24: MEC calculation model: siThe amount of computational tasks performed at the MEC is ai 2×IiDefining the execution delay at MEC as
Figure FDA00025821835200000226
A transmission delay of
Figure FDA0002582183520000023
The return delay is
Figure FDA0002582183520000024
From vehicle viTotal time delay to offload to MEC is
Figure FDA0002582183520000025
Total energy consumption of
Figure FDA0002582183520000026
Figure FDA0002582183520000027
Figure FDA0002582183520000028
Figure FDA0002582183520000029
Figure FDA00025821835200000210
Figure FDA00025821835200000211
Figure FDA00025821835200000212
Wherein, PmecIs the device power of the MEC server,
Figure FDA00025821835200000213
is a vehicle viThe upload power of (1) is an output data volume coefficient representing a relationship between an output data volume and an input data volume;
s25: a remote cloud server computing model: the unloading to the remote cloud server needs to be carried out to the BS firstly and then to the remote cloud server by the optical fiber; defining an execution latency at a remote cloud server as
Figure FDA00025821835200000214
A transmission delay of
Figure FDA00025821835200000215
The calculated result returns to the transmission delay of
Figure FDA00025821835200000216
Calculating task average transmission waiting time delay t on optical fiber linecloudFrom vehicle viTotal time delay for offloading to the remote cloud server is
Figure FDA00025821835200000217
Total energy consumption of
Figure FDA00025821835200000218
Figure FDA00025821835200000219
Figure FDA00025821835200000220
Figure FDA00025821835200000221
Figure FDA00025821835200000222
Figure FDA00025821835200000223
Wherein, PcloudDevice Power, P, representing remote cloud ServerBSRepresents the transmit power of the base station;
s26: idle vehicle calculation model: the time delay of the unloading to the idle vehicle is
Figure FDA00025821835200000224
A transmission delay of
Figure FDA00025821835200000225
The return time of the calculation result is
Figure FDA0002582183520000031
Average relay time delay between vehicles is twFrom vehicle viThe total time delay for unloading to an idle vehicle is t and the total energy consumption is
Figure FDA0002582183520000032
Figure FDA0002582183520000033
Figure FDA0002582183520000034
Figure FDA0002582183520000035
Figure FDA0002582183520000036
Figure FDA0002582183520000037
Wherein, PidleIndicating the device power of the idle vehicle.
3. The method of claim 1, wherein the establishing a constrained optimization problem and resource allocation model that minimizes the total system cost and satisfies the maximum delay tolerance comprises:
the total time delay T and the total energy consumption E of the combined unloading of the vehicles define the cost of the combined unloading system as H;
Figure FDA0002582183520000038
Figure FDA0002582183520000039
H＝γ·T+(1-γ)·E
wherein gamma is a time delay weight coefficient, and (1-gamma) is an energy consumption weight coefficient;
in meeting task SiMaximum tolerated delay
Figure FDA00025821835200000310
And under the resource limit, minimizing the total cost of the combined unloading system, and modeling the task unloading and resource allocation of the system as follows:
Figure FDA00025821835200000311
s.t.C1:
Figure FDA00025821835200000312
C2:
Figure FDA00025821835200000313
C3:
Figure FDA00025821835200000314
C4:
Figure FDA00025821835200000316
C5:
Figure FDA00025821835200000315
wherein D is an unloading decision matrix, F is a calculation resource allocation vector of the MEC server and is expressed as
Figure FDA0002582183520000041
4. The method for adaptive joint computation offload and resource allocation in the internet of vehicles according to claim 1, wherein: the particle matrix encoding method comprises the following steps:
the optimized parameters of each vehicle are 5, and are respectively ai 1,ai 2,ai 3,ai 4And
Figure FDA0002582183520000042
if n vehicles need to unload tasks under the RSU, the encoding matrix M of one particle is an n multiplied by 5 matrix; the first 4 columns of the matrix are unloading decision matrixes D of the vehicles, and the 5 th column of the matrix is a calculation resource allocation vector F of the MEC server; the whole particle swarm is stored by a matrix A, each particle coding matrix M is firstly converted into a row and stored in the matrix A; the matrix a is a matrix of N rows (N × 5) columns, where N is the particle group size.
5. The method for adaptive joint computation offload and resource allocation in the internet of vehicles according to claim 1, wherein: the proposed particle correction algorithm comprises:
taking out each row of the matrix A, reducing the matrix into a particle coding matrix M, taking out each row of the particle coding matrix, and correcting to enable the task of each vehicle to meet ai 1,ai 2,ai 3,ai 4∈[0,1]，ai 1+ai 2+ai 3+ai 4＝1。
6. The method for adaptive joint computation offload and resource allocation in the internet of vehicles according to claim 1, wherein: the method for solving the constraint optimization problem by using the compression particle swarm algorithm and combining the penalty function method specifically comprises the following steps:
the penalty function is:
P(q)＝θ(q)·qγ(q)
Figure FDA0002582183520000043
wherein q is a relative constraint penalty function, θ (q) is a piecewise assignment function, and γ (q) is a penalty index; the fitness function is the target function plus a penalty function:
Figure FDA0002582183520000044
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Cited By (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112367640A (en) * 2020-11-09 2021-02-12 中科怡海高新技术发展江苏股份公司 V2V mode multitask unloading method and system based on mobile edge calculation
CN112492626A (en) * 2020-12-07 2021-03-12 南京邮电大学 Method for unloading computing task of mobile user
CN112638005A (en) * 2020-12-29 2021-04-09 张晓益 Distributed street lamp control device and system based on Internet of vehicles
CN112685163A (en) * 2021-01-06 2021-04-20 北京信息科技大学 Computing unloading method based on mobile edge computing and mobile edge computing server
CN112714178A (en) * 2020-12-25 2021-04-27 北京信息科技大学 Task unloading method and device based on vehicle-mounted edge calculation
CN112737842A (en) * 2020-12-29 2021-04-30 西北工业大学深圳研究院 Task safety unloading method based on minimized time delay in air-ground integrated Internet of vehicles
CN112860350A (en) * 2021-03-15 2021-05-28 广西师范大学 Task cache-based computation unloading method in edge computation
CN112888021A (en) * 2021-01-29 2021-06-01 重庆邮电大学 Task unloading method for avoiding interruption in Internet of vehicles
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CN112969163A (en) * 2021-02-22 2021-06-15 安徽大学 Cellular network computing resource allocation method based on self-adaptive task unloading
CN112995289A (en) * 2021-02-04 2021-06-18 天津理工大学 Internet of vehicles multi-target computing task unloading scheduling method based on non-dominated sorting genetic strategy
CN113051003A (en) * 2021-03-22 2021-06-29 中国科学院上海微系统与信息技术研究所 Method, system and storage medium for processing calculation task unloading of satellite communication network
CN113179296A (en) * 2021-04-08 2021-07-27 中国科学院计算技术研究所 Task unloading method for vehicle-mounted edge computing system
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CN113535397A (en) * 2021-07-14 2021-10-22 南通大学 Resource allocation method for intelligent vehicle edge computing network
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CN114189521A (en) * 2021-12-15 2022-03-15 福州大学 Method for cooperative computing offload in F-RAN architecture
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013123445A1 (en) * 2012-02-17 2013-08-22 Interdigital Patent Holdings, Inc. Smart internet of things services
WO2016057885A1 (en) * 2014-10-10 2016-04-14 DimensionalMechanics, Inc. System and methods for generating interactive virtual environments
CN109814951A (en) * 2019-01-22 2019-05-28 南京邮电大学 The combined optimization method of task unloading and resource allocation in mobile edge calculations network
CN110035410A (en) * 2019-03-07 2019-07-19 中南大学 Federated resource distribution and the method and system of unloading are calculated in a kind of vehicle-mounted edge network of software definition
CN110856259A (en) * 2019-11-12 2020-02-28 郑州轻工业学院 Resource allocation and offloading method for adaptive data block size in mobile edge computing environment
CN111124531A (en) * 2019-11-25 2020-05-08 哈尔滨工业大学 Dynamic unloading method for calculation tasks based on energy consumption and delay balance in vehicle fog calculation

