CN112867065A - Air-ground cooperative edge calculation method and system - Google Patents

Air-ground cooperative edge calculation method and system Download PDF

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CN112867065A
CN112867065A CN202110008402.6A CN202110008402A CN112867065A CN 112867065 A CN112867065 A CN 112867065A CN 202110008402 A CN202110008402 A CN 202110008402A CN 112867065 A CN112867065 A CN 112867065A
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calculation
allocation
computing
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aerial vehicle
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CN112867065B (en
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张天魁
徐瑜
肖霖
杨鼎成
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Beijing University of Posts and Telecommunications
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Beijing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • 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
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/38—TPC being performed in particular situations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power

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Abstract

The application discloses a method and a system for computing an air-ground cooperative edge, wherein the method for computing the air-ground cooperative edge specifically comprises the following steps: setting initialization information; responding to the completion of the initialization parameters, and distributing the calculation tasks; responsive to completing allocation of the computing task, performing allocation of the communication resources; responsive to completing the communication resource allocation, performing an allocation of the computing resource; after responding to the completion of the allocation of the computing resources, determining the trajectory of the unmanned aerial vehicle; determining a duration value in response to completing the trajectory determination of the unmanned aerial vehicle; updating the computational efficiency in response to the determination of the completion age value; judging whether the calculation efficiency meets the preset convergence precision or not; and if so, outputting the distribution result of the communication resources, the distribution result of the calculation resources, the unmanned aerial vehicle track and the time length value as the optimal result. The method and the device achieve the purposes of system resource allocation including communication resources and computing resources and design of flight path of the combined unmanned aerial vehicle.

Description

Air-ground cooperative edge calculation method and system
Technical Field
The application relates to the field of big data, in particular to a method and a system for computing an air-ground cooperative edge.
Background
With the rise of the internet of things, the number of different types of TDs (Terminal Devices) such as cloud sensors, smart phones, wearable Devices and the like is increasing rapidly; intelligent applications such as face recognition, interactive games, virtual reality, etc. are also emerging continuously. However, since these terminal devices have weak computing power and low battery capacity, it is a troublesome problem to efficiently calculate the large amount of computing data generated by these applications. In this context, MEC (Mobile Edge Computing) is a very promising technology to help TDs perform data processing and computation at the network Edge (such as access point and base station), so that the above problems can be solved.
In recent years, UVA (Unmanned Aerial Vehicle) is gradually appearing in the line of sight of people due to its high reliable line-of-sight communication capability. The unmanned aerial vehicle is used as an aerial mobile platform carrying the MEC server, task unloading access and calculation can be provided for ground users, and the subject is researched more and more widely in the academic field. Unlike the ground-based fixed MEC server arrangement, drone assisted MECs have many unique features. Firstly, the unmanned aerial vehicle has high flexibility and controllability, the position of the unmanned aerial vehicle can be adjusted according to a real-time unloading strategy of a user, and the flight track of the unmanned aerial vehicle can be planned and designed according to certain specific scenes and targets, such as specific planning and design according to different targets of energy consumption, safety or throughput and the like. In addition, due to the advantage of high altitude, the unmanned aerial vehicle is free from being influenced by special geographic terrain, and the communication coverage is enhanced and enlarged. Above-mentioned characteristics make unmanned aerial vehicle exert important effect in the MEC system gradually, have also compensatied the not enough of the fixed deployment of ground server.
On this basis, the prior art provides and solves the key problem of air-ground cooperation with optimal calculation efficiency in the unmanned aerial vehicle auxiliary MEC system, and the joint design is performed on the communication resources, the calculation resources and the unmanned aerial vehicle track of the system by mainly considering the calculation amount and the energy overhead of the system. However, although the MEC provides an effective solution for task computation of the terminal device, the flexibility and the computing power of the system can be further enhanced through the auxiliary design of the unmanned aerial vehicle. However, the air-ground cooperative MEC system still faces more complicated problems of equipment scheduling, resource allocation, energy overhead and the like. On one hand, it is desirable that the computing power of the system is strong enough to obtain as large a computing amount as possible and low time delay; on the other hand, it is desirable to minimize the system power consumption to extend the life cycle of the device.
Therefore, how to balance the maximum calculation amount and the minimum energy overhead of the system in the air-ground collaborative scenario and how to plan the flight trajectory of the unmanned aerial vehicle still remain key problems to be solved by those skilled in the art.
Disclosure of Invention
The invention provides an air-ground collaborative edge calculation method and a system thereof, which can balance the relationship between the calculation amount and the energy consumption overhead of the system and solve the problems of air-ground computing resource collaborative design, communication resource allocation and unmanned aerial vehicle track design which are not considered in the prior art.
