CN115334591A - Multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method - Google Patents
Multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method Download PDFInfo
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Abstract
The invention discloses a multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method, which aims at minimizing the number of unmanned aerial vehicles, optimizing unmanned aerial vehicle tracks and task unloading strategies, establishes an unmanned aerial vehicle hierarchical scheduling model, comprehensively considers task transmission delay, task unloading decisions and minimum energy consumption required by task completion, performs joint optimization on parameters such as track allocation, flight speed, data transmission rate, flight path and the like of each unmanned aerial vehicle node, reduces the energy consumption of the unmanned aerial vehicle auxiliary edge computing system for completing tasks, obtains an optimal system resource allocation strategy, programs the position of each unmanned aerial vehicle into a unit, adapts a variable-length optimization problem into a two-dimensional optimization problem, and adjusts an unmanned aerial vehicle energy consumption weighting factor to reduce the complexity of the optimization problem. The invention further provides an unmanned aerial vehicle hierarchical optimization scheduling mode to solve the problem of non-uniform variable types, and the algorithm complexity is effectively reduced.
Description
Technical Field
The invention relates to the technical field of computer wireless communication, in particular to a multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method.
Background
At present, the number of internet of things equipment is increased explosively, the number of various terminal equipment such as wearable equipment, mobile phone panels, household appliances and sensors is increased explosively, 293 billions of global networking equipment in 2023 years is estimated, the global data volume in 2025 years reaches 163ZB, and the computing-intensive application causes the cloud computing load and the network flow to increase rapidly, so that the computing cost is high, the network congestion is serious, and the computing requirements of users cannot be guaranteed reliably. Meanwhile, new technology applications are emerging continuously, applications such as automatic driving, virtual/augmented reality, multimedia video streaming and the like are popularized continuously, the applications have high requirements on computing capacity, time delay, energy consumption and safety, cloud computing cannot provide computing services with low energy consumption, low time delay, high efficiency and safety, and user experience quality cannot be guaranteed. Aiming at the challenges of limited cloud Computing resources and harsh Computing task time delay, the Mobile Edge Computing (MEC) becomes an effective technical scheme for solving problems, and is rapidly developed in academia and industry. The mobile edge computing is a brand new distributed computing mode based on a mobile network, and is a cloud server which runs at the edge of the mobile network and runs specific tasks. The MEC can draw the cloud computing and the cloud storage to the edge of the network, thereby creating a service environment with high performance, low delay and high bandwidth, accelerating the distribution and downloading of various contents, services and applications in the network and leading users to have higher-quality network experience. To traditional MEC server deployment cost height, it is poor to deploy the mobility, network coverage ability scheduling problem inadequately, the MEC system of unmanned aerial vehicle messenger, synthesize unmanned aerial vehicle's calculation power and MEC's mobility, have unique advantage, unmanned aerial vehicle combines cellular network can support mobile communication with the mode of low cost, high mobility, and unmanned aerial vehicle is as the basic station, compares with ground basic station, and unmanned aerial vehicle basic station is stronger to environmental change's adaptability. Certainly, as a new technology, the problem and the challenge of the unmanned aerial vehicle auxiliary MEC also remain to be solved, and it is obvious that the unmanned aerial vehicle has extremely limited cruising energy, the network life cycle is short due to the large overhead of the flight energy consumption of the unmanned aerial vehicle, and the computing capability is limited, meanwhile, the freedom degree of the multi-dimensional track design of the unmanned aerial vehicle is not fully exerted by the traditional 2D track optimization, and the communication performance needs to be improved, so that the resource allocation method for researching the mobile edge computing system based on the layered scheduling of multiple unmanned aerial vehicles under the large-scale ground terminal has certain significance.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments, and in this section as well as in the abstract and the title of the invention of this application some simplifications or omissions may be made to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made keeping in mind the above problems occurring in the prior art and/or the problems occurring in the prior art.
Therefore, the invention aims to solve the technical problems that the unmanned aerial vehicle has extremely limited cruising energy, the network life cycle is short due to large overhead of flight energy consumption of the unmanned aerial vehicle, the computing capability is limited, meanwhile, the freedom degree of multi-dimensional track design of the unmanned aerial vehicle is not fully exerted in the traditional 2D track optimization, and the communication performance needs to be improved.
In order to solve the technical problems, the invention provides the following technical scheme: a multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method comprises the following steps:
establishing an edge unloading scene with a large-scale ground terminal and a plurality of unmanned aerial vehicles;
establishing an energy consumption model required by task local execution;
establishing an energy consumption model required by unloading tasks to the unmanned aerial vehicle;
establishing an energy consumption model required by hovering of the unmanned aerial vehicle;
the method comprises the steps of taking minimized system energy consumption as a total target, and considering unmanned aerial vehicle track deployment and task unloading strategy constraints to obtain an optimal solution;
the system is subjected to hierarchical scheduling, and an upper-layer system obtains an optimal solution of unmanned aerial vehicle track deployment by using an improved differential evolution algorithm, namely a hierarchical optimization algorithm;
and after the optimal solution of the upper-layer system is obtained, the optimal solution of the task unloading strategy is obtained by the lower-layer system.
