Efficient Configuration Method and System for Multi-dimensional Edge Resources under 5G Power MEC

Abstract

This invention provides a method and system for efficient allocation of multi-dimensional edge resources under 5G power MEC. The method is applied to an edge server for efficient allocation of multi-dimensional edge resources under 5G power MEC, and includes: determining the terminal's priority and first demand degree for edge server resources based on resource requests sent by the terminal; determining the terminal's second demand degree for the transmission capacity of the transmission node based on the transmission power consumption of the transmission node; determining the total revenue function of the efficient allocation system for multi-dimensional edge resources under 5G power MEC based on the priority, first demand degree, and second demand degree; determining the total revenue function as the objective function, and determining the resource allocation results of the edge server and transmission node based on the objective function, optimization objective, and constraints. This invention rationally allocates computing resources under limited computing resources and power consumption, meets the quality of service of the power grid, and maximizes the system's utility.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a method and system for efficient configuration of multi-dimensional edge resources under 5G power MEC. Background Technology

[0002] With the continuous development of mobile internet and industrial IoT, 5G smart grid applications are emerging, and the number of mobile terminals in the power grid is increasing dramatically. This places higher demands on network services such as computing power, network quality, transmission rate, and response latency. The explosive growth in traffic has brought unprecedented pressure to the power grid, greatly affecting service quality. In order to effectively alleviate the challenges faced by the 5G power grid in terms of high load, high bandwidth, high mobility, and low latency, the concept of mobile edge computing (MEC) has emerged.

[0003] However, due to limited computing resources, the issue of multidimensional resource allocation has always been a hot topic in edge computing environments. And how to rationally allocate computing resources under conditions of limited resources and power consumption is a problem that urgently needs to be solved in existing technologies. Summary of the Invention

[0004] This invention provides a method and system for efficient multi-dimensional edge resource allocation under 5G power MEC, which solves the defects of unreasonable resource allocation in the prior art when resources and power consumption are limited. With the goal of maximizing system utility, it rationally allocates resources of each dimension to the terminal and provides corresponding transmission power consumption.

[0005] This invention provides a method for efficient configuration of multi-dimensional edge resources under 5G power MEC. The method is applied to the edge server of a multi-dimensional edge resource efficient configuration system under 5G power MEC. The 5G power MEC multi-dimensional edge resource efficient configuration system further includes terminals and transmission nodes. The method includes:

[0006] Based on the resource requests sent by the terminal, the priority of the terminal and the terminal's first degree of demand for edge server resources are determined.

[0007] Based on the transmission power consumption of the transmission node, the second requirement of the terminal for the transmission capability of the transmission node is determined.

[0008] Based on the priority, the first demand degree, and the second demand degree, the total revenue function of the multi-dimensional edge resource efficient allocation system under 5G power MEC is determined;

[0009] The total revenue function is determined as the objective function, and based on the objective function, the optimization objective, and the constraints, the resource configuration results of the edge server and the transmission node are determined.

[0010] According to the present invention, a method for efficient allocation of multi-dimensional edge resources under 5G power MEC is provided. The method for determining the total revenue function of the efficient allocation system for multi-dimensional edge resources under 5G power MEC based on the priority, the first demand degree, and the second demand degree includes:

[0011] Based on the priority and the first demand level, a first upper limit function for the terminal's demand for the edge server resources is determined;

[0012] Based on the second demand level, determine the second upper limit function of the terminal's demand for the transmission capacity of the transmission node;

[0013] Based on the first demand ceiling function and the second demand ceiling function, determine the terminal revenue function;

[0014] Based on the terminal revenue function, edge server revenue function, and transmission node revenue function, the total revenue function of the multi-dimensional edge resource high-efficiency configuration system under 5G power MEC is determined.

[0015] According to the present invention, a method for efficient allocation of multi-dimensional edge resources under 5G power MEC includes a process for determining the edge server revenue function and the transmission node revenue function, comprising:

[0016] Based on the resource request, the unit resource cost of the edge server, and the unit resource price of the edge server, determine the revenue function of the edge server;

[0017] The revenue function of the transmission node is determined based on the transmission power consumption, the unit power consumption cost of the transmission node, and the unit power consumption price of the transmission node.

[0018] According to the present invention, a method for efficient allocation of multi-dimensional edge resources under 5G power MEC is provided, wherein the optimization objective is to maximize the objective function, and the constraints include resource constraints and power grid power consumption constraints.

[0019] The resource constraint condition is used to limit the total number of resource requests sent by the terminals of the 5G power MEC multi-dimensional edge resource high-efficiency configuration system to not exceed the resource limit provided by the edge server;

[0020] The power grid power consumption constraint is used to limit the total transmission power consumption of the transmission nodes in the 5G power MEC multi-dimensional edge resource high-efficiency configuration system to not exceed the upper limit of power consumption resources provided by the power grid.

[0021] According to the present invention, a method for efficient allocation of multi-dimensional edge resources under 5G power MEC is provided, wherein the power grid power consumption constraint is shown in Equation 1:

[0022]

[0023] in, Here, m represents the total number of terminals in the 5G power MEC multi-dimensional edge resource high-efficiency configuration system, m is the terminal serial number, and d is the type serial number of the edge server resource. φ represents the total number of resource types of the edge servers. d α represents the energy consumption per unit of resource of type d. md For terminal m's task resource request for resource of type d, P m Let P be the transmission power vector of terminal m. max Total power consumption provided to the power grid.