In order to achieve the above object, the present application provides a method for computing an air-ground cooperative edge, which specifically includes the following steps: s110: setting initialization information; s120: responding to the completion of the initialization parameters, and distributing the calculation tasks; s130: responsive to completing allocation of the computing task, performing allocation of the communication resources; s140: responsive to completing the communication resource allocation, performing an allocation of the computing resource; s150: after responding to the completion of the allocation of the computing resources, determining the trajectory of the unmanned aerial vehicle; s160: determining a duration value in response to completing the trajectory determination of the unmanned aerial vehicle; s170: updating the computational efficiency in response to the determination of the completion age value; step S180; judging whether the calculation efficiency meets the preset convergence precision or not; if yes, go to step S190: and outputting the distribution result of the communication resources, the distribution result of the calculation resources, the unmanned aerial vehicle track and the time length value as the optimal result.
As above, if the convergence accuracy is not satisfied, the steps S130-160 are continuously iterated until the updated calculation efficiency satisfies the convergence accuracy or reaches the maximum iteration number, and the allocation result of the communication resource, the allocation result of the calculation resource, the trajectory of the drone, and the duration value are output as the optimal results.
As above, wherein the initialization information includes the maximum transmission power P of the terminal devicek maxMaximum sum of transmit power of mobile edge computing servers
Figure BDA0002884002250000032
Maximum CPU calculation frequency F of terminal equipmentk maxMaximum CPU computation frequency of a mobile edge computation Server
Figure BDA0002884002250000034
System bandwidth B, slot length deltatAnd terminal equipment minimum computation requirement
Figure BDA0002884002250000035
And convergence accuracy and maximum number of iterations.
The above, wherein the distributing of the computing task includes decomposing the computing task into a plurality of sub-task quantities, respectively offloading the plurality of sub-task quantities to the ground mobile edge computing server and the air mobile edge computing server for auxiliary computing, and completing the remaining part of the computing in the mobile device.
As above, wherein the amount of subtasks offloaded to the ground mobile edge compute server needs to satisfy the conditional constraint
Figure BDA0002884002250000036
Wherein
Figure BDA0002884002250000037
Indicating the amount of subtasks offloaded to server m at the nth slot, lk0,nThe subtask amount completed by the equipment per se is shown, M +1 represents a mobile edge calculation server carried on the unmanned aerial vehicle in the air, N represents the total time slot of the system,
Figure BDA0002884002250000038
represents the minimum computation demand and T represents the service period.
As above, the allocating communication resources specifically includes the following sub-steps: using the specified information as first input data; constructing a Lagrange function according to the first input data, and acquiring power allocation and bandwidth allocation of communication resources; the appointed information comprises initialization information and setting information, and the setting information comprises preset duration, terminal equipment transmitting power control, mobile edge computing server transmitting power control and system bandwidth resource allocation.
The above, wherein before the specifying information is taken as the first input data, designing of the setting information is further included; the pre-designed time length comprises data unloading time length, calculation time length and result downloading time length; designed duration of time satisfies
Figure BDA0002884002250000039
Figure BDA00028840022500000310
And
Figure BDA00028840022500000311
respectively representing the task unloading duration, the calculation duration and the result downloading duration, deltatIndicating the time slot length; the transmission power of the terminal equipment and the server is designed to meet the requirement
Figure BDA00028840022500000312
Figure BDA00028840022500000313
And
Figure BDA00028840022500000314
respectively representing the transmission power of the terminal equipment and the transmission power of the mobile edge computing server in each time slot, Pk maxAnd are and
Figure BDA00028840022500000316
respectively representing the maximum transmitting power of the initialized terminal equipment and the MEC server; designed system bandwidth resource allocation
Figure BDA0002884002250000041
Bkm,nThe method comprises the steps of representing system bandwidth allocation, K representing K pieces of ground terminal equipment, M +1 representing an MEC server carried on an unmanned aerial vehicle in the air, M and K being natural numbers, and B representing system bandwidth.
The method comprises the following steps of allocating power to the terminal equipment and the mobile edge computing server; wherein the first Lagrangian function L of the construction1The concrete expression is as follows:
Figure BDA0002884002250000042
wherein alpha iskm,n，λkm,n，μn，θk,n，υm,n，
Figure BDA0002884002250000043
And ρkFor Lagrange multiplier, η is given computational efficiency, the computational efficiency for updating the system, wtIs the weighted value of the energy consumption of the terminal equipment, hkm,nCalculating the channel gain between the terminal equipment and the mobile edge server, N0Representing the noise power spectral density, Bkm,nWhich represents the allocation of the system bandwidth,
Figure BDA0002884002250000044
and
Figure BDA0002884002250000045
respectively representing the terminal device transmission power and the transmission power of the mobile edge computing server in each time slot,
Figure BDA0002884002250000046
indicating the length of time that the task was unloaded,
Figure BDA0002884002250000047
indicating the duration of the download, Pk maxAnd are and
Figure BDA00028840022500000416
respectively representing the maximum transmitting power, P, of the initialized terminal equipment and the mobile edge computing serverm maxRepresents the maximum CPU computation frequency of the mobile edge computation server,
Figure BDA00028840022500000411
representing the terminal device minimum computation requirement.