The invention relates to a preferable scheme of a multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method, wherein the preferable scheme comprises the following steps: considering a mobile edge computing system for layering scheduling of unmanned aerial vehicles, wherein the mobile edge computing system comprises M ground terminals and N unmanned aerial vehicles;
the location of the ith user is denoted as (x) i ,y i 0), i belongs to M; the drone is flying at a fixed height H, with coordinates expressed as (X) j ,Y j H), j belongs to N; definition of U i A task to be executed for the ith ground terminal; defining K as the execution mode of each task, K belongs to K, K =0 represents the local execution of the task, and K belongs to the execution mode of each task>0 indicates that the task is offloaded to drone k for execution, at which time a i,k ＝1，a i,k =1 represents task U i Performed in k-mode; the energy consumption required for the local execution of the task on the ground terminal equipment is defined as follows:
wherein eta 1 Representing effective switched capacitance, v being a constant greater than 0, C i Indicating the number of CPU revolutions required to execute the task;
defining the energy consumption required by unloading the task to the unmanned aerial vehicle for execution as follows:
where P denotes the transmission power of each mobile device, D i Represents the size of the uploaded data of the user i, r i,k Representing data upload rate, η 2 Representing the effective switched capacitance;
task U i The data upload rate of (a) is:
where B is the channel bandwidth and P is the transmit power of each ground terminal device, β 0 Denotes the channel power gain at the reference distance, G 0 Is a constant, N 0 Is the noise power spectral density; θ represents a fixed beamwidth directional antenna of the drone;
the energy consumption required by hovering of the unmanned aerial vehicle is defined as follows:
E H ＝P 0 T
wherein, P 0 Indicating hover power, T indicating hover time.
As a preferred scheme of the multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method, the method comprises the following steps: establishing a system model, wherein the system model specifically comprises an objective function and a constraint condition; the objective function is to minimize system energy consumption, and the main constraint conditions are unmanned aerial vehicle deployment strategy and mission planning.
Wherein, the system model minimizes the system energy consumption as follows:
the constraints are as follows:
wherein, the matrix a is defined as the unloading decision, a i,0 Indicating that the task is executing locally, a i,k Indicating that the task is unloaded to be executed on an edge server deployed on the drone, C1 indicating that the maximum distance between the ground terminal i and the drone j is the coverage radius of the drone j,
representing the distance between the unmanned aerial vehicle j and the ground terminal i; c2 represents a minimum distance constraint between two drones to prevent collisions; c3 represents the constraint condition of the maximum number of executed tasks of each unmanned aerial vehicle; c4 represents a task execution completion constraint; c5 and C6 constraint on f in k mode i,k >0, matrix f i,k Indicating to task U in k-mode i The computing resource allocation of (2); c7 and C8 are transmission delay constraints for each task.
The invention relates to a preferable scheme of a multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method, wherein the preferable scheme comprises the following steps: the system is subjected to hierarchical scheduling, and an upper-layer system obtains an optimal solution of unmanned aerial vehicle track deployment by using an improved differential evolution algorithm, namely a hierarchical optimization algorithm, and specifically comprises the following steps:
firstly, an initialization operator is carried out, namely the first one is noneA position is randomly generated by a man-machine and stored in the P, a position is generated for the second unmanned aerial vehicle, if the distance between the two satisfies the constraint C2, namely the two can not collide, the position of the second unmanned aerial vehicle is stored in the P, and num is stored at the moment vio =0, otherwise the location of the second drone is illegal, num vio ＝num vio +1 statistics on number of failures, when num vio >200 hours, restart the initialization operator, if num vio If the position of the unmanned aerial vehicle does not exceed 200, regenerating the position of the second unmanned aerial vehicle;
generating a third and a fourth … unmanned aerial vehicle positions until all the unmanned aerial vehicle positions are successfully generated to obtain an initial deployment P;
performing upper layer optimization to obtain optimal deployment of the unmanned aerial vehicles, namely optimal number and positions of the unmanned aerial vehicles;
setting N as an initial value max ，
The number of N is then reduced by 1 until at least one task can no longer be performed within the propagation delay constraints
For the ith cell in P
The mutation operator and the crossover operator of (c) are expressed as:
wherein
And
are three mutually independent units randomly selected from P,
respectively, a mutation vector and a test vector, F is a measurement vector, j rand Is an integer randomly selected between 1 and 2 to ensure
At least with
Differing by at least one dimension, rand j (0,1) represents a uniformly distributed random number between 0 and 1, and CR represents a crossover control parameter.
