CN116634466A - Unmanned aerial vehicle-based collaborative multi-access edge computing task unloading and resource allocation method - Google Patents
Unmanned aerial vehicle-based collaborative multi-access edge computing task unloading and resource allocation method Download PDFInfo
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- H—ELECTRICITY
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Abstract
The method comprises the steps of constructing an optimization model with the aim of minimizing service delay and guaranteeing service fairness among user equipment according to the requirements of each user equipment in terms of service experience, simplifying a problem model based on a Dinkelbach method and a convex optimization theory, providing a four-stage alternate iterative optimization algorithm, decomposing the optimization target into four sub-optimization targets of an unmanned plane track decision, a task unloading decision, a service cache decision and a resource allocation decision, and utilizing an alternate solving iterative algorithm to carry out solving calculation until the target converges to obtain the task unloading and resource allocation decision in the unmanned plane cooperative multi-access edge calculation network. The present disclosure enables lower service delays while guaranteeing better fairness among all user devices.
Description
Technical Field
The disclosure relates to the technical field of mobile communication, in particular to a method for unloading and distributing resources based on unmanned aerial vehicle cooperative multi-access edge computing tasks.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In recent years, with the development and popularization of mobile communication technology, many new applications such as online video, map navigation, mobile payment, face recognition and the like are presented, and then, the explosion of networking intelligent devices leads to explosive growth of data. Meanwhile, in the case of incidents such as various infectious diseases, face-to-face communication between people becomes difficult, and dependence on network medical treatment, online learning and remote work is significantly increased. Such applications are typically delay sensitive, requiring significant communication and computing resources. Multiple access edge computing, which was previously mobile edge computing, has become a key technology in next generation wireless networks in order to support a large number of smart devices and to handle large amounts of data in time. By deploying mobile edge computing servers on the edge side of the communication network (e.g., terrestrial cellular infrastructure), user equipment can offload data to the edge to improve the service experience.
However, many problems exist with current edge computing systems. The fixed location ground mobile edge computing server cannot be adjusted according to the requirements of the terminal. Due to non-line-of-sight links, their channel quality may be poor, resulting in limited communication rates. Also, some user devices may discard the mobile edge computing service due to serious obstructions or damages caused by natural disasters. Recently, drones have become a promising technology due to their flexible deployment and low cost, improving wireless connectivity and providing broad coverage in mobile edge computing networks. In general, there are two techniques for unmanned aerial vehicle-assisted mobile edge computing networks, where the unmanned aerial vehicle acts as an air relay and an air mobile edge computing server. In addition, with the rapid growth of user equipment, a single or even multiple unmanned aerial vehicle may not meet the demands of a large number of computationally intensive and delay sensitive applications such as virtual reality, intelligent traffic, etc., and the service delay and fairness problems, i.e., the service experience ratio of the user equipment, cannot be maximized.
Disclosure of Invention
In order to solve the problems, the method for unloading and distributing the resources based on the cooperation of the unmanned aerial vehicle and the multi-access edge computing task is provided, the unmanned aerial vehicle is utilized to perform mobile edge computing service cooperation on computing and buffering resources, service delay is minimized, and service fairness among user equipment is guaranteed.
According to some embodiments, the present disclosure employs the following technical solutions:
the unmanned aerial vehicle-based collaborative multi-access edge computing task unloading and resource allocation method comprises the following steps:
initializing a collaborative computing task unloading environment, and providing mobile edge computing service for user equipment by a base station and all unmanned aerial vehicles in a collaborative manner, acquiring a task set, and dividing a task period into a plurality of time slots with equal duration;
the method comprises the steps of obtaining the input data size of a calculation task of a user request service in each time slot, constructing an optimization model with the aim of minimizing service delay and guaranteeing service fairness among user equipment according to the requirement of each user equipment in terms of service experience, simplifying a problem model based on a Dinkelbach method and a convex optimization theory, providing an optimization algorithm of four-stage alternate iteration, decomposing the optimization target into four sub-optimization targets of unmanned plane track decision, task unloading decision, service cache decision and resource allocation decision, carrying out solution calculation by using an alternate solution iteration algorithm until the targets are converged, and obtaining and executing task unloading and resource allocation decision in an unmanned plane cooperative multi-access edge calculation network.