As above, wherein the transmission power of the terminal device, the transmission power of the terminal device is obtained by the KKT condition
Figure BDA00028840022500000412
The concrete expression is as follows:
Figure BDA00028840022500000413
wherein N is0Representing the noise power spectral density, αkm,n，θk,nFor Lagrange multipliers, η is a given computational efficiency, wtIs the weighted value of the energy consumption of the terminal equipment,
Figure BDA00028840022500000414
indicates the task unload duration, hkm,nThe channel gain between the server is calculated for the terminal device and the mobile edge,
Figure BDA00028840022500000415
indicating the best bandwidth allocation.
An air-ground collaborative edge computing system specifically comprises: the system comprises an initialization unit, a calculation task allocation unit, a communication resource allocation unit, a calculation resource allocation unit, an unmanned aerial vehicle track determination unit, a duration determination unit and a calculation efficiency updating unit; the initialization unit is used for setting initialization information of the system; the calculation task allocation unit is used for responding to the completion of the initialization parameters and allocating calculation tasks; a communication resource allocation unit for allocating communication resources; a computing resource allocation unit for allocating computing resources; the unmanned aerial vehicle track determining unit is used for determining the unmanned aerial vehicle track; the duration determining unit is used for determining a duration value; and the calculation efficiency updating unit is used for updating the calculation efficiency.
The application has the following beneficial effects:
(1) the method and the device realize mutual cooperation between the ground fixed MEC server and the aerial mobile MEC server, get rid of the single mode of cooperation of the traditional pure ground MEC server and the pure aerial MEC server, and achieve the purposes of system resource distribution including communication resources and computing resources and unmanned aerial vehicle combined flight trajectory design through a complete combined design idea.
(2) The method and the device can achieve the design goal of optimal system computing efficiency, and enable the system to complete computing tasks as much as possible, so that computing requirements of the terminal equipment are better met. Meanwhile, the system can avoid generating excessive energy expenditure so as to guarantee the life cycle of the terminal equipment.
(3) This application has guaranteed the fairness between each equipment energy consumption value of system through introducing the weight coefficient for terminal equipment energy consumption and unmanned aerial vehicle flight energy consumption. Meanwhile, the priority of energy consumption control of the terminal equipment can be embodied by adjusting the weight of the energy consumption of the terminal equipment, and the best output result can be achieved by adjusting the weight value.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a flowchart of a method for computing an edge collaborative in an open space according to an embodiment of the present application;
FIG. 2 is an internal block diagram of an air-to-ground collaborative edge computing system according to an embodiment of the present application;
Detailed Description
The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The application relates to a method and a system for computing an air-ground cooperative edge. According to the method and the device, the computing efficiency of the system can be effectively improved, the minimum computing requirements of all ground terminal equipment are guaranteed, and the joint design of communication resources, computing resources and unmanned aerial vehicle tracks in the system is realized.
As shown in fig. 1, the method for computing an empty space collaborative edge provided by the present application specifically includes the following steps:
wherein within the target area, K ═ {1, 2.., K } ground terminal devices are assumed, which have minimal computational requirements
Figure BDA0002884002250000061
Meanwhile, M ═ 1, 2., M +1} MEC calculation servers, where M +1 denotes a MEC server mounted in the air on the drone and the rest are ground fixed MEC servers.
Further, the total system time slot is represented by N ═ {1,2, K, N }, and the unmanned aerial vehicle trajectory is represented as
Figure BDA0002884002250000062
Figure BDA0002884002250000063
Which represents the transmit power of the terminal device in each time slot.
Figure BDA0002884002250000064
The transmitting power of the MEC server in each time slot is represented, and the task unloading time length, the calculation time length and the result downloading time length in each time slot are respectively represented as
Figure BDA0002884002250000065
And
Figure BDA0002884002250000066
by fk,nAnd fkm,nTo represent the allocation of computing resources of the terminal equipment and the MEC server, respectively, Bkm,nRepresenting the system bandwidth allocation.
Step S110: initialization information is set.
The initialization information comprises the design of an unmanned aerial vehicle initialization track, the maximum emission power of equipment, the maximum CPU calculation frequency, the minimum calculation quantity requirement of the equipment and the maximum bandwidth of a system.
According to the position condition of the ground terminal equipment and the position deployment of the ground MEC server, the track of the unmanned aerial vehicle can be initialized by adopting a track initialization method for establishing rules, and the initialized flight track of the unmanned aerial vehicle and the initialized duration value are given. In this embodiment, a straight-line uniform-speed flying mode is used as an example, the unmanned aerial vehicle flies from a given starting point to a terminating point within the total time T, and at this time, the trajectory of the unmanned aerial vehicle is a straight line.