[0024] According to the present invention, a method for efficient allocation of multi-dimensional edge resources under 5G power MEC includes determining the resource allocation results of the edge server and the transmission node based on the objective function, optimization objective, and constraints, comprising:

[0025] Based on the objective function, the optimization objective, and the constraints, the optimal solution of the objective function is obtained using the external approximation method.

[0026] Based on the optimal solution, the resource configuration results of the edge server and the transmission node are determined.

[0027] This invention also provides a high-efficiency configuration system for multi-dimensional edge resources under 5G power MEC, including: a terminal, a transmission node, and an edge server;

[0028] The terminal is used to send resource requests to the server;

[0029] The transmission node is used to enable communication between the terminal and the edge server;

[0030] The edge server is configured to determine the priority of the terminal and the terminal's first demand for edge server resources based on the resource request sent by the terminal; and to determine the terminal's second demand for the transmission capacity of the transmission node based on the transmission power consumption of the transmission node.

[0031] The edge server is further configured to determine the total revenue function of the multi-dimensional edge resource efficiency configuration system under the 5G power MEC based on the priority, the first demand degree, and the second demand degree; determine the total revenue function as the objective function; and determine the resource configuration results of the edge server and the transmission node based on the objective function, the optimization objective, and the constraints.

[0032] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the efficient configuration method for multi-dimensional edge resources under 5G power MEC as described above.

[0033] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the efficient configuration method for multi-dimensional edge resources under 5G power MEC as described above.

[0034] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the efficient configuration method for multi-dimensional edge resources under 5G power MEC as described above.

[0035] This invention provides a method and system for efficient multi-dimensional edge resource allocation under 5G power MEC. Considering that resource allocation should maximize the utility of each terminal in the system, it offers a new interpretation of the rationality of edge server resource allocation. A resource allocation mechanism based on urgency factors such as priority, first demand level, and second demand level is proposed. Terminal resource requests are processed according to urgency factors, achieving on-demand allocation under different demand levels. This adapts to the complex needs of terminals, more rationally allocating resources across various dimensions to terminals and providing corresponding transmission power consumption. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the efficient configuration method for multi-dimensional edge resources under 5G power MEC provided by the present invention.

[0038] Figure 2 This is a schematic diagram of vertices within or on the feasible domain boundary of the objective function provided by the present invention;

[0039] Figure 3 This is a schematic diagram of the feasible region of the objective function when the blocks formed by the vertices provided by this invention completely contain the objective function.

[0040] Figure 4 This is a schematic diagram of the block formed by the vertices provided by the present invention containing part of the feasible region of the objective function;

[0041] Figure 5This is a schematic diagram of the process of selecting a new vertex by comparing the utility values ​​of a function, as provided by the present invention.

[0042] Figure 6 This is a schematic diagram illustrating the usage of various types of resources provided in the embodiments of the present invention;

[0043] Figure 7 This is a schematic diagram illustrating the average resource utilization rate of different solutions provided in the embodiments of the present invention;

[0044] Figure 8 A schematic diagram illustrating the relationship between transmission energy consumption and urgency factor provided in this embodiment of the invention;

[0045] Figure 9 A comparative diagram of the benefits of different systems provided in this embodiment of the invention;

[0046] Figure 10 This is a schematic diagram of the structure of the high-efficiency configuration system for multi-dimensional edge resources under 5G power MEC provided by the present invention;

[0047] Figure 11 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0049] With the rapid development of smart grid technology, the demand for computing resources from 5G power grid terminals has increased dramatically, leading to the development of power cloud computing technology. The core of power cloud computing technology is to offload some or all tasks from 5G terminals to cloud servers. However, due to the generally distant geographical location of cloud servers, despite their powerful computing capabilities, the distance from the terminals increases transmission energy consumption and significantly worsens response latency. To meet the service needs of the power grid, 5G power MEC technology has emerged. However, due to limited computing resources, the problem of multi-dimensional resource allocation remains one of the key issues to be addressed in edge computing environments.

[0050] 5G power MEC allows users to offload computing tasks to servers deployed at the edge of the power grid, enabling business localization, significantly reducing the amount of remote data transmission, and lowering network latency and transmission costs. While offloading computing tasks to edge servers can alleviate the demands of terminals on network performance services to some extent, edge server resources are limited. Therefore, it is necessary to introduce a reasonable resource allocation model and task scheduling algorithm to manage edge resources comprehensively. The following section will discuss this in conjunction with... Figures 1-6 This invention describes a method for efficient configuration of multi-dimensional edge resources under 5G mobile edge computing (MEC).

[0051] like Figure 1 As shown in the figure, the efficient configuration method for multi-dimensional edge resources under 5G power MEC is applied to the edge server of the efficient configuration system for multi-dimensional edge resources under 5G power MEC. The efficient configuration system for multi-dimensional edge resources under 5G power MEC also includes terminals and transmission nodes. The method includes at least the following steps:

[0052] Step 101: Based on the resource requests sent by the terminal, determine the terminal's priority and the terminal's first degree of demand for edge server resources;

[0053] Step 102: Based on the transmission power consumption of the transmission node, determine the second degree of the terminal's demand for the transmission capability of the transmission node;

[0054] Step 103: Based on priority, first demand degree and second demand degree, determine the total revenue function of the multi-dimensional edge resource efficient allocation system under 5G power MEC;

[0055] Step 104: Determine the total revenue function as the objective function, and based on the objective function, optimization objective, and constraints, determine the resource allocation results for edge servers and transmission nodes.