Further, the task offloading decision-making optimization based on satisfaction includes: fixing unmanned plane track, bandwidth resource allocation decision, service buffer decision, calculation resource allocation decision and auxiliary variable to optimize task unloading decision, and defining optimization target formula of task unloading sub-problem.
In the optimization process of the task unloading decision, defining a plurality of unmanned aerial vehicles and a set of tasks, each user equipment sends a task unloading request to the unmanned aerial vehicle associated with the unmanned aerial vehicle at the beginning of a time slot, a proper unloading position is selected for the task based on the satisfaction of a user, under the current task unloading decision, the value of an objective function corresponding to each user equipment is calculated, and if the maximum delay tolerance of all the user equipment is met and the calculation resource and the energy consumption limit of each unmanned aerial vehicle are not exceeded, the task unloading decision at the moment is proper.
In the set, each task has different satisfaction degrees for different unloading positions, the satisfaction degree value is related to task processing delay and fairness, and the larger the task processing delay is, the lower the fairness is, the smaller the satisfaction degree value is.
Further, the optimization of the service buffer decision is to fix the track, the bandwidth resource allocation decision, the task unloading decision, the calculation resource allocation decision and the auxiliary variable of the unmanned aerial vehicle to optimize the service buffer decision, and define an optimization formula of the service buffer decision sub-problem.
Considering the cache space utilization rate of the unmanned aerial vehicle, the priority of the service needed by the task with a high service cache decision value for caching on the unmanned aerial vehicle is high until the upper limit of the cache space of the unmanned aerial vehicle is reached.
Further, the optimization of the unmanned aerial vehicle track comprises a fixed task unloading decision, a bandwidth resource allocation decision, a service cache decision, a calculation resource allocation decision and an auxiliary variable to optimize the unmanned aerial vehicle track, and a formula of an unmanned aerial vehicle track optimization sub-problem is defined.
In the optimization of the unmanned aerial vehicle track, track planning is used as an optimization variable, and the optimization variable consists of the coordinate position of each time slot of the unmanned aerial vehicle in the whole task period.
Further, the optimization of the computing resource allocation decision comprises the steps of giving a track of the unmanned aerial vehicle, a task unloading decision, a service buffer decision and auxiliary variables, and defining an optimization formula of a computing resource allocation sub-problem, wherein the computing resource allocation sub-problem is a convex problem, and the convex optimization is adopted to obtain an optimal solution of bandwidth resource allocation and computing resource allocation.
Further, task unloading, service caching, track planning and resource allocation are combined, service experience ratio is improved to the greatest extent, four-stage alternate iterative optimization is provided to solve the original problem, task unloading decision, service caching decision and unmanned plane track planning are respectively iterated and optimized until target values are converged, and after each round of four-stage free iteration, parameters of the Dinkelbach method are updated.
Compared with the prior art, the beneficial effects of the present disclosure are:
according to the unmanned aerial vehicle-based multi-access-edge-cooperation-based computing task unloading and resource allocation method, the unmanned aerial vehicle can effectively utilize computing and caching resources to conduct mobile edge computing service cooperation, service delay is minimized, and service fairness among user equipment is guaranteed.
In order to improve service experience, under the constraint of unmanned energy budget and delay demand, joint optimization task unloading, resource allocation, track planning and service cache placement are considered, and are expressed as service experience ratio maximization problems. Since the original problem is a mixed integer non-convex programming problem with a fractional structure, the problem is difficult to solve in polynomial time. The method simplifies a problem model based on a Dinkelbach method and a convex optimization theory, and provides a four-stage alternate iterative service ratio maximization algorithm to solve the problem. Numerical results indicate that the proposed algorithm can reduce service delay by 78.2% compared to other baseline algorithms, while improving fairness among all user devices by 53.0%.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain the disclosure, and do not constitute an undue limitation on the disclosure.