Specifically, the initialization setting information is, specifically, the maximum transmission power P of the terminal devicek maxMaximum transmit power of MEC server and
Figure BDA0002884002250000072
maximum CPU calculation frequency F of terminal equipmentk maxMaximum CPU computation frequency of MEC server
Figure BDA0002884002250000073
System bandwidth B, slot length deltatAnd terminal equipment minimum computation requirement
Figure BDA0002884002250000074
And convergence accuracy and maximum number of iterations. Wherein the convergence accuracy is the optimal value desired by the present embodiment.
Step S120: responsive to completion of the initialization parameters, allocation of the computing task is performed.
The terminal device decomposes the calculation task into a plurality of subtask quantities according to the real-time condition of the system, respectively unloads the plurality of subtask quantities to the ground MEC server and the air MEC server for auxiliary calculation, and completes the calculation of the rest part on the mobile device.
The subtask volume offloaded to the ground MEC server needs to satisfy the following condition, that is, the subtask volume from each terminal device to each MEC server needs to satisfy Shannon's theorem and satisfy the condition constraint of the minimum calculation requirement.
Wherein the conditional constraint is expressed as
Figure BDA0002884002250000075
Figure BDA0002884002250000076
Indicating the amount of subtasks offloaded to server m at the nth slot, lk0,nThe subtask amount completed by the equipment per se is shown, M +1 represents an MEC server carried on the unmanned aerial vehicle in the air, N represents the total time slot of the system,
Figure BDA0002884002250000077
represents the minimum computation demand and T represents the service period.
Step S130: the allocation of communication resources is performed in response to completing the allocation of the computing task.
Specifically, the allocation procedure of the communication resource specifically includes the following sub-steps:
step 1301: the designation information is used as first input data.
The designated information comprises initialization information and setting information, the setting information comprises preset duration, terminal equipment transmission power control, MEC server transmission power control and system bandwidth resource allocation, and the initialization information and the setting information are collectively referred to as the designated information and serve as first input data.
The initialization information includes the unmanned aerial vehicle initialization trajectory set in step S110, the maximum transmission power of the MEC server, the maximum CPU computation frequency of the terminal device and the MEC server, the minimum computation requirement of the terminal device, and the maximum bandwidth of the system.
Wherein before the designated information is used as the first input data, the design of the setting information is further included.
Specifically, the pre-designed duration includes a data unloading duration, a calculation duration and a result downloading duration. Wherein the length of the design needs to be satisfied
Figure BDA0002884002250000081
Figure BDA0002884002250000082
And
Figure BDA0002884002250000083
respectively representing the task unloading duration, the calculation duration and the result downloading duration, deltatIndicating the slot length.
Designed transmission power requirements of terminal equipment and server
Figure BDA0002884002250000084
Figure BDA0002884002250000085
Figure BDA0002884002250000086
And
Figure BDA0002884002250000087
respectively representing the transmission power of the terminal equipment and the transmission power of the MEC server in each time slot, Pk maxAnd
Figure BDA0002884002250000089
the maximum transmitting power of the initialized terminal equipment and the maximum transmitting power of the MEC server are respectively shown, M +1 shows the MEC server carried on the unmanned aerial vehicle in the air, and M is a natural number.
Wherein the system bandwidth resource allocation needs of design are satisfied
Figure BDA00028840022500000810
Bkm,nThe method comprises the steps of representing system bandwidth allocation, K representing K pieces of ground terminal equipment, M +1 representing an MEC server carried on an unmanned aerial vehicle in the air, M and K being natural numbers, and B representing system bandwidth.
Step 1302: and constructing a Lagrange function according to the first input data, and acquiring power allocation and bandwidth allocation of communication resources.
Wherein, the power distribution is the transmission power of the terminal equipment and the MEC server.
Firstly, through the set initialized flight trajectory of the unmanned aerial vehicle and the time length designed in the step S1301, the solutions of the transmission power of the terminal device and the server and the system bandwidth allocation can be obtained by using the lagrangian duality method.
Wherein the first Lagrangian function L of the construction1Comprises the following steps:
Figure BDA00028840022500000811
wherein alpha iskm,n，λkm,n，μn，θk,n，υm,n，
Figure BDA00028840022500000812
And ρkFor Lagrange multiplier, η is given computational efficiency, the computational efficiency for updating the system, wtIs the weighted value of the energy consumption of the terminal equipment, hkm,nFor the channel gain between the terminal equipment and the MEC server, N0Representing the noise power spectral density, Bkm,nWhich represents the allocation of the system bandwidth,
Figure BDA0002884002250000091
and
Figure BDA0002884002250000092
respectively representing the terminal equipment transmission power and the transmission power of the MEC server in each time slot,
Figure BDA0002884002250000093
indicating the length of time that the task was unloaded,
Figure BDA0002884002250000094
indicating the duration of the download, Pk maxAnd
Figure BDA0002884002250000096
respectively representing the maximum transmission power of the initialized terminal equipment and the mobile edge computing server,
Figure BDA0002884002250000097
represents the maximum CPU computation frequency of the mobile edge computation server,
Figure BDA0002884002250000098
representing the terminal device minimum computation requirement.