As a preferred scheme of the multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method, the method comprises the following steps: the method for obtaining the optimal solution of unmanned aerial vehicle trajectory deployment by the hierarchical optimization algorithm comprises the following steps:
the positions of all unmanned aerial vehicles are coded into a unit, and the unit is integrated into a population P to represent the deployment of the unmanned aerial vehicles, so that the length of each unit is 2 in the evolution process of the algorithm, and the deployment of the unmanned aerial vehicles can be optimized in a two-dimensional space;
the hierarchical optimization algorithm firstly gives an initial value of a population P with N units, substitutes for a solving unloading decision a and a resource allocation f, and if all tasks can be executed and completed within the transmission delay limit, an execution elimination operator continuously reduces the number of units until the tasks cannot be executed and completed within the transmission delay limit;
generating a sub-population Q used as an intermediate quantity by a differential evolution algorithm, wherein the Q is used for updating the population P, and checking a variable num if the updated task cannot be executed within the transmission delay limit inf ，num inf Indicates the number of consecutive unexecutable entries in { N, P, a, f }, when num inf The set threshold of 1000 is reached and N is no longer reduced, resulting in an optimum value for N, when the last one is returned to that which can be performed within the propagation delay limitsStates to optimize { P, a, f };
if the updated task can be executed and completed within the transmission delay limit, the elimination operator is continuously executed, and then the process is repeated until the maximum value FEs of fitness evaluation is reached max And the flow is ended.
The invention relates to a preferable scheme of a multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method, wherein the preferable scheme comprises the following steps: after the optimal solution of the layer system is obtained, the method is used for the lower layer system to obtain the optimal solution of the task unloading strategy, and the specific processing flow is as follows:
the lower-layer optimization target is to optimize a task unloading strategy after the known unmanned aerial vehicle is deployed, the unmanned aerial vehicle deployment meets the constraint C2, and the task unloading strategy is to be established
Substituting to solve the lower optimization problem model as follows:
s.t.C1,C3,C4,C5,C6,C7,C8
under constraints C7 and C8, f i,k Must not be less than the minimum value, will
And
substituting constraints C7 and C8 to obtain
And
the optimal resource allocation f is thus obtained as:
the invention relates to a preferable scheme of a multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method, wherein the preferable scheme comprises the following steps: after the optimal resource allocation f is obtained, the constraints C5, C6, C7, and C8 are satisfied, and the lower optimization problem model can be rewritten into the following mode:
s.t.C1,C3,C4
wherein
Representing the minimum energy consumption in the case of task local computation,
the minimum energy consumption required by the unmanned aerial vehicle calculation is indicated when the task is unloaded, and at the moment, the lower-layer optimization problem only needs to be optimized by a i,k And a is i,k ＝0 or 1；
Dividing all tasks into three classes, wherein the first class is a local computing mode, the second class is a full unloading computing mode, the third class is a partial unloading mode, and setting M 1 ,M 2 ,M 3 The matrix of the unloading decision a is obtained by the number of tasks contained in the three types of tasks respectively as follows:
the invention relates to a preferable scheme of a multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method, wherein the preferable scheme comprises the following steps: the priority of the three types of tasks is sequenced, the priority of the first type of tasks is highest, then unloading decision deployment optimization of the second type of tasks and the third type of tasks is carried out, wherein the second type of tasks are selected to be executed firstly with the least unloading tasks, all tasks are completed with higher possibility, and then a certain participation mode is selected from the tasks to calculate the minimum energy consumption; the third category of tasks considers both participation mode and task execution energy consumption, where the least offloaded and least energy-consuming tasks are executed first, so that all tasks can be executed with as little system energy as possible.