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013123445A1 (en) * 2012-02-17 2013-08-22 Interdigital Patent Holdings, Inc. Smart internet of things services
WO2016057885A1 (en) * 2014-10-10 2016-04-14 DimensionalMechanics, Inc. System and methods for generating interactive virtual environments
CN109814951A (en) * 2019-01-22 2019-05-28 南京邮电大学 The combined optimization method of task unloading and resource allocation in mobile edge calculations network
CN110035410A (en) * 2019-03-07 2019-07-19 中南大学 Federated resource distribution and the method and system of unloading are calculated in a kind of vehicle-mounted edge network of software definition
CN110856259A (en) * 2019-11-12 2020-02-28 郑州轻工业学院 Resource allocation and offloading method for adaptive data block size in mobile edge computing environment
CN111124531A (en) * 2019-11-25 2020-05-08 哈尔滨工业大学 Dynamic unloading method for calculation tasks based on energy consumption and delay balance in vehicle fog calculation

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CHUNHUI LIU等: ""Adaptive Offloading for Time-Critical Tasks in Heterogeneous Internet of Vehicles"", 《IEEE INTERNET OF THINGS JOURNAL》 *
栾秋季: ""车联网系统中基于MEC的任务卸载优化研究"", 《中国优秀博硕士学位论文全文数据库(硕士)》 *
秦笙: ""面向车载自组织网络中紧急消息传输机制研究"", 《中国优秀博硕士学位论文全文数据库(硕士)》 *

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