Wherein the weight value wtThe larger the energy priority of the terminal equipment, the longer the life cycle time of the equipment; the shorter the opposite.
Furthermore, a closed solution of the transmission power of the terminal equipment can be obtained through the KKT condition
Figure BDA0002884002250000099
Expressed as:
Figure BDA00028840022500000910
wherein N is0Representing the noise power spectral density, αkm,n，θk,nFor Lagrange multipliers, η is a given computational efficiency, wtIs the weighted value of the energy consumption of the terminal equipment,
Figure BDA00028840022500000911
indicates the task unload duration, hkm,nFor the channel gain between the terminal equipment and the MEC server,
Figure BDA00028840022500000912
indicating optimal bandwidth allocation。
Closed solution of transmit power of MEC server
Figure BDA00028840022500000913
Expressed as:
Figure BDA00028840022500000914
wherein λkm,nAnd upsilonm,nFor the lagrange multiplier, η is the given computational efficiency,
Figure BDA00028840022500000915
duration of result download, N0Representing the noise power spectral density, hkm,nFor channel gain between terminal equipment and MEC server, wherein optimal bandwidth allocation
Figure BDA00028840022500000916
Can be specifically expressed as:
Figure BDA00028840022500000917
wherein the content of the first and second substances,
Figure BDA00028840022500000918
represents L1To Bkm,nPartial derivative of, Bkm,nIndicating system bandwidth allocation, L1A first lagrangian function of the construct is represented.
Through the steps, the communication resources responsible for the system can be reasonably designed so as to fully utilize the limited resources of the system.
Step S140: in response to completing the communication resource allocation, allocation of the computing resources is performed.
Wherein, the computing resource allocation needs to satisfy the condition:
Figure BDA0002884002250000101
fk,nand fkm,nTo represent the computational resource allocation of the terminal devices and the MEC server, respectively, K represents K ground terminal devices,
Figure BDA0002884002250000102
representing the maximum CPU computation frequency, F, of the MEC serverk maxRepresenting the maximum CPU computation frequency of the terminal device. Step S140 specifically includes the following substeps:
step 1401: and taking the initialization information and the conditions required to be met by the computing resource allocation as second input data.
The initialization information includes the unmanned aerial vehicle initialization trajectory set in step S110, the maximum transmission power of the MEC server, the maximum CPU computation frequency of the terminal device and the MEC server, the minimum computation requirement of the terminal device, and the maximum bandwidth of the system.
Step 1402: and constructing a Lagrange function according to the second input data, and acquiring the CPU calculation frequency of the terminal equipment and the MEC server.
Wherein the constructed second Lagrangian function L2Expressed as:
Figure BDA0002884002250000103
in which ξk,n，ωkm,n，
Figure BDA0002884002250000104
Being lagrange multipliers, gammacCalculating the effective capacitance coefficient of the server for the MEC, η being the given computational efficiency, δtIndicating the length of the time slot, fk,nAnd fkm,nRespectively representing the computing resource allocation of the terminal device and the MEC server.
Further, the calculation frequency of the terminal device and the calculation frequency of the MEC server are obtained from the KKT condition.
Wherein the calculated frequency of the terminal device
Figure BDA0002884002250000105
Expressed as:
Figure BDA0002884002250000106
wherein, γcCalculating the effective capacitance coefficient of the server for the MEC, η being the given calculation efficiency, Fk maxIndicating the maximum CPU calculation frequency, w, of the terminal devicetIs the weighted value of the energy consumption of the terminal equipmentk,nIs a lagrange multiplier.
Optimal computing frequency of MEC server
Figure BDA0002884002250000111
Expressed as:
Figure BDA0002884002250000112
wherein
Figure BDA0002884002250000113
Indicating the calculated time duration, gammacCalculating the effective capacitance coefficient of the server for the MEC, η being the given calculation efficiency, wtIs the weighted value of the energy consumption of the terminal equipment,
Figure BDA0002884002250000114
representing the maximum CPU computation frequency, ω, of the MEC serverkm,n，
Figure BDA0002884002250000115
Is a lagrange multiplier.
By completing the allocation of the computing resources of the terminal equipment and the computing resources of the MEC server in the system, the energy expenditure of the system can be reduced as much as possible on the premise of ensuring the completion of the tasks.
Step S150: in response to completing allocation of computing resources, performing unmanned aerial vehicle trajectory determination.
Specifically, step S150 specifically includes the following substeps.
Step 1501: the initialization information, the communication resources and the calculation resource allocation result are taken as third input data.