The invention has the beneficial effects that: and a hierarchical scheduling optimization technology is introduced, the upper layer optimizes unmanned aerial vehicle track deployment, the lower layer optimizes task unloading strategies, an optimal scheme can be found to minimize system energy consumption, and a hierarchical scheduling model is adopted to jointly optimize unmanned aerial vehicle track deployment and task unloading strategies under large-scale ground terminals so as to obtain the minimum energy consumption required by the whole system. According to the method, the length of each unit in the execution process of the evolutionary algorithm is fixed by programming the position of each unmanned aerial vehicle into one unit, so that the high complexity of the evolutionary algorithm caused by the variable length of a decision variable is solved, and meanwhile, the complexity of the optimization problem is reduced by adjusting the hovering energy consumption weighting factor of the unmanned aerial vehicle. And a hierarchical optimization algorithm is adopted, and the upper-layer optimization problem comprises a continuous decision variable X j And Y j The lower layer optimization problem contains a binary decision variable a i,k The problem that decision variables in the optimization problem are not uniform in type is solved skillfully.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor. Wherein:
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a diagram of a system model of the present invention;
fig. 3 is a coding interpretation diagram of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Next, the present invention is described in detail with reference to the drawings, and in the detailed description of the embodiments of the present invention, the cross-sectional views illustrating the device structures are not enlarged partially according to the general scale for convenience of illustration, and the drawings are only exemplary, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Examples
Referring to fig. 1 to 3, the present embodiment provides a method for allocating resources of an edge computing in hierarchical scheduling for multiple terminals and multiple drones, including the following steps:
s1: establishing an edge unloading scene with a large-scale ground terminal and a plurality of unmanned aerial vehicles;
s2: establishing an energy consumption model required by task local execution;
s3: establishing an energy consumption model required by unloading tasks to the unmanned aerial vehicle;
s4: establishing an energy consumption model required by hovering of the unmanned aerial vehicle;
s5: based on the energy consumption model required by the local execution of the task in the S2, the S3 and the S4, the energy consumption model required by the unloading of the task to the unmanned aerial vehicle execution and the energy consumption model required by the hovering of the unmanned aerial vehicle, the energy consumption of the system is taken as a total target, and the optimal solution is obtained by considering the track deployment of the unmanned aerial vehicle and the constraint of a task unloading strategy;
s6: the system is subjected to hierarchical scheduling, and an upper-layer system obtains an optimal solution of unmanned aerial vehicle track deployment by using an improved differential evolution algorithm, namely a hierarchical optimization algorithm;
s7: and after the optimal solution of the upper-layer system is obtained, the optimal solution of the task unloading strategy is obtained by the lower-layer system.
As shown in fig. 1, for step S1, a mobile edge computing system for hierarchical scheduling of unmanned aerial vehicles is considered, which includes M ground terminals and N unmanned aerial vehicles; each ground terminal has certain computing power and meets the requirement of locally executing some simple tasks. And the unmanned aerial vehicle carrying the edge server has strong computing power. Ground terminal can carry out data uninstallation to unmanned aerial vehicle. The set of ground terminals and drones are defined as M and N, respectively.
The location of the ith user is denoted as (x) i ,y i 0), i ∈ M; the drone is flying at a fixed height H, with coordinates expressed as (X) j ,Y j H), j ∈ N; definition of U i A task to be executed for the ith ground terminal; defining K as the execution mode of each task, K belongs to K, K =0 represents the local execution of the task, and K belongs to the execution mode of each task>0 indicates that the task is offloaded to drone k for execution, at which time a i,k ＝1，a i,k =1 represents task U i Performed in k-mode; due to limited local computing power and latency requirements, users can process their tasks locally or offload portions of the tasks to legitimate drones for processing.
Further, for the steps S2-4, a mathematical model is utilized to model the steps into a formula optimization problem. Based on the above description, the following mathematical model can be established:
task U i When the method is executed locally on the ground terminal equipment, the time spent on the task is as follows:
in S2, energy consumption required for local execution of the task at the ground terminal device is defined as:
wherein eta is 1 Representing effective switched capacitance, v being a constant greater than 0, C i Indicating the number of CPU revolutions required to execute the task;
in S3, when the task needs to be unloaded to the unmanned aerial vehicle, the task is firstly transmitted to the unmanned aerial vehicle, then executed by a mobile edge server on the unmanned aerial vehicle, and the result is returned to the ground terminal after the execution is finished. Distance between ground terminal i and unmanned aerial vehicle j:
at the same time, if U i Executed on drone j, ground terminal i must be within the coverage of drone j, so there is a constraint C1:
r denotes the coverage radius of each drone, R = Htan θ, θ denotes the fixed beam bandwidth of the directional antenna of each drone, the distance between two drones is expressed as:
since a minimum distance needs to be maintained between two drones to avoid collision, there is a constraint C2:
due to the limit of the computing power of each mobile edge server, the maximum number of tasks that can be executed by each unmanned aerial vehicle is n max Therefore, there is a constraint C3:
task U i The data uploading rate of (1) is:
where B is the channel bandwidth and P is the transmit power of each ground terminal device, β 0 Denotes the channel power gain at the reference distance, G 0 Is a constant number, N 0 Is the noise power spectral density; θ represents a fixed beamwidth directional antenna of the drone; will U i The transmission time and the calculation time are added to obtain a completed task U i Total time of
Defining the energy consumption required by unloading the task to the unmanned aerial vehicle for execution as follows: adding the energy required for transmission and the energy required for calculation to obtain a finished task U i Total energy of
Where v is a constant, P represents the transmit power of each mobile device, D i Represents the size of the uploaded data of the user i, r i,k Representing data upload rate, η 2 Represents the effective switched capacitance;
further, the energy consumption required by hovering of the unmanned aerial vehicle is defined as E, and the energy consumed by the unmanned aerial vehicle when hovering at the height H is defined as H ：
E H ＝P 0 T
Wherein, P 0 Indicating hover power, T indicating hover time.