Fig. 1 is a schematic diagram of a multi-unmanned auxiliary mobile edge computing scenario in an embodiment of the present disclosure;
FIG. 2 is an exploded schematic view of an optimization method according to an embodiment of the present disclosure.
Detailed Description
The disclosure is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
An embodiment of the present disclosure provides a method for unloading and allocating resources based on unmanned aerial vehicle cooperative multi-access edge computing tasks, including:
step one: initializing a collaborative computing task unloading environment, and providing mobile edge computing service for user equipment by a base station and all unmanned aerial vehicles in a collaborative manner, acquiring a task set, and dividing a task period into a plurality of time slots with equal duration;
step two: acquiring the size of input data of a computing task of a user requesting service in each time slot, and constructing an optimization model with the aim of minimizing service delay and guaranteeing service fairness among user equipment according to the requirement of each user equipment on service experience;
step three: based on a Dinkelbach method and a convex optimization theory, a problem model is simplified, a four-stage alternate iterative optimization algorithm is provided, an optimization target is decomposed into four sub-optimization targets of unmanned plane track decision, task unloading decision, service buffer decision and resource allocation decision, and the alternate solution iterative algorithm is utilized to carry out solution calculation until the targets are converged, so that the task unloading and resource allocation decision in the unmanned plane cooperative multi-access edge computing network is obtained and executed.
As an embodiment, the disclosure is a method for task offloading and resource allocation in a service experience-based cache unmanned aerial vehicle collaborative multi-access edge computing network, which solves the service delay and fairness problems, that is, maximizes the service experience ratio of a user device. In order to solve the technical aim, the method specifically comprises the following implementation processes:
step 1: for a mobile edge computing network supported by unmanned aerial vehicles, the mobile edge computing problem assisted by multiple unmanned aerial vehicles is proposed to maximize the service experience ratio of user equipment.
Initializing a collaborative computing task unloading environment, and providing mobile edge computing service for user equipment by a base station and all unmanned aerial vehicles in a collaborative manner, acquiring a task set, and dividing a task period into a plurality of time slots with equal duration;
the base station and all unmanned aerial vehicles cooperate to provide mobile edge computing service for M user equipment. One of the macro base stations, U drones and M user devices are denoted b, y= {1,2, & gt, U }, m= {1,2, & gt, M }, respectively. The set of all services that a macro base station can provide is denoted Σ= {1, 2..s }. As the connection between the user equipment and the drone can be kept stable for a sufficiently short period of time. For ease of presentation, the task period N is divided into segments of equal duration Δ t And t= {1,2,... Each user equipment has only one time delay sensitive task within one time slot, can be offloaded to a drone or macro base station for processing, and each task is atomic and indivisible. In time slot t, user m generates a time delay sensitive task requesting service sCan be represented by a 3-tuple
Assume thatThe input data size representing the computational task of the request service s for user m in time slot t. Let->The computational effort of the computational task requesting service s on behalf of user m in time slot t. />Is the processing delay tolerance of the task requesting service s, exceeds this limit, and is consequently ineffective for user m, and each user device has different requirements in terms of service experience; the proposed goal is to maximize the service experience ratio of the user equipment. This may be achieved by jointly optimizing task offloading, service caching, drone trajectories and resource allocation. The method comprises the following steps:
wherein C is 1 Meaning that in order to offload the task of user m requesting service s to drone u, service s needs to be cached in drone u. C (C) 2 Representing the spectral resource allocation constraints of the user devices associated with the same drone. C (C) 3 Representing computational resource constraints of a single drone. C (C) 4 Representing that the total memory space occupied by the services stored on each drone must not exceed the total memory size of the drone by representing K u 。C 5 Indicating that the ground user should be within coverage as an associated drone. C (C) 6 Representing the positional change constraint of the unmanned aerial vehicle between any two time slots. C (C) 7 Meaning that a minimum safety distance should be maintained between any two unmanned aerial vehicles to ensure collision avoidance between them during time slot t. C (C) 8 Indicating that the flight trajectories of all unmanned aerial vehicles should be within the target area. C in order to avoid high communication delays of the unmanned aerial vehicle 9 Indicating that the horizontal distance of the associated drone from the drone selected as a relay does not exceed R uav 。C 10 Representing the upper energy limit E of each unmanned aerial vehicle in time slot t th 。C 11 Indicating that the completion delay of each task cannot exceed the processing delay tolerance of the task. C (C) 12 It is pointed out that the tasks generated by each user equipment are allowed to be precisely offloaded to one unmanned or macro base station in the vicinity. C (C) 13 And C 14 Representing the service cache decision and task offload decision variables, respectively, are binary, constraint C 15 The variables representing bandwidth and computing resource allocation are contiguous.