Specifically, the allocation result of the communication resource includes the results of power allocation and bandwidth allocation, and the calculation resource allocation result includes the CPU calculation frequencies of the terminal device and the MEC server. The initialization information includes the unmanned aerial vehicle initialization trajectory set in step S110, the maximum transmission power of the MEC server, the maximum CPU calculation frequency of the terminal device and the MEC server, the minimum calculation amount requirement of the terminal device, and the maximum bandwidth of the system.
Step 1502: and obtaining the flight track of the unmanned aerial vehicle by using a continuous convex approximation method according to the third input data.
Wherein, can utilize convex optimization toolbox to solve and obtain the solution about unmanned aerial vehicle flight trajectory.
Step S160: in response to completing the trajectory determination of the drone, a determination of a duration value is made.
The step S160 specifically includes the following sub-steps:
step 1601: the initialization information and the obtained communication resource and calculation resource allocation result are taken as fourth input data.
Specifically, the initialization information includes the unmanned aerial vehicle initialization trajectory set in step S110, the maximum transmission power of the MEC server, the maximum CPU computation frequency of the terminal device and the MEC server, the minimum computation requirement of the terminal device, and the maximum bandwidth of the system. The result of allocating the communication resources is the result obtained in step S130, and the result of allocating the calculation resources is the result obtained in step S140.
Step 1602: and obtaining a time length value by using a continuous convex approximation method according to the fourth input data.
Wherein, the convex optimization toolbox can still be used for solving to obtain the specific numerical value of the duration.
Step S170: in response to the determination of the age value being completed, the computational efficiency is updated.
Wherein the updated computational efficiency η is expressed as:
Figure BDA0002884002250000121
wherein K represents K ground terminal devices, N represents total system time slot, and lk0,nRepresents the subtask amount completed by the equipment, and M +1 represents the MEC carried on the unmanned aerial vehicle in the airThe server is provided with a plurality of servers,
Figure BDA0002884002250000122
represents the amount of subtasks offloaded to server m at the nth slot, wtWeighted value for energy consumption of terminal equipment, Et，Es，EuRespectively representing the energy consumption, w, of the terminal equipment, the MEC server and the unmanned aerial vehicleuAnd the weight coefficient is the energy consumption of the unmanned aerial vehicle.
The method comprises the steps of pre-designing convergence precision and maximum iteration times, and executing step S180 after updating the calculation efficiency.
Step S180: and judging whether the updating calculation efficiency meets the convergence precision.
If the convergence accuracy is satisfied, the power allocation acquired in step S130 is set as the optimal power allocation, and the bandwidth allocation is set as the optimal bandwidth allocation. The CPU calculation frequency of the terminal device and the MEC server acquired in step S140 is taken as the optimum CPU calculation frequency of the terminal device and the MEC server. The unmanned aerial vehicle trajectory obtained in step S150 is taken as the optimal unmanned aerial vehicle trajectory. The time length value acquired in step S160 is the most optimal time length value. And executing step S190: and outputting the result.
If the convergence accuracy is not satisfied, continuously iterating to execute steps S130-160 until the updated calculation efficiency satisfies the convergence accuracy or reaches the maximum iteration number. If the convergence accuracy is satisfied, step S190 is executed.
Specifically, the steps S130-160 are continuously executed in an iterative manner, wherein during the process of updating the flight trajectory of the drone each time in an iterative manner, the trajectory of the drone is adjusted accordingly until the flight trajectory converges to a stable flight trajectory as the output optimal trajectory of the drone.
Wherein during each iteration, an update of the computational efficiency is required. After the calculation efficiency is updated each time, it is determined whether the iteration count reaches a preset maximum iteration count, and if the iteration count reaches the maximum iteration count, the power allocation obtained in step S130 is used as an optimal power allocation, and the bandwidth allocation is used as an optimal bandwidth allocation. The CPU calculation frequency of the terminal device and the MEC server acquired in step S140 is taken as the optimum CPU calculation frequency of the terminal device and the MEC server. The unmanned aerial vehicle trajectory obtained in step S150 is taken as the optimal unmanned aerial vehicle trajectory. The time length value obtained in step S160 is the most optimal time length value, and the above result is output. If the maximum iteration number is not reached, adding one to continue iteration, namely continuously performing allocation of communication resources, allocation of calculation resources, determination of an optimal unmanned aerial vehicle track and optimal duration until the maximum iteration number is reached, taking the power allocation obtained in the step S130 as optimal power allocation, and taking bandwidth allocation as optimal bandwidth allocation. The CPU calculation frequency of the terminal device and the MEC server acquired in step S140 is taken as the optimum CPU calculation frequency of the terminal device and the MEC server. The unmanned aerial vehicle trajectory obtained in step S150 is taken as the optimal unmanned aerial vehicle trajectory. The time length value obtained in step S160 is the most optimal time length value, and the result is output
It should be noted that when the maximum number of iterations is satisfied in steps S130-160, or the updated computation efficiency satisfies the convergence accuracy, the optimal communication resource allocation, computation resource allocation, drone trajectory and duration values are outputted.