[0056] Regarding step 101, it should be noted that the terminal in this embodiment of the invention is a smart terminal such as an IoT terminal. The resource requests sent by the terminal generally include requests for resources such as computing power, storage, and bandwidth. Since terminal devices have many different types of resource requirements, considering the urgency of events in real life, a terminal priority and a primary demand level for edge server resources are introduced. The terminal priority refers to the importance of each terminal within the edge server; more important terminals have higher priority, akin to a VIP level. The primary demand level for edge server resources is used to characterize the degree of demand each terminal has for different types of server resources. For example, if a terminal wants to perform a movie playback task, its request for bandwidth resources from the edge server is relatively high; therefore, the primary demand level for bandwidth resources for that terminal can be increased accordingly.

[0057] Regarding step 102, it should be noted that this embodiment of the invention only considers the downlink transmission portion. During the downlink transmission phase, the transmission power consumption is only related to the allocated power consumption; the higher the allocated power consumption, the better the transmission effect. Furthermore, the transmission power consumption required by the terminal varies depending on the task being performed. For example, downloading a movie requires a larger power allocation, in which case the second power requirement can be increased accordingly.

[0058] Priority, first demand level, and second demand level are all urgency factors for resource requests.

[0059] Regarding step 103, it should be noted that in the 5G power MEC multi-dimensional edge resource efficient allocation system, each end wants to maximize its own functionality during resource allocation. For example, edge servers want to allocate as many resources as possible, terminals want to request as many resources as possible, and transmission nodes want to allocate as much transmission power as possible for task execution. At the same time, each end cannot allocate resources without limit and will be constrained by other ends or the environment. Therefore, this embodiment, based on the above-mentioned relationship between edge servers, transmission nodes, and terminals, applies the concepts of cost and benefit from the financial field to the resource allocation task, establishing a benefit function for the 5G power MEC multi-dimensional edge resource efficient allocation system. The resources that each end wants to allocate in the 5G power MEC multi-dimensional edge resource efficient allocation system are presented in the form of a selling price, and the constraints or costs incurred by each end when allocating resources are presented in the form of a cost. The system benefit is the total system selling price minus the total system cost.

[0060] Regarding step 104, it should be noted that both the resources allocated by the server and the power consumption resources provided by the power grid are limited. Therefore, in addition to setting the objective function, it is also necessary to set optimization objectives and constraints. In this embodiment of the invention, the optimization objective is to maximize system benefits, and resource constraints and power grid power consumption constraints are used as constraints to solve the optimal solution of the objective function and obtain the resource configuration results of the edge server and transmission node.

[0061] This invention discloses a method for efficient multi-dimensional edge resource allocation under 5G power MEC. By introducing resource request urgency factors such as priority and first demand level, resources are allocated to terminals that maximize system utility, with system utility as the optimization objective. Furthermore, by introducing a power consumption request urgency factor such as second demand level, transmission power consumption is rationally allocated, with terminals having higher urgency factors receiving more transmission power. Considering that different terminals have varying degrees of urgency in their resource needs, resulting in different overall system utility, this invention dynamically adjusts transmission power consumption by solving an objective function to achieve downlink quality of resource services while maximizing system utility.

[0062] Understandably, based on priority, first demand level, and second demand level, the total revenue function of the multi-dimensional edge resource efficient allocation system under 5G power MEC is determined, including:

[0063] Based on priority and first demand degree, determine the first demand ceiling function for edge server resources of the terminal;

[0064] Based on the second demand degree, determine the second upper limit function of the terminal's demand for the transmission capacity of the transmission node;

[0065] Determine the terminal revenue function based on the first and second demand ceiling functions;

[0066] Based on the terminal revenue function, edge server revenue function, and transmission node revenue function, the total revenue function of the multi-dimensional edge resource efficient allocation system under 5G power MEC is determined.

[0067] It should be noted that the priority of terminal m is set to ω. m The terminal m's first demand degree for the d-th type of resource is set to r. md Therefore, the upper limit function of the terminal's first demand for edge server resources is expressed as: In this application, the terminal's resource demands conform to the trend of the ln() function. Specifically, after the edge server satisfies the terminal's requested resource demands, the terminal's user experience reaches a satisfactory level. At this point, allocating more resources will not further improve the terminal's experience. The natural logarithm function's upward trend gradually flattens out, conforming to this characteristic. Based on this, this embodiment of the invention also introduces priority and a first demand degree. Combining these two urgency factors, the improved first demand ceiling function can better characterize the highest price offered by the terminal for a given resource request when its demand is met. Therefore, this embodiment of the invention constructs a first demand ceiling function for edge server resources by the terminal based on priority and the first demand degree.

[0068] Similarly, the second requirement degree of terminal m for transmission power consumption is set to v. mp The expression for the upper limit function of the terminal's second demand on the transmission capacity of the transmission node is: U2 = ω m r mp ln(1+P m ), where P m This represents the transmission power consumption of terminal m. The second demand degree of this embodiment of the invention, the improved second demand upper limit function, can better characterize the highest price offered by the terminal when providing the best experience for backhauling to a certain terminal.

[0069] Furthermore, it should be noted that in addition to the total revenue mentioned above, the terminal also needs to calculate revenue based on the cost price. In the embodiments of the invention, the cost includes the terminal's cost when enjoying the service corresponding to the requested resources. And the cost of enjoying the transmission speed p t P m Therefore, the final terminal revenue function can be expressed as:

[0070]

[0071] Since there is more than one terminal in the system, and each terminal requests more than one type of resource, the terminal revenue function for terminal m needs to sum the revenue corresponding to all requested resource types. The unit price of selling the d-th type of resource to the terminal is p. d And the unit price of transmission power consumption is p t .