Based on the above analysis, in S5, a system model is established, which specifically includes an objective function and a constraint condition, that is, the optimization problem in the present invention can be modeled as follows:
wherein, the system model is as follows:
wherein, the matrix a is defined as the unloading decision, a i,0 Indicating that the task is executing locally, a i,k C1 table representing tasks offloaded to execution on edge servers deployed on dronesThe maximum distance between the ground terminal i and the unmanned plane j is the coverage radius of the unmanned plane j,
representing the distance between the unmanned plane j and the ground terminal i; c2 represents a minimum distance constraint between two drones to prevent collisions; c3 represents the constraint condition of the maximum number of executed tasks of each unmanned aerial vehicle; c4 represents a task execution completion constraint; c5 and C6 constraint of f in k-mode i,k >0, matrix f i,k Indicating to task U in k-mode i The computing resource allocation of (1); c7 and C8 are transmission delay constraints for each task.
It should be noted that, the formula optimization problem of energy consumption required for local execution by the ground terminal device is a non-convex nonlinear optimization problem, and a general optimization method cannot solve the optimization problem, whereas the traditional differential evolution algorithm provides a possibility for solving the optimization problem because the traditional differential evolution algorithm is a heuristic search method based on a population without gradient information.
Due to the need for the location (X) of drone j j ,Y j ) Offload decision a i,k Resource allocation f i,k Joint optimization is performed, so the number of decision variables to be optimized is (2 (N + M) + 1), the number of decision variables increases with increasing M or N, the number of decision variables is very complex in view of the large-scale ground terminal situation considered in the present invention, and due to a i,k Is a binary decision variable, N is an integer decision variable, X j ,Y j ,f i,k The method is a continuous decision variable, the variable types are not uniform, and the deployment of the unmanned aerial vehicle and the task unloading strategy are mutually influenced, so that the layered optimization algorithm is provided.
Further, in S6, the system is hierarchically scheduled, and the upper system obtains an optimal solution for unmanned aerial vehicle trajectory deployment by using an improved differential evolution algorithm, i.e., a hierarchical optimization algorithm, and specifically includes the following steps: firstly, initializing an operator, randomly generating a position for a first unmanned aerial vehicle, storing the position into P, generating a position for a second unmanned aerial vehicle, and if the distance between the position and the position meets the constraint C2, namely the distance between the position and the position meets the constraint C2No collision, then store into P with second unmanned aerial vehicle position, num this moment vio =0, otherwise the second drone's location is illegal, num vio ＝num vio +1 statistics on number of failures, when num vio >200 hours, restart the initialization operator, if num vio If the position of the unmanned aerial vehicle does not exceed 200, regenerating the position of the second unmanned aerial vehicle;
generating a third and a fourth … unmanned aerial vehicle positions until all the unmanned aerial vehicle positions are successfully generated to obtain an initial deployment P;
performing upper layer optimization to obtain optimal deployment of the unmanned aerial vehicles, namely the optimal number and positions of the unmanned aerial vehicles;
setting an initial value of N to N max ，
The number of N is then reduced by 1 until at least one task can no longer be performed within the propagation delay constraints
For the ith cell in P
The mutation operator and the crossover operator of (c) are expressed as:
wherein
And
are three mutually independent units randomly selected from P,
respectively, a mutation vector and a test vector, F is a measurement vector, j rand Is an integer randomly selected between 1 and 2 to ensure
At least with
Differing by at least one dimension, rand j (0,1) represents a uniformly distributed random number between 0 and 1, and CR represents a crossover control parameter.
In the evolution process, a differential evolution algorithm is used on P to generate a sub-population Q, wherein the elements in Q are used for replacing elements randomly selected by P, so that the original P is updated to R, if R meets a constraint C2, an unloading decision a 'and a resource allocation f' are calculated, if N, R, a ', f' can execute more tasks or the energy consumption of N, R, a ', f' is lower than that of N, P, a, f under the constraint of transmission delay, N, R, a ', f' are replaced by N, R, a ', f', P, a, f is defined to represent an optimized state, if N, P, a, f is still infeasible after 1000 times of continuous updating, the number N of unmanned aerial vehicles is considered to be unreduceable, the optimal solution of N is N +1, the flag =1 is set, N, P, a, f is returned to the last executable state, and an updating operator is executed to optimize the positions of P and the unmanned aerial vehicles; if N, P, a, f is still feasible, setting flag =0, interrupting the update operator, and continuing to execute the elimination operator on N, P, a, f to continue to reduce the number N of the drones.