Step 2: the method comprises the steps of obtaining the input data size of a calculation task of a user request service in each time slot, constructing an optimization model with the aim of minimizing service delay and guaranteeing service fairness among user equipment according to the requirement of each user equipment in terms of service experience, simplifying a problem model based on a Dinkelbach method and a convex optimization theory, providing an optimization algorithm of four-stage alternate iteration, decomposing the optimization target into four sub-optimization targets of unmanned plane track decision, task unloading decision, service cache decision and resource allocation decision, carrying out solution calculation by using an alternate solution iteration algorithm until the targets are converged, and obtaining and executing task unloading and resource allocation decision in an unmanned plane cooperative multi-access edge calculation network.
Specifically, in order to improve the service experience ratio to the maximum extent, the optimization target is decomposed into four sub-optimization targets of unmanned plane track, task unloading decision, service cache decision and resource allocation decision. After each round of these four free-form iterations, the parameters of the Dinkelbach method are updated. To decouple the non-convex object, it is decomposed into different sub-objects, and an iterative algorithm for alternate solution is proposed, comprising the following steps:
s1: firstly, optimizing task unloading decisions based on satisfaction, and fixing unmanned aerial vehicle tracks Q, bandwidth resource allocation decisions B, service cache decisions A, calculation resource allocation decisions F and auxiliary variables eta to optimize the task unloading decisions. Formulating the task offloading optimization sub-problem as:
s.t.C 1 、C 3 、C 5 、C 9 -C 12 、C 14
to better describe the optimization process of task offloading, several sets of related drones and tasks are defined. Let the set of drones caching the service s required by task m be defined asEach user equipment sends a task offloading request to its associated unmanned aerial vehicle at the beginning of a time slot, and the set of task offloading requests received by the associated unmanned aerial vehicle u is denoted +.>Which contains tasks associated with user equipment and collaborative drone offloading. If the associated unmanned plane u belongs to the set +.>The drone hits the service s required for task m. Then, the process is carried out,the hit task is added to +.>Missed add to +.>During the initialization of the task offloading decision, the hypothesis set +.>The calculation can be performed by the drone u. Set->Further offloading of tasks in (1) to the set->Zhongpi (Chinese style) 3-1 The cooperative unmanned aerial vehicle i or the base station with the largest value. Set->Further offloading of tasks in (1) to the set->A co-operating drone i or a base station.
An appropriate offloading location is selected for the task based on the user's satisfaction. Under the current task unloading decision, calculating the pi corresponding to each user equipment 3-1 The value of the objective function. If the maximum delay tolerance of all user devices is met and the computational resource and energy consumption limits of each drone are not exceeded, then the task offloading decision at this point is appropriate. Then, calculate the satisfaction pi of each task 3-1 Sequentially selecting setsThe task with the smallest satisfaction value is further unloaded. It is then sub->Move to->Up to the aggregate->The maximum delay tolerance and CSS resources and energy consumption limitations are met by all tasks in (a).
At the collectionEach task having a different satisfaction with a different unloading location. The value of satisfaction is related to task processing delay and fairness. The greater the task processing delay and the lower the fairness, the smaller the value of satisfaction. Then, the task m rejected by the associated drone u and requiring further offloading has a satisfaction value for the collaborative drone i, which can be expressed as:
task m of requesting service s rejected by the associated drone u has a satisfaction with macro base station b, which can be expressed as:
task m, which is associated with the unmanned aerial vehicle's request service s, sends an offload request preferentially to a location with high satisfaction. If the requested location is a macro base station, the offload request will be accepted directly,if the requested location is a collaborative drone, the collaborative drone is required to allow. If the unload request is rejected, it will be sent to the next best unload position in the next iteration until accepted, let ∈ ->The above process is repeated until the unloading positions of all tasks are found.