[0072] It is understandable that the process of determining the revenue functions of the remaining two ends in the 5G power MEC multi-dimensional edge resource efficient configuration system, namely the edge server revenue function and the transmission node revenue function, includes:

[0073] The revenue function of the edge server is determined based on resource requests, the unit resource cost of the edge server, and the unit resource price of the edge server.

[0074] The revenue function of a transmission node is determined based on transmission power consumption, unit power cost of the transmission node, and unit power price of the transmission node.

[0075] It should be noted that when constructing the edge server revenue function, the overall edge server model must first be considered. Since the edge server model consists of several edge servers, this embodiment of the invention uses "virtualization" to manage these physical resources uniformly. This model divides these resources into... There are several types, denoted as S = {S1, S2, ..., S...} D The upper limit for each type of resource is S. d , Terminal m's task resource request is α m ={α m1 ,α m2 ,...,α mD}, c d Let p be the unit cost price of the marginal resource of type d. d The unit price for selling the d-th type of resource to the end user.

[0076] Therefore, the benefits for edge server resources are:

[0077]

[0078] Additionally, it should be noted that the downlink transmission portion of the transmission node model is only related to the allocated power consumption. The higher the allocated power consumption, the better the transmission performance and the greater the benefit. Therefore, this embodiment of the invention introduces a transmission power consumption vector P = {P1, P2, ..., P3}. M This is reflected in the following statement. The unit cost of transmission power consumption is c. t The unit price of transmission power consumption is p. t .

[0079] Therefore, the benefit at the transmission node is:

[0080]

[0081] It is understandable that the optimization objective is to maximize the objective function, and the constraints include resource constraints and power grid power consumption constraints.

[0082] Resource constraints are used to limit the total number of resource requests sent by terminals in the 5G power MEC multi-dimensional edge resource efficiency configuration system to not exceed the upper limit of resources provided by the edge server.

[0083] The power grid power consumption constraint is used to limit the total transmission power consumption of the transmission nodes in the 5G power MEC multi-dimensional edge resource high-efficiency configuration system to not exceed the upper limit of power consumption resources provided by the power grid.

[0084] It should be noted that, to achieve the ultimate goal of maximizing the overall system benefit while satisfying the constraints, the resource constraints limit the total number of resource requests of all types from all terminals in the system to not exceed the edge resource limit, i.e.:

[0085]

[0086] It is understood that the power consumption constraint of the power grid is as shown in Equation 1:

[0087]

[0088] in, Here, m represents the total number of terminals in the 5G power MEC multi-dimensional edge resource high-efficiency configuration system, m is the terminal serial number, and d is the type serial number of the edge server resource. φ represents the total number of resource types of the edge servers. d α represents the energy consumption per unit of resource of type d. md For terminal m's task resource request for resource of type d, P m Let P be the transmission power vector of terminal m. max Total power consumption provided to the power grid.

[0089] Understandably, based on the objective function, optimization objective, and constraints, the resource configuration results for edge servers and transmission nodes are determined, including:

[0090] Based on the objective function, optimization objective, and constraints, the external approximation method is used to find the optimal solution of the objective function;

[0091] Based on the optimal solution, the resource configuration results for edge servers and transmission nodes are determined.

[0092] It should be noted that the final objective function is expressed as:

[0093]

[0094] The optimization problem, determined based on the objective function, optimization objective, and constraints, is as follows:

[0095]

[0096]

[0097]

[0098] Optionally, embodiments of the present invention employ the Polyblock external approximation method to solve for the optimal solution.

[0099] It should be noted that the objective function U(α,P) is a... The function of the element, i.e. Therefore, the expression for the total revenue corresponding to each terminal and demand can be written as U. i =ωr·ln(1+x)-cx, where the domain is [0,+∞), therefore U is in X=[x1 * x1 * x1 * ,…,x M*(D+1) * The maximum value is obtained at [].

[0100] Because Polyblocks possess an important property: the maximum value of a strictly increasing function defined on a Polyblock is always obtained at one of the Polyblock's vertices, the maximum value of a strictly increasing function can be efficiently obtained by traversing the vertices of the Polyblock.

[0101] The solution process includes at least the following:

[0102] Step 201: Establish initial blocks;

[0103] Based on the relationship between the extreme points and the feasible region boundary, the initial block division has the following two cases:

[0104] (1) The vertex is inside or on the boundary of the feasible region.

[0105] like Figure 2 As shown, since the function reaches its maximum value at the value point and satisfies the constraints, this point is the optimal solution.

[0106] (2) The vertex is outside the feasible region boundary.

[0107] Scenario 1: For example Figure 3 As shown, when the block formed by the extreme points completely contains the feasible region, the function satisfies the monotonicity condition within the feasible region. Therefore, we narrow the block boundary and let X1 = [S1…S1,……,S1… ... D …S D ,P max …P max If ], then that point is the initial block vertex.

[0108] Scenario 2: For example Figure 4 As shown, when the block formed by the extreme points contains part of the feasible region, according to the properties of extreme points, the function is monotonically increasing within the block formed by the extreme points X, and monotonically decreasing outside the block. Let X be any point in the feasible region outside the block. * The external function is monotonically decreasing on the S1 axis, therefore X2 > X3, and P max Since the axis is within the partitioned region, the function is monotonically increasing; therefore, X³ > X. * Therefore, for any point outside the block, X2 > X. * The condition is always true, meaning there is no optimal solution outside the block. Therefore, let X1 be the initial block vertex.