A hierarchical scheduling algorithm is introduced, and the specific method comprises the following steps: the positions of all unmanned aerial vehicles are coded into a unit, and the unit is integrated into a population P to represent the deployment of the unmanned aerial vehicles, so that the length of each unit is 2 in the evolution process of the algorithm, and the deployment of the unmanned aerial vehicles can be optimized in a two-dimensional space;
the hierarchical optimization algorithm firstly gives an initial value of a population P with N units, substitutes for a solution unloading decision a and a resource allocation f, and if all tasks can be executed and completed within the transmission delay limit, an execution elimination operator continuously reduces the number of units until the tasks cannot be executed and completed within the transmission delay limit;
generating a sub-population Q used as an intermediate quantity by a differential evolution algorithm, wherein the Q is used for updating the population P, and checking a variable num if the updated task cannot be executed within the transmission delay limit inf ，num inf Indicates the number of consecutive unexecutable entries in { N, P, a, f }, when num inf When the set threshold value is reached, N can not be reduced any more, the optimal value of N is obtained, and then the state which can be executed and completed within the transmission delay limit is returned to the latest state to optimize { P, a, f };
if the updated task can be executed and completed within the transmission delay limit, the elimination operator is continuously executed, and then the process is repeated until the maximum value FEs of fitness evaluation is reached max And ending the process.
After obtaining the optimal solution of the upper system, the method is used for the lower system to obtain the optimal solution of the task unloading strategy, and the specific processing flow is as follows:
the lower-layer optimization target is to optimize a task unloading strategy after unmanned aerial vehicle deployment. Unmanned aerial vehicle deployment is known to mean N, X j ,Y j ,E H Are known and drone deployment satisfies constraint C2 because if drone deployment does not satisfy constraint C2, it can not be programmed into R to be discarded when upper layer optimization. Combining the components established in steps (1.2) and (1.3)
Substituting to solve the lower optimization problem model as follows:
s.t.C1,C3,C4,C5,C6,C7,C8
from the lower optimization problem model, it can be seen that the more computing resources are consumed and the greater the energy consumption is when the mode is determined, because the energy consumption is along with f i,k Or f i,0 Is increased. Therefore, to reduce power consumption, it is desirable to reduce f as much as possible i,k ,f i,0 I.e. f, at the same timeUnder constraints C7 and C8, f i,k Must not be less than the minimum value, will
And
substituting constraints C7 and C8 to obtain
And
the optimal resource allocation f is thus obtained as:
after the optimal resource allocation f is obtained, constraints C5, C6, C7, and C8 are satisfied, and the lower optimization problem model can be rewritten into the following mode:
s.t.C1,C3,C4
wherein
Representing the minimum energy consumption in the case of task local computation,
the minimum energy consumption required by the unmanned aerial vehicle calculation is indicated when the task is unloaded, and at the moment, the lower-layer optimization problem only needs to be optimized by a i,k A and a i,k =0 or 1 having only two values, greedy is used thereforThe cardiac algorithm optimizes the underlying optimization problem.
Dividing all tasks into three classes, wherein the first class is a local computing mode, the second class is a full unloading computing mode, the third class is a partial unloading mode, and setting M 1 ,M 2 ,M 3 The matrix of the unloading decision a is obtained by the number of tasks contained in the three types of tasks respectively as follows:
the priority of the three types of tasks is sequenced, the priority of the first type of tasks is highest, then unloading decision deployment optimization of the second type of tasks and the third type of tasks is carried out, wherein the second type of tasks are selected to be executed firstly with the least unloading tasks, all tasks are completed with higher possibility, and then a certain participation mode is selected from the tasks to calculate the minimum energy consumption; the third category of tasks considers both the participation mode and the task execution energy consumption, wherein the task with the least unloading task and the least energy consumption is executed first, so that all tasks can be executed under the condition that the system can be exhausted to a small extent.
It is important to note that the construction and arrangement of the present application as shown in the various exemplary embodiments is illustrative only. Although only a few embodiments have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters (e.g., temperatures, pressures, etc.), mounting arrangements, use of materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter recited in this application. For example, elements shown as integrally formed may be constructed of multiple parts or elements, the position of elements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of this invention. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. In the claims, any means-plus-function clause is intended to cover the structures described herein as performing the recited function and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present inventions. Therefore, the present invention is not limited to a particular embodiment, but extends to various modifications that nevertheless fall within the scope of the appended claims.
Moreover, in an effort to provide a concise description of the exemplary embodiments, all features of an actual implementation may not be described (i.e., those unrelated to the presently contemplated best mode of carrying out the invention, or those unrelated to enabling the invention).
It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure, without undue experimentation.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should 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 of the present invention, which should be covered by the claims of the present invention.