As shown in fig. 2, the air-ground cooperative edge computing system provided by the present application specifically includes: the system comprises an initialization unit 201, a calculation task allocation unit 202, a communication resource allocation unit 203, a calculation resource allocation unit 204, an unmanned aerial vehicle trajectory determination unit 205, a duration determination unit 206 and a calculation efficiency updating unit 207.
Wherein the initialization unit 201 is used to set initialization information of the system.
The calculation task allocation unit 202 is connected to the initialization unit 201, and is configured to perform allocation of the calculation task in response to completion of the initialization parameter.
The communication resource allocation unit 203 is connected to the calculation task allocation unit 202, and is configured to allocate communication resources.
The calculation resource allocation unit 204 is connected to the communication resource allocation unit 203, and is configured to allocate calculation resources.
The unmanned aerial vehicle trajectory determination unit 205 is connected to the computing resource allocation unit 204, and is configured to perform unmanned aerial vehicle trajectory determination.
The duration determining unit 206 is connected to the unmanned aerial vehicle trajectory determining unit 205, and is configured to determine a duration value.
The calculation efficiency updating unit 207 is connected to the duration determining unit 206, and is configured to update the calculation efficiency.
The application has the following beneficial effects:
(3) the method and the device realize mutual cooperation between the ground fixed MEC server and the aerial mobile MEC server, get rid of the single mode of cooperation of the traditional pure ground MEC server and the pure aerial MEC server, and achieve the purposes of system resource distribution including communication resources and computing resources and unmanned aerial vehicle combined flight trajectory design through a complete combined design idea.
(4) The method and the device can achieve the design goal of optimal system computing efficiency, and enable the system to complete computing tasks as much as possible, so that computing requirements of the terminal equipment are better met. Meanwhile, the system can avoid generating excessive energy expenditure so as to guarantee the life cycle of the terminal equipment.
(5) This application has guaranteed the fairness between each equipment energy consumption value of system through introducing the weight coefficient for terminal equipment energy consumption and unmanned aerial vehicle flight energy consumption. Meanwhile, the priority of energy consumption control of the terminal equipment can be embodied by adjusting the weight of the energy consumption of the terminal equipment, and the best output result can be achieved by adjusting the weight value.
Although the present application has been described with reference to examples, which are intended to be illustrative only and not to be limiting of the application, changes, additions and/or deletions may be made to the embodiments without departing from the scope of the application.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A method for computing an air-ground cooperative edge is characterized by comprising the following steps:
s110: setting initialization information;
s120: responding to the completion of the initialization parameters, and distributing the calculation tasks;
s130: responsive to completing allocation of the computing task, performing allocation of the communication resources;
s140: responsive to completing the communication resource allocation, performing an allocation of the computing resource;
s150: after responding to the completion of the allocation of the computing resources, determining the trajectory of the unmanned aerial vehicle;
s160: determining a duration value in response to completing the trajectory determination of the unmanned aerial vehicle;
s170: updating the computational efficiency in response to the determination of the completion age value;
step S180; judging whether the calculation efficiency meets the preset convergence precision or not;
if yes, go to step S190: and outputting the distribution result of the communication resources, the distribution result of the calculation resources, the unmanned aerial vehicle track and the time length value as the optimal result.
2. The method of claim 1, wherein if the convergence accuracy is not satisfied, the steps S130-160 are executed iteratively until the updated calculation efficiency satisfies the convergence accuracy or the maximum number of iterations is reached, and the allocation result of the communication resource, the allocation result of the calculation resource, the unmanned aerial vehicle trajectory, and the duration value are output as the optimal result.
3. The air-ground cooperative edge computing method according to claim 1, wherein the initialization information includes a maximum transmission power of the terminal device
Figure FDA0002884002230000011
Maximum transmit power sum of mobile edge computing server
Figure FDA0002884002230000012
Maximum CPU calculation frequency of terminal equipment
Figure FDA0002884002230000013
Maximum CPU computation frequency of mobile edge computation server
Figure FDA0002884002230000014
System bandwidth B, slot length deltatAnd terminal equipment minimum computation requirement
Figure FDA0002884002230000015
And convergence accuracy and maximum number of iterations.
4. The air-ground cooperative edge computing method according to claim 1, wherein the distributing of the computing task comprises decomposing the computing task into a plurality of sub-task quantities, offloading the plurality of sub-task quantities to the ground mobile edge computing server and the air mobile edge computing server respectively for assisting in computing, and completing the remaining computing in the mobile device.