[0109] Step 202: Solve for the optimal solution by updating the blocks;

[0110] First, construct the "0-X" line using the "two-point method", as shown in Equation 8:

[0111]

[0112] Then, a set of constraints is formed. The feasible region boundary of dimension X can be obtained by substituting the "0-X" line into the feasible region boundary, as shown in Equation 9:

[0113] φ1k1*x * 1+φ2k1*x * 2+…+φ M*D k1*x * M*D +k1 formula 9;

[0114] *x * M*D+1 +…+k1*x * M*(D+1) ≤P max

[0115] k2*x * 1+k2*x * 2 + … + k2*x * M ≤S1

[0116] k3*x * M+1 +k3*x * M+2 +…+k3*x * M+M ≤S2

[0117]

[0118] The common solution for the scaling factor k, which satisfies all D+1 constraints in the above equation, is: k = min(k1, k1, k1…k D+1 Substituting the values ​​of the lines "0-X", we can find the intersection point: R = [x1, x1, x1, ..., x1]. M*(D+1) ].

[0119] Finally, calculate the function values ​​at the vertex and the intersection point. If the utility value at the vertex is infinitely close to the utility value at the intersection point, then that point is the optimal solution, and the algorithm ends; otherwise, ... Figure 5 As shown, a new vertex system is generated, and new vertices are selected by comparing the utility values ​​of the function. This process is repeated until the optimal solution is found.

[0120] This invention provides an optimal solution solution process based on Polyblock, including:

[0121] Step 201, Parameter initialization: n = 1, ε = 0.001;

[0122] Step 202: Construct the initial block vertices and establish the vertex coordinate system [Xa(n), XP(n)];

[0123] Step 203: Determine the relationship between the vertex coordinate system [Xa(n), XP(n)] and the feasible region R;

[0124] If the vertex coordinate system [Xa(n), XP(n)] is within the feasible region R, such as Figure 2 The point system is the optimal solution, let [MAXa = Xa(n), MAXP = XP(n)];

[0125] Otherwise, according to Figure 3 , Figure 4 The vertex coordinate system is redefined as shown.

[0126] Step 204: If the optimal solution has not yet been found, find the system of intersection points [Ra(n),RP(n)] between the "0-X" line and the feasible region;

[0127] Step 205: Substitute [Ra(n),RP(n)] and [Xa(n),XP(n)] into Equation 6 to calculate the system revenue, i.e., the utility values ​​U(Ra(n),RP(n)) and U(Xa(n),XP(n));

[0128] Step 206: Determine the relationship between |U(Ra(n),RP(n))-U(Xa(n),XP(n))| and ε;

[0129] If |U(Ra(n),RP(n))-U(Xa(n),XP(n))|<ε, then the point system [Ra(n),RP(n)] is the optimal solution; let n=0, and let [MAXa=Ra(n),MAXP=RP(n)];

[0130] Otherwise, let n = n + 1, from Figure 5 The construction shown Let each of the new point systems be a new system of points. Substitute these points into Equation 6 to find the point system [Xa(n),XP(n)] that has the greatest system utility.

[0131] It should be noted that the above parameter values ​​are determined based on the specific application environment, so that the values ​​of computing resources and power supply are close to reality. In the optimal solution solution process of this embodiment, the settings of each parameter include:

[0132] 1. Resource cap for each dimension S3 = [4.3 * 10 16 cycle, 10 4 GB, 10 4 GB], which correspond to the resource limits for CPU, RAM and hard disk respectively;

[0133] 2. Total power consumption P provided by the power grid max =2.7*10 9 J;

[0134] 3. Calculation cycle required per unit of data: 500 cycles / bit;

[0135] 4. The unit cost of 3D resources on the edge server is: c d =400 / GB;

[0136] 5. The unit cost of transmission power consumption is: c t =40 / GB, meaning that transmitting 1GB of bits consumes 40 units of cost;

[0137] 6. Unit energy consumption of 3D resources on edge servers: φ d= [90000, 80000, 70000] J / GB;

[0138] 7. Urgency factor ω for resource requests from each terminal m =[0.3,1.0], representing priority;

[0139] 8. Urgency factor r for various types of resource requests md =[0.3,1.0], representing the first degree of demand;

[0140] 9. Balance factor v of each terminal mp =[40,50], representing the second degree of demand.

[0141] In embodiments of the present invention, simulations can be performed on the above parameters to obtain the optimal solution under constraints, i.e., the system achieves maximum utility under the upper limits of power consumption and revenue. In embodiments of the present invention, due to the introduction of an urgency factor r for various types of resource requests... md The terminal can dynamically adjust the urgency factor r based on the urgency of its resource needs and the potential benefits. md Based on real-world usage of CPU, RAM, and hard drive, the resource urgency factors for these three dimensions are set as follows: r m1 =[0.5,0.7]、r m2 =[0.4,0.6]、r m3 =[0.3,0.5]. For example... Figure 6 As shown, it can be seen that when the system utility is maximized, the terminal's resource requirements for different dimensions are not the same, which better reflects the demand situation in the actual environment.

[0142] according to Figure 6 The resource usage data for each dimension is shown. This embodiment of the invention is compared with a distributed mobile edge computing scenario in the prior art. Figure 7 As shown, this method (i.e. Figure 7 The average resource utilization rate of Option 1 is significantly higher than that of the existing technology (Option 2).

[0143] The source of this prior art is: Li Z, Qin J, Wen W. Delay-guaranteed Task Allocation in Mobile Edge Computing with Balanced Resource Utilization[C] / / Proceedings of the 2020 4th International Conference on High Performance Compilation, Computing and Communications.2020:35-41.