S2: the optimization of the service buffer decision is to fix the track, the bandwidth resource allocation decision, the task unloading decision, the calculation resource allocation decision and the auxiliary variable of the unmanned aerial vehicle to optimize the service buffer decision, and define an optimization formula of the service buffer decision sub-problem.
Specifically, the track Q, the bandwidth resource allocation decision B, the task offloading decision X, the computing resource allocation decision F and the auxiliary variable η of the unmanned aerial vehicle are fixed to optimize the service caching decision a. The service cache decision sub-problem is formulated as:
s.t.C 1 、C 4 、C 10 、C 11 、C 13
because the cache space of the unmanned aerial vehicle is limited, all programs cannot be cached. The service caching decisions will be optimized to minimize task processing delays and ensure fairness. In order to improve the utilization rate of the buffer space, consider pi 3-2 The priority of the service required by the task with higher value to be cached on the unmanned aerial vehicle is higher until the upper limit of the cache space of the unmanned aerial vehicle is reached. Let theAnd |M u The i represent the set and number of tasks offloaded to the drone u, respectively. Accordingly let Sigma u Sum sigma u The i represent the set and number of services required for the task offloaded to the drone u, respectively. In general terms, the process is carried out,because multiple tasks may request the same service, there is Σ u |＜|M u | a. The invention relates to a method for producing a fibre-reinforced plastic composite. The service s unmanned aerial vehicle u required by the task m has a priority value which can be expressed as:
respectively press the tasks requesting the same serviceIs arranged in descending order of values and then +.>The task with the largest value is stored in the collection sigma u 。/>Wherein-> Then aggregate sigma u The elements in->The values are arranged in descending order, will->And the service with larger value is sequentially cached until the upper limit of the cache space of the unmanned plane u is reached. We will further Σ' u ＝{s 1 ，s 2 ，...，s J-1 [ wherein ]>
S3: the optimization of the unmanned aerial vehicle track comprises the steps of optimizing the unmanned aerial vehicle track by fixing task unloading decisions, bandwidth resource allocation decisions, service buffer decisions, calculation resource allocation decisions and auxiliary variables, and defining a formula of an unmanned aerial vehicle track optimization sub-problem.
Specifically, the fixed task offloading decision X, the bandwidth resource allocation decision B, the service buffering decision a, the computing resource allocation decision F and the auxiliary variable η optimize the trajectory Q of the unmanned aerial vehicle. The unmanned aerial vehicle track sub-problem is formulated as:
s.t.C 5 、C 8 -C 11
the trajectory planning of the unmanned aerial vehicle is used as an optimization variable and consists of the coordinate position of each time slot of the unmanned aerial vehicle in the whole task period. The constant is removed and the temperature of the material is changed,can be simplified as:
note that due to the presence in the objective functionProblem P is known 3-3 Is non-convex. Constraint C 10 And C 11 Is non-convex with respect to the flight trajectory Q of the drone. Constraint C 7 Is non-convex in that the domain of the convex function is a non-empty convex set. Thus, solving the non-convex problem is challenging.
Next, to deal with the non-convexity problem, a successive approximation method is used to implement problem P 3-3 Is a locally optimal solution of (1). The key idea of the successive convex approximation method is to approximate the non-convex function as a convex function in an iterative manner.