Claims (8)
1. A multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method is characterized by comprising the following steps: the method comprises the following steps:
establishing an edge unloading scene with a large-scale ground terminal and a plurality of unmanned aerial vehicles;
establishing an energy consumption model required by task local execution;
establishing an energy consumption model required by unloading tasks to the unmanned aerial vehicle;
establishing an energy consumption model required by hovering of the unmanned aerial vehicle;
the method comprises the steps of taking minimized system energy consumption as a total target, and considering unmanned aerial vehicle track deployment and task unloading strategy constraints to obtain an optimal solution;
the system is subjected to hierarchical scheduling, and an upper-layer system obtains an optimal solution of unmanned aerial vehicle track deployment by using an improved differential evolution algorithm, namely a hierarchical optimization algorithm;
and after the optimal solution of the upper-layer system is obtained, the optimal solution of the task unloading strategy is obtained by the lower-layer system.
2. The multi-terminal multi-UAV hierarchical scheduling assisted edge computing resource allocation method according to claim 1, characterized in that: considering a mobile edge computing system for layering scheduling of unmanned aerial vehicles, wherein the mobile edge computing system comprises M ground terminals and N unmanned aerial vehicles;
the location of the ith user is denoted as (x) i ,y i 0), i ∈ M; the drone flies at a fixed height H, with coordinates expressed as (X) j ,Y j H), j ∈ N; definition of U i A task to be executed for the ith ground terminal; defining K as the execution mode of each task, K belongs to K, K =0 represents the local execution of the task, and K belongs to the execution mode of each task>0 indicates that the task is offloaded to drone k for execution, at which time a i,k ＝1，a i,k =1 represents task U i Performed in k-mode; the energy consumption required for the local execution of the task on the ground terminal equipment is defined as follows:
wherein eta is 1 Representing effective switched capacitance, v being a constant greater than 0, C i Indicating the number of CPU revolutions required to execute the task;
the energy consumption required for defining the task to be unloaded to the unmanned aerial vehicle for execution is as follows:
where P denotes the transmission power of each mobile device, D i Represents the size of the uploaded data of the user i, r i,k Representing data upload rate, η 2 Represents the effective switched capacitance;
task U i The data uploading rate of (1) is:
where B is the channel bandwidth and P is the transmit power of each ground terminal device, β 0 Denotes the channel power gain at the reference distance, G 0 Is a constant number, N 0 Is the noise power spectral density; θ represents a fixed beamwidth directional antenna of the drone;
the energy consumption required by hovering of the unmanned aerial vehicle is defined as follows:
E H ＝P 0 T
wherein, P 0 Indicating the hover power and T indicating the hover time.
3. The multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method according to claim 1 or 2, characterized in that: establishing a system model, wherein the system model specifically comprises an objective function and a constraint condition;
wherein, the system model is as follows:
s.t.C1∶
C2∶
C3∶
C4∶
C5∶
C6∶
C7∶
C8∶
wherein, a is defined as an unloading decision, a i,0 Indicating that the task is executing locally, a i,k Indicating that the task is offloaded to be executed on an edge server deployed on drone, C1 indicates that the maximum distance between ground terminal i and drone j is the coverage radius of drone j,
representing the distance between the unmanned aerial vehicle j and the ground terminal i; c2 represents a minimum distance constraint between two drones to prevent collision; c3 represents the constraint condition of the maximum number of executed tasks of each unmanned aerial vehicle; c4 represents a task execution completion constraint; c5 and C6 constraint of f in k-mode i,k >0, matrix f i,k Indicating to task U in k-mode i The computing resource allocation of (1); c7 and C8 are transmission delay constraints for each task.
4. The multi-terminal multi-unmanned aerial vehicle hierarchical scheduling auxiliary edge computing resource allocation method according to claim 3, characterized in that: the system is subjected to hierarchical scheduling, and an upper-layer system obtains an optimal solution of unmanned aerial vehicle track deployment by using an improved differential evolution algorithm, namely a hierarchical optimization algorithm, and specifically comprises the following steps:
firstly, initializing an operator, randomly generating a position for a first unmanned aerial vehicle and storing the position into P, then generating a position for a second unmanned aerial vehicle, and if the distance between the two satisfies constraint C2, namely the two can not collide, storing the position of the second unmanned aerial vehicle into P, wherein num at the moment vio =0, otherwise the location of the second drone is illegal, num vio ＝num vio +1 statistics on number of failures, when num vio >200 hours, restart the initialization operator, if num vio If the position of the unmanned aerial vehicle does not exceed 200, regenerating the position of the second unmanned aerial vehicle;
generating a third and a fourth … unmanned aerial vehicle positions until all the unmanned aerial vehicle positions are successfully generated to obtain an initial deployment P;
performing upper layer optimization to obtain optimal deployment of the unmanned aerial vehicles, namely optimal number and positions of the unmanned aerial vehicles;
setting N as an initial value max ，
The number of N is then reduced by 1 until at least one task can no longer be performed within the propagation delay constraints
For the ith cell in P
The mutation operator and the crossover operator of (c) are expressed as:
wherein
And
are three mutually independent units randomly selected from P,
respectively, a mutation vector and a test vector, F is a measurement vector, j rand Is an integer randomly selected between 1 and 2 to ensure
At least with
Differing by at least one dimension, rand j (0,1) represents a uniformly distributed random number between 0 and 1, and CR represents a crossover control parameter.