5. The air-ground cooperative edge computing method according to claim 4, wherein the amount of subtasks offloaded to the ground mobile edge computing server needs to satisfy a conditional constraint
Figure FDA0002884002230000021
Wherein
Figure FDA0002884002230000022
Indicating the amount of subtasks offloaded to server m at the nth slot, lk0,nThe subtask amount completed by the equipment per se is shown, M +1 represents a mobile edge calculation server carried on the unmanned aerial vehicle in the air, N represents the total time slot of the system,
Figure FDA0002884002230000023
represents the minimum computation demand, T represents the week of serviceAnd (4) period.
6. A space-ground cooperative edge computing method according to claim 2, wherein the allocating of communication resources specifically comprises the sub-steps of:
using the specified information as first input data;
constructing a Lagrange function according to the first input data, and acquiring power allocation and bandwidth allocation of communication resources;
the appointed information comprises initialization information and setting information, and the setting information comprises preset duration, terminal equipment transmitting power control, mobile edge computing server transmitting power control and system bandwidth resource allocation.
7. The air-ground cooperative edge computing method according to claim 6, further comprising, before taking the designation information as the first input data, performing design of setting information;
the pre-designed time length comprises data unloading time length, calculation time length and result downloading time length; designed duration of time satisfies
Figure FDA0002884002230000024
Figure FDA0002884002230000025
And
Figure FDA0002884002230000026
respectively representing the task unloading duration, the calculation duration and the result downloading duration, deltatIndicating the time slot length;
the transmission power of the terminal equipment and the server is designed to meet the requirement
Figure FDA0002884002230000027
Figure FDA0002884002230000028
And
Figure FDA0002884002230000029
respectively representing the terminal device transmission power and the transmission power of the mobile edge computing server in each time slot,
Figure FDA00028840022300000210
and
Figure FDA00028840022300000211
respectively representing the maximum transmitting power of the initialized terminal equipment and the MEC server;
designed system bandwidth resource allocation
Figure FDA00028840022300000212
Bkm,nThe method comprises the steps of representing system bandwidth allocation, K representing K pieces of ground terminal equipment, M +1 representing an MEC server carried on an unmanned aerial vehicle in the air, M and K being natural numbers, and B representing system bandwidth.
8. The air-ground cooperative edge computing method according to claim 6, wherein the power distribution is the transmission power of the terminal device and the mobile edge computing server;
wherein the first Lagrangian function L of the construction1The concrete expression is as follows:
Figure FDA0002884002230000031
wherein alpha iskm,n，λkm,n，μn，θk,n，υm,n，
Figure FDA0002884002230000032
And ρkFor Lagrange multiplier, η is given computational efficiency, the computational efficiency for updating the system, wtIs the weighted value of the energy consumption of the terminal equipment, hkm,nCalculating the channel gain between the terminal equipment and the mobile edge server, N0Representing the noise power spectral density, Bkm,nWhich represents the allocation of the system bandwidth,
Figure FDA0002884002230000033
and
Figure FDA0002884002230000034
respectively representing the terminal device transmission power and the transmission power of the mobile edge computing server in each time slot,
Figure FDA0002884002230000035
indicating the length of time that the task was unloaded,
Figure FDA0002884002230000036
the duration of the download is indicated,
Figure FDA0002884002230000037
and
Figure FDA0002884002230000038
respectively representing the maximum transmission power of the initialized terminal equipment and the mobile edge computing server,
Figure FDA0002884002230000039
represents the maximum CPU computation frequency of the mobile edge computation server,
Figure FDA00028840022300000310
representing the terminal device minimum computation requirement.
9. An air-ground cooperative edge calculation method as claimed in claim 8, wherein the transmission power of the terminal device is obtained by the KKT condition, and the transmission power of the terminal device is obtained by the KKT condition
Figure FDA00028840022300000311
The concrete expression is as follows:
Figure FDA00028840022300000312
wherein N is0Representing the noise power spectral density, αkm,n，θk,nFor Lagrange multipliers, η is a given computational efficiency, wtIs the weighted value of the energy consumption of the terminal equipment,
Figure FDA00028840022300000313
indicates the task unload duration, hkm,nThe channel gain between the server is calculated for the terminal device and the mobile edge,
Figure FDA00028840022300000314
indicating the best bandwidth allocation.
10. An air-ground collaborative edge computing system is characterized by specifically comprising: the system comprises an initialization unit, a calculation task allocation unit, a communication resource allocation unit, a calculation resource allocation unit, an unmanned aerial vehicle track determination unit, a duration determination unit and a calculation efficiency updating unit;
the initialization unit is used for setting initialization information of the system;
the calculation task allocation unit is used for responding to the completion of the initialization parameters and allocating calculation tasks;
a communication resource allocation unit for allocating communication resources;
a computing resource allocation unit for allocating computing resources;
the unmanned aerial vehicle track determining unit is used for determining the unmanned aerial vehicle track;
the duration determining unit is used for determining a duration value;
and the calculation efficiency updating unit is used for updating the calculation efficiency.
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