Definition of the definitionFor the available spectral efficiency from the user equipment m to the drone u, it can be written as:
it is easy to seeIs about->Is a convex function of (a). Thus, it can be achieved by having at any pointThe first order taylor expansion of (c) implements the global lower bound. Given the flight trajectory of the unmanned aerial vehicle for the kth iterationThe lower bound of (2) may be calculated as:
wherein the method comprises the steps ofAnd->The available spectral efficiency from user equipment m to drone u and +.>About->They are given as follows:
definition of the definitionFor the available spectral efficiency from drone u to drone i, it can be written as:
is about->Is a convex function of (a). Thus, it can be achieved by having at any pointThe first order taylor expansion of (c) implements the global lower bound. Giving the unmanned plane flight trajectory of the kth iteration +.>Andcan be calculated as
Wherein the method comprises the steps ofAnd->The available spectral efficiency from unmanned plane u to unmanned plane i and +.>About->They are given as follows:
definition of the definitionFor the available spectral efficiency from drone u to macro base station b, it can be written as:
is about->Is a convex function of (a). Thus, it can be achieved by having at any pointThe first order taylor expansion of (c) implements the global lower bound. Given the flight trajectory of the unmanned aerial vehicle for the kth iterationThe lower bound of (2) may be calculated as:
wherein the method comprises the steps ofAnd->The available spectral efficiency from the drone u to the macro base station b and +.>About->They are given as follows:
in constraint C 7 In (C) because ofRegarding the flight trajectory of the drone being convex, we use a successive convex approximation method to relax the constraints. By giving arbitrary->And->The following inequality is obtained using the first-order taylor expansion:
thus, the first and second substrates are bonded together,the lower bound of (2) may be calculated as:
in addition, for constraint C 10 In the flying power ofThe first and third items are about speed +.>Is a convex function of (a). Introduces a continuous relaxation variable +.>To process the second term in the propulsion power formula, which becomes:
the above formula can be simplified to obtain:
unmanned aerial vehicle flight speed at given kth iterationAnd->The right side of the above inequality is approximated by applying a first order taylor expansion, as follows:
then, can pass throughTo approximate it is:
based on the above discussion, solve the problem pi 3-3 The original problem in the kth iteration can be restated as the following approximation problem P' 3-3 (k)。
s.t.C 5 、C 6 、C 8 、C 9
/>
After proving the convexity of the problem, the optimal solution of unmanned aerial vehicle trajectory planning can be obtained effectively through convex optimization. Notably, from the approximation problem pi' 3-3 The optimal solution obtained is problem pi 3-3 Is defined below.
S4: the optimization of the computing resource allocation decision comprises the steps of giving a unmanned aerial vehicle track, a task unloading decision, a service buffer decision and auxiliary variables, and defining an optimization formula of a computing resource allocation sub-problem, wherein the computing resource allocation sub-problem is a convex problem, and the convex optimization is adopted to obtain the optimal solution of bandwidth resource allocation and computing resource allocation.
Specifically, given an unmanned aerial vehicle trajectory Q, a task offloading decision X, a service caching decision a, and an auxiliary variable η, a computational resource allocation sub-problem is formulated as:
s.t.C 2 、C 3 、C 10 、C 11 、C 15
because of the problem pi 3-4 Is a convex problem and a convex optimization tool is employed to obtain an optimal solution for bandwidth resource allocation and computing resource allocation.
The present disclosure proposes an alternate optimization to solve the original problem P 1 . The key idea is to respectively and iteratively optimize anyAnd carrying out traffic unloading decision, service buffer decision and unmanned aerial vehicle trajectory planning until the target value converges.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the specific embodiments of the present disclosure have been described above with reference to the drawings, it should be understood that the present disclosure is not limited to the embodiments, and that various modifications and changes can be made by one skilled in the art without inventive effort on the basis of the technical solutions of the present disclosure while remaining within the scope of the present disclosure.
Claims (10)
1. The unmanned aerial vehicle-based collaborative multi-access-edge computing task unloading and resource allocation method is characterized by comprising the following steps of:
initializing a collaborative computing task unloading environment, and providing mobile edge computing service for user equipment by a base station and all unmanned aerial vehicles in a collaborative manner, acquiring a task set, and dividing a task period into a plurality of time slots with equal duration;
the method comprises the steps of obtaining the input data size of a calculation task of a user request service in each time slot, constructing an optimization model with the aim of minimizing service delay and guaranteeing service fairness among user equipment according to the requirement of each user equipment in terms of service experience, simplifying a problem model based on a Dinkelbach method and a convex optimization theory, providing an optimization algorithm of four-stage alternate iteration, decomposing the optimization target into four sub-optimization targets of unmanned plane track decision, task unloading decision, service cache decision and resource allocation decision, carrying out solution calculation by using an alternate solution iteration algorithm until the targets are converged, and obtaining and executing task unloading and resource allocation decision in an unmanned plane cooperative multi-access edge calculation network.