5. The multi-terminal multi-unmanned aerial vehicle hierarchical scheduling assisted edge computing resource allocation method according to claim 4, characterized in that: the obtaining of the optimal solution of unmanned aerial vehicle trajectory deployment by the hierarchical optimization algorithm comprises the following steps:
the position of each unmanned aerial vehicle is coded into a unit, and the units are integrated into a population P to represent the deployment of the unmanned aerial vehicles, so that the length of each unit is 2 in the evolution process of the algorithm, and the deployment of the unmanned aerial vehicles can be optimized in a two-dimensional space;
the hierarchical optimization algorithm firstly gives an initial value of a population P with N units, substitutes for a solution unloading decision a and a resource allocation f, and if all tasks can be executed and completed within the transmission delay limit, an execution elimination operator continuously reduces the number of units until the tasks cannot be executed and completed within the transmission delay limit;
at this moment, the utility modelGenerating a sub-population Q used as an intermediate quantity by an over-differential evolution algorithm, using the Q to update the population P, and checking a variable num if the updated task cannot be executed within the transmission delay limit inf ，num inf Indicates the number of consecutive unexecutable entries in { N, P, a, f }, when num inf When the set threshold value is reached, N can not be reduced any more, the optimal value of N is obtained, and then the state which can be executed and completed within the transmission delay limit is returned to the latest state to optimize { P, a, f };
if the updated task can be executed and completed within the transmission delay limit, the elimination operator is continuously executed, and then the process is repeated until the maximum FEs of fitness evaluation is reached max And the flow is ended.
6. The multi-terminal multi-unmanned aerial vehicle hierarchical scheduling assisted edge computing resource allocation method according to claim 5, characterized in that: after the optimal solution of the upper-layer system is obtained, the optimal solution is used for the lower-layer system to carry out task unloading strategy solving, and the specific processing flow is as follows:
the lower-layer optimization target is to optimize a task unloading strategy after the known unmanned aerial vehicle is deployed, the unmanned aerial vehicle deployment meets the constraint C2, and the task unloading strategy is to be established
Substituting to solve the lower optimization problem model as follows:
s.t.C1,C3,C4,C5,C6,C7,C8
under constraints C7 and C8, f i,k Must not be less than the minimum value, will
And
substituting constraints C7 and C8 to obtain
And
the optimal resource allocation f is thus obtained as:
7. the multi-terminal multi-unmanned aerial vehicle hierarchical scheduling assisted edge computing resource allocation method according to claim 6, characterized in that: after the optimal resource allocation f is obtained, the constraints C5, C6, C7, and C8 are satisfied, and the lower optimization problem model can be rewritten into the following mode:
s.t.C1,C3,C4
wherein
Representing the minimum energy consumption in the case of task local computation,
the minimum energy consumption required by the unmanned aerial vehicle calculation is expressed when the task is unloaded, and at the moment, only a needs to be optimized for the lower-layer optimization problem i,k A and a i,k ＝0 or 1；
Dividing all tasks into three classes, the first class is a local computing mode, the second class is a full unloading computing mode, the third class is a partial unloading mode, and setting M 1 ,M 2 ,M 3 The number of tasks included in each of the three classes of tasks, to obtain a matrix of offload decisions a, e.g.The following:
8. the multi-terminal multi-unmanned aerial vehicle hierarchical scheduling assisted edge computing resource allocation method according to claim 7, characterized in that: the priority of the three types of tasks is sequenced, the priority of the first type of tasks is highest, then unloading decision deployment optimization of the second type of tasks and the third type of tasks is carried out, wherein the second type of tasks are selected to be executed firstly with the least unloading tasks, all tasks are completed with higher possibility, and then a certain participation mode is selected from the tasks to calculate the minimum energy consumption; the third category of tasks considers both the participation mode and the task execution energy consumption, wherein the task with the least unloading task and the least energy consumption is executed first, so that all tasks can be executed under the condition that the system can be exhausted to a small extent.
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| CN117939429A (en) * | 2023-10-26 | 2024-04-26 | 广东工业大学 | Double-layer unmanned aerial vehicle-assisted Internet of vehicles signal coverage method |
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