[0144] This embodiment of the invention also adjusts the resource request urgency factor ω of each terminal. m To allocate transmission power consumption, let ω m They are distributed in the following intervals: ω m =[0.1,0.2]、φ m =[0.3,0.4]、ω m =[0.5,0.6]. For example... Figure 8 As shown, the transmission power consumption is related to the urgency factor ω of each terminal's resource request. m Closely related, the emergency factor ω in different intervals m The transmission power consumption allocated to each terminal is different. If a terminal has an urgent need for resources, the terminal urgency factor ω can be increased. m The system allocates more transmission power to provide services, and then seeks a balance between different resource requests under the constraints of power consumption and the benefits gained, in order to obtain the maximum utility of the system.

[0145] The embodiments of the present invention introduce ω m and r md To dynamically adjust the resource requests of each terminal to different dimensions, at the edge resource S d and total power consumption P max Under certain circumstances, embodiments of the present invention provide four schemes to compare system benefits.

[0146] Option 1: This model architecture dynamically allocates resource requests to each terminal based on the urgency factor;

[0147] Option 2: Using this model architecture, edge resources are equally allocated to each terminal.

[0148] Option 3: Instead of using this model, directly value the edge resources and sell them directly to the end user.

[0149] Option 4: Evaluate the system utility using a model scheme based on a Stackelberg game framework.

[0150] The source of Scheme 4 is: Chen Y, Li Z, Yang B, et al. A Stackelberg game approach to multiple resource allocation and pricing in mobile edge computing[J]. FutureGeneration Computer Systems, 2020, 108: 273-287.

[0151] like Figure 9 As shown, when the urgency factor is relatively small, the terminal's demand for resources is not urgent, and the utility created by responding to resource requests is also relatively limited. Therefore, the embodiments of the present invention do not offer any advantage, and the system utility of Scheme 4 remains high. However, as the urgency factor of resource demand increases, responding to these resource requests and providing them with greater transmission power will yield greater utility, ultimately surpassing the system utility of other schemes, thus demonstrating the advantages of the embodiments of the present invention.

[0152] The following describes the efficient configuration system for multi-dimensional edge resources under 5G power MEC provided by this invention. The efficient configuration system for multi-dimensional edge resources under 5G power MEC described below can be referred to in correspondence with the efficient configuration method for multi-dimensional edge resources under 5G power MEC described above. Figure 10 As shown in the figure, this invention discloses a multi-dimensional edge resource high-efficiency configuration system under 5G power MEC, including: a terminal, a transmission node, and an edge server;

[0153] A terminal is used to send resource requests to a server;

[0154] Transmission nodes are used to enable communication between terminals and edge servers;

[0155] Edge servers are used to determine the priority of terminals and their primary demand for edge server resources based on resource requests sent by terminals; and to determine the secondary demand of terminals for transmission capabilities based on the transmission power consumption of transmission nodes.

[0156] The edge server is also used to determine the total revenue function of the multi-dimensional edge resource efficiency configuration system under 5G power MEC based on priority, first demand degree and second demand degree; the total revenue function is determined as the objective function, and the resource configuration results of the edge server and transmission node are determined based on the objective function, optimization objective and constraints.

[0157] It should be noted that the edge resource allocation system consists of three parts: terminals, transmission nodes, and edge servers. The terminals are the senders of edge resource requests and also responsible for receiving the computation results from the edge servers. The transmission nodes, connected directly via fiber optic cables, implement the system's centralized communication, caching, and computation functions; in this embodiment, the transmission nodes are primarily responsible for transmitting data. The computer resources in each edge server are managed uniformly through virtualization. The electrical energy required for system computation and communication is supplied by the power grid.

[0158] In this system, the resources of the edge server mainly consist of basic hardware resources, including CPU (computing resources), hard disk (storage resources), and network interface card (NIC) (network resources). The edge server comprises a main control board and a baseband board. The main control board is responsible for processing signaling from terminals and transmission nodes and interconnecting with them, while the baseband board is responsible for baseband processing such as data computation, encoding, and modulation, and then sends the processed data to the terminals through the transmission nodes. This enables real-time interaction between the terminals and the edge resource platform, ultimately realizing the vision of "number of terminals, edge computing."

[0159] This invention discloses a multi-dimensional edge resource high-efficiency configuration system under 5G power MEC, which employs a 5G power grid edge resource allocation model to rationally allocate computing resources to edge servers, thereby improving the overall system utility. Considering that different terminals have varying degrees of urgency in their resource needs, resulting in different overall system utility, an urgency factor for each terminal across various dimensions is introduced to improve the overall Quality of Service (QoS) for users. Furthermore, transmission power consumption is dynamically adjusted based on the urgency factor to achieve downlink quality of resource services while maximizing system utility.

[0160] It is understood that the total revenue function for determining the multi-dimensional edge resource efficient allocation system under 5G power MEC, based on the priority, the first demand degree, and the second demand degree, includes:

[0161] Based on the priority and the first demand level, a first upper limit function for the terminal's demand for the edge server resources is determined;

[0162] Based on the second demand level, determine the second upper limit function of the terminal's demand for the transmission capacity of the transmission node;

[0163] Based on the first demand ceiling function and the second demand ceiling function, determine the terminal revenue function;

[0164] Based on the terminal revenue function, edge server revenue function, and transmission node revenue function, the total revenue function of the multi-dimensional edge resource high-efficiency configuration system under 5G power MEC is determined.

[0165] It is understood that the process of determining the edge server revenue function and the transmission node revenue function includes:

[0166] Based on the resource request, the unit resource cost of the edge server, and the unit resource price of the edge server, determine the revenue function of the edge server;

[0167] The revenue function of the transmission node is determined based on the transmission power consumption, the unit power consumption cost of the transmission node, and the unit power consumption price of the transmission node.