2. The unmanned aerial vehicle-based collaborative multi-access edge computing task offloading and resource allocation method of claim 1, wherein satisfaction-based task offloading decision optimization comprises: fixing unmanned plane track, bandwidth resource allocation decision, service buffer decision, calculation resource allocation decision and auxiliary variable to optimize task unloading decision, and defining optimization target formula of task unloading sub-problem.
3. The method for task offloading and resource allocation based on unmanned aerial vehicle cooperation multi-access edge calculation according to claim 2, wherein in the optimization process of task offloading decision, a plurality of unmanned aerial vehicles and a set of tasks are defined, each user equipment sends a task offloading request to its associated unmanned aerial vehicle at the beginning of a time slot, a suitable offloading position is selected for the task based on user satisfaction, under the current task offloading decision, the value of an objective function corresponding to each user equipment is calculated, and if the maximum delay tolerance of all user equipment is satisfied and the calculation resource and energy consumption limit of each unmanned aerial vehicle are not exceeded, then the task offloading decision at this time is suitable.
4. The unmanned aerial vehicle-based collaborative multi-access edge computing task offloading and resource allocation method of claim 3, wherein each task has a different satisfaction with a different offloading location in the aggregate, the value of satisfaction being related to task processing delay and fairness, the greater the task processing delay and the lower the fairness, the smaller the value of satisfaction.
5. The unmanned aerial vehicle-based collaborative multi-access-edge computing task offloading and resource allocation method of claim 1, wherein the optimization of the service caching decision is to fix a trajectory of the unmanned aerial vehicle, a bandwidth resource allocation decision, a task offloading decision, a computing resource allocation decision, and auxiliary variables to optimize the service caching decision, and define an optimization formula of a service caching decision sub-problem.
6. The unmanned aerial vehicle-based collaborative multi-access-edge computing task offloading and resource allocation method according to claim 5, wherein the priority of the service required for the task with a high service caching decision value to be cached on the unmanned aerial vehicle is high until the upper limit of the cache space of the unmanned aerial vehicle is reached, taking the cache space utilization of the unmanned aerial vehicle into consideration.
7. The unmanned aerial vehicle-based collaborative multi-access edge computing task offloading and resource allocation method of claim 1, wherein the optimization of the unmanned aerial vehicle trajectory comprises optimizing the unmanned aerial vehicle trajectory by fixing task offloading decisions, bandwidth resource allocation decisions, service caching decisions, computing resource allocation decisions, and auxiliary variables, and defining a formula of an unmanned aerial vehicle trajectory optimization sub-problem.
8. The unmanned aerial vehicle collaborative multi-access-edge-based task offloading and resource allocation method according to claim 7, wherein in the optimization of the unmanned aerial vehicle trajectory, trajectory planning is used as an optimization variable, and the optimization variable consists of the coordinate position of each time slot of the unmanned aerial vehicle in the whole task period.
9. The unmanned aerial vehicle-based collaborative multi-access edge computing task offloading and resource allocation method of claim 1, wherein optimization of computing resource allocation decisions comprises defining an optimization formula of computing resource allocation sub-problems given unmanned aerial vehicle trajectories, task offloading decisions, service buffering decisions and auxiliary variables, wherein the computing resource allocation sub-problems are convex problems, and the convex optimization is adopted to obtain optimal solutions of bandwidth resource allocation and computing resource allocation.
10. The unmanned aerial vehicle-based collaborative multi-access-edge computing task unloading and resource allocation method according to claim 1, wherein task unloading, service buffering, track planning and resource allocation are jointly optimized, service experience ratio is improved to the greatest extent, four-stage alternate iterative optimization is provided to solve the original problem, task unloading decision, service buffering decision and unmanned aerial vehicle track planning are respectively iterated and optimized until target values are converged, and after each round of four-stage free iteration, parameters of the Dinkelbach method are updated.
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