[0168] It is understood that the optimization objective is to maximize the objective function, and the constraints include resource constraints and power grid power consumption constraints.

[0169] The resource constraint condition is used to limit the total number of resource requests sent by the terminals of the 5G power MEC multi-dimensional edge resource high-efficiency configuration system to not exceed the resource limit provided by the edge server;

[0170] The power grid power consumption constraint is used to limit the total transmission power consumption of the transmission nodes in the 5G power MEC multi-dimensional edge resource high-efficiency configuration system to not exceed the upper limit of power consumption resources provided by the power grid.

[0171] It is understood that the power consumption constraint of the power grid is as shown in Equation 1:

[0172]

[0173] in, Here, m represents the total number of terminals in the 5G power MEC multi-dimensional edge resource high-efficiency configuration system, m is the terminal serial number, and d is the type serial number of the edge server resource. φ represents the total number of resource types of the edge servers. d α represents the energy consumption per unit of resource of type d. md For terminal m's task resource request for resource of type d, P m Let P be the transmission power vector of terminal m. max Total power consumption provided to the power grid.

[0174] It is understood that determining the resource configuration results of the edge server and the transmission node based on the objective function, optimization objective, and constraints includes:

[0175] Based on the objective function, the optimization objective, and the constraints, the optimal solution of the objective function is obtained using the external approximation method.

[0176] Based on the optimal solution, the resource configuration results of the edge server and the transmission node are determined.

[0177] Figure 11 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 11 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, communication interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions from the memory 830 to execute a method for efficient configuration of multi-dimensional edge resources under 5G power MEC. This method is applied to the edge server of a multi-dimensional edge resource efficient configuration system under 5G power MEC. The 5G power MEC multi-dimensional edge resource efficient configuration system also includes terminals and transmission nodes. The method includes:

[0178] Based on the resource requests sent by the terminal, the priority of the terminal and the terminal's first demand for edge server resources are determined.

[0179] Based on the transmission power consumption of the transmission node, determine the second degree of terminal demand for the transmission capability of the transmission node.

[0180] Based on priority, first demand degree and second demand degree, the total revenue function of the multi-dimensional edge resource high-efficiency allocation system under 5G power MEC is determined;

[0181] The total revenue function is determined as the objective function, and based on the objective function, optimization objective, and constraints, the resource allocation results for edge servers and transmission nodes are determined.

[0182] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0183] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the efficient configuration method for multi-dimensional edge resources under 5G power MEC provided by the above methods. The method is applied to the edge server of the efficient configuration system for multi-dimensional edge resources under 5G power MEC. The efficient configuration system for multi-dimensional edge resources under 5G power MEC also includes a terminal and a transmission node. The method includes:

[0184] Based on the resource requests sent by the terminal, the priority of the terminal and the terminal's first demand for edge server resources are determined.

[0185] Based on the transmission power consumption of the transmission node, determine the second degree of terminal demand for the transmission capability of the transmission node.

[0186] Based on priority, first demand degree and second demand degree, the total revenue function of the multi-dimensional edge resource high-efficiency allocation system under 5G power MEC is determined;

[0187] The total revenue function is determined as the objective function, and based on the objective function, optimization objective, and constraints, the resource allocation results for edge servers and transmission nodes are determined.

[0188] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform the efficient configuration method for multi-dimensional edge resources under 5G power MEC provided by the above methods. The method is applied to the edge server of the efficient configuration system for multi-dimensional edge resources under 5G power MEC. The efficient configuration system for multi-dimensional edge resources under 5G power MEC also includes terminals and transmission nodes. The method includes:

[0189] Based on the resource requests sent by the terminal, the priority of the terminal and the terminal's first demand for edge server resources are determined.

[0190] Based on the transmission power consumption of the transmission node, determine the second degree of terminal demand for the transmission capability of the transmission node.

[0191] Based on priority, first demand degree and second demand degree, the total revenue function of the multi-dimensional edge resource high-efficiency allocation system under 5G power MEC is determined;

[0192] The total revenue function is determined as the objective function, and based on the objective function, optimization objective, and constraints, the resource allocation results for edge servers and transmission nodes are determined.

[0193] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0194] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0195] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for efficient configuration of multi-dimensional edge resources under 5G power MEC, characterized in that, The method is applied to an edge server of a multi-dimensional edge resource efficient configuration system under a 5G power mobile edge computing (MEC), and the multi-dimensional edge resource efficient configuration system under the 5G power MEC further includes a terminal and a transmission node, and the method includes: determining a priority of the terminal and a first demand degree of the terminal to edge server resources based on a resource request sent by the terminal; determining a second demand degree of the terminal to transmission capacity of the transmission node based on transmission power consumption of the transmission node; determining a total revenue function of the multi-dimensional edge resource efficient configuration system under the 5G power MEC based on the priority, the first demand degree and the second demand degree; determining a resource configuration result of the edge server and the transmission node based on the total revenue function as a target function, an optimization target and a constraint condition; wherein the target function is expressed as the total number of terminals of the multi-dimensional edge resource efficient configuration system under the 5G power MEC, m is the terminal serial number, d is the type serial number of the edge server resource, the total number of resource types of the edge server, ω m indicates the priority set by the terminal m, r md indicates the first demand degree setting of the terminal m to the dth type of resource, α md is the task resource request of the terminal m to the dth type of resource, c d is the unit cost price of the dth type of edge resource, v mp indicates the second demand degree setting of the terminal m to the transmission power consumption, P m indicates the transmission power consumption of the terminal m, c t indicates the unit cost price of the transmission power consumption; The optimization objective is to maximize an objective function; the constraint conditions include resource constraint conditions and power grid power consumption constraint conditions; the resource constraint conditions are represented as The power grid power consumption constraint conditions are represented as The total number of terminals of the 5G power MEC multi-dimensional edge resource efficient configuration system, m is the terminal serial number, d is the type serial number of the edge server resource, S is the total number of resource types of the edge server d φ represents the upper limit of each type of resource d α is the unit energy consumption of the dth type of resource md P is the task resource request of terminal m to the dth type of resource m P is the transmission power consumption vector of terminal m max P is the total transmission power consumption provided by the power grid.

2. The method of claim 1, wherein the method is a method of multi-dimensional edge resource efficient configuration under 5G power MEC, characterized in that, The determination of the total revenue function of the multi-dimensional edge resource efficient configuration system under the 5G power MEC based on the priority, the first demand degree and the second demand degree includes: determining a first demand upper limit function of the terminal to the edge server resources according to the priority and the first demand degree; determining a second demand upper limit function of the terminal to the transmission capacity of the transmission node according to the second demand degree; determining a terminal revenue function based on the first demand upper limit function and the second demand upper limit function; determining the total revenue function of the multi-dimensional edge resource efficient configuration system under the 5G power MEC according to the terminal revenue function, an edge server revenue function and a transmission node revenue function.

3. The method of claim 2, wherein the method is a method of multi-dimensional edge resource efficient configuration under 5G power MEC, characterized in that, The determination of the edge server revenue function and the transmission node revenue function includes: determining the edge server revenue function based on the resource request, a unit resource cost of the edge server and a unit resource price of the edge server; determining the transmission node revenue function based on the transmission power consumption, a unit power consumption cost of the transmission node and a unit power consumption price of the transmission node.

4. The method of claim 1 to 3, wherein, The optimization target is to maximize the target function, and the constraint condition includes a resource constraint condition and a power grid power consumption constraint condition; The resource constraint condition is used to limit that a total of resource requests sent by terminals of the multi-dimensional edge resource efficient configuration system under the 5G power MEC does not exceed an upper limit of resources provided by the edge server; The power grid power consumption constraint condition is used to limit that a total of transmission power consumptions of transmission nodes of the multi-dimensional edge resource efficient configuration system under the 5G power MEC does not exceed an upper limit of power consumption resources provided by a power grid.

5. The method of claim 4, wherein the method is characterized by, The power grid power consumption constraint condition is shown in formula 1: wherein, is the total number of terminals of the 5G power MEC multi-dimensional edge resource efficient configuration system, m is the terminal serial number, d is the type serial number of the edge server resource, is the total number of resource types of the edge server, d is the unit energy consumption of the dth type of resource, α md is the task resource request of terminal m to the dth type of resource, P m is the transmission power consumption vector of terminal m, P max is the total transmission power consumption provided by the power grid.

6. The method of claim 1 to 3, wherein, The determination of the resource configuration result of the edge server and the transmission node based on the target function, the optimization target and the constraint condition includes: solving an optimal solution of the target function by using an outer approximation method based on the target function, the optimization target and the constraint condition; determining the resource configuration result of the edge server and the transmission node based on the optimal solution.

7. A multi-dimensional edge resource efficient configuration system under 5G power MEC, characterized in that, It includes: a terminal, a transmission node and an edge server; the terminal is used to send a resource request to the server; The transmission node is configured to realize communication between the terminal and the edge server. The edge server is configured to determine a priority of the terminal and a first demand degree of the terminal for edge server resources based on the resource request sent by the terminal, and determine a second demand degree of the terminal for transmission node transmission capability based on transmission power consumption of the transmission node. The edge server is further configured to determine a total revenue function of a multi-dimensional edge resource efficient configuration system under 5G power MEC based on the priority, the first demand degree and the second demand degree, determine the total revenue function as an objective function, and determine resource configuration results of the edge server and the transmission node based on the objective function, an optimization target and a constraint condition. The objective function is expressed as the total number of terminals of the multi-dimensional edge resource efficient configuration system under the 5G power MEC, m is the terminal serial number, d is the type serial number of the edge server resource, the total number of resource types of the edge server, m indicates the priority set by the terminal m, r md indicates the first demand degree setting of the terminal m to the dth type of resource, α md is the task resource request of the terminal m to the dth type of resource, c d is the unit cost price of the dth type of edge resource, v mp indicates the second demand degree setting of the terminal m to the transmission power consumption, P m indicates the transmission power consumption of the terminal m, c t indicates the unit cost price of the transmission power consumption; The optimization objective is to maximize an objective function; the constraint conditions include resource constraint conditions and power grid power consumption constraint conditions; the resource constraint conditions are represented as The power grid power consumption constraint conditions are represented as The total number of terminals of the 5G power MEC multi-dimensional edge resource efficient configuration system, m is the terminal serial number, d is the type serial number of the edge server resource, S is the total number of resource types of the edge server d φ represents the upper limit of each type of resource d α is the unit energy consumption of the dth type of resource md P is the task resource request of terminal m to the dth type of resource m P is the transmission power consumption vector of terminal m max P is the total transmission power consumption provided by the power grid.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the 5G power MEC multi-dimensional edge resource efficient configuration method according to any one of claims 1 to 6 when executing the program. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the 5G power MEC multi-dimensional edge resource efficient configuration method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the 5G power MEC multi-dimensional edge resource efficient configuration method according to any one of claims 1 to 6.

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