Low earth orbit satellite edge computing resource allocation and unloading method

Abstract

The invention provides a low-earth-orbit satellite edge computing resource allocation and unloading method, which relates to the technical field of wireless communication networks and comprises the following steps: S1, acquiring bandwidth resources and computing resources according to a computing task request sent to a low-earth-orbit satellite by a ground terminal, and establishing a low-earth-orbit satellite communication model; s2, determining local calculation time delay and transmission energy consumption based on a low earth orbit satellite communication model; s3, establishing a resource allocation and unloading model according to local calculation time delay and transmission energy consumption; s4, updating parameters of the resource allocation and unloading model by using an Adam optimization algorithm to obtain an optimal solution; s5, performing resource allocation and unloading on the satellite bandwidth resources and the computing resources according to the optimal solution; according to the method, task unloading and resource allocation can be effectively completed, and the system time delay is remarkably reduced.

Description

Technical Field

[0001] This invention relates to the field of wireless communication network technology, and more specifically, to a method for allocating and offloading edge computing resources for low-Earth orbit satellites. Background Technology

[0002] Compared to traditional terrestrial wireless communication networks, low-Earth orbit (LEO) satellite networks offer advantages such as wide coverage and low latency, providing reliable communication connections for remote areas and mobile users. Edge computing, a distributed computing model, moves computing resources and services from traditional cloud data centers to edge devices or nodes closer to users, reducing data transmission latency, improving computing efficiency, and better meeting real-time and privacy requirements. Combining LEO satellite networks with edge computing leverages the strengths of both, enabling faster processing and analysis of satellite data, reducing reliance on terrestrial data centers, and providing stronger support for various applications such as disaster response, agricultural monitoring, and environmental monitoring.

[0003] With the development of edge computing in low-Earth orbit (LEO) satellite networks, the joint optimization of computation offloading and resource allocation has become a research hotspot, presenting considerable complexity and challenges. Traditional methods often only consider the scenario where a single LEO satellite receives and performs computation on a single ground terminal device's offloading task, or where a single ground terminal device offloads the task to multiple LEO satellites and ground base stations. These methods fail to consider the cooperation between multiple LEO satellites and the coverage time issues during LEO satellite movement, resulting in excessively high system latency. Summary of the Invention

[0004] The purpose of this invention is to provide a method for allocating and offloading edge computing resources for low-Earth orbit satellites, which can effectively complete task offloading and resource allocation, and significantly reduce system time latency.

[0005] The technical solution of this invention is as follows:

[0006] In a first aspect, this application provides a method for allocating and offloading edge computing resources for low-Earth orbit satellites, which includes the following steps:

[0007] S1. Obtain bandwidth and computing resources based on the computing task request sent by the ground terminal to the low-orbit satellite, and establish a low-orbit satellite communication model.

[0008] S2. Determine local computation latency and transmission power consumption based on a low-Earth orbit satellite communication model;

[0009] S3. Establish a resource allocation and offloading model based on local computing latency and transmission energy consumption;

[0010] S4. Use the Adam optimization algorithm to update the parameters of the resource allocation and unloading model to obtain the optimal solution;

[0011] S5. Allocate and offload satellite bandwidth and computing resources based on the optimal solution.

[0012] Furthermore, the expression for the above low-Earth orbit satellite communication model is:

[0013]

[0014] In the formula, r n,m Let μ be the transmission rate of the link from the ground terminal to the satellite, and B be the uplink bandwidth resource allocated to the ground terminal user n. n,m p is the channel bandwidth allocated by satellite m to ground terminal user n. n For the transmission power of ground terminal user n, g n,m For channel gain, σ 2 For the system, white Gaussian noise, I n,m This represents the interference noise power of other terminals.

[0015] Furthermore, in step S2, the calculation formulas for the local computation latency and transmission power consumption include:

[0016]

[0017] In the formula, For local computation latency, c n The total number of CPUs required to complete the computational task. For the computing resources of ground terminal user n, Let κ be the transmission energy consumption of ground terminal user n, and κ be a constant.

[0018] Furthermore, in step S3, the calculation process for establishing the resource allocation and unloading model includes:

[0019] Task unloading delay:

[0020]

[0021] Satellite computing power consumption:

[0022]

[0023] Satellite transmission power consumption:

[0024]

[0025] Local computing cost:

[0026]

[0027] Calculate the total unloading delay and total energy consumption:

[0028]

[0029] Calculate unloading costs:

[0030]

[0031] In the formula, For the communication delay of transmitting mission data on the ground-to-satellite wireless link, d n r represents the data size of the task. n,m Let m be the transmission rate of the link from ground terminal user n to satellite m. Let d be the propagation delay between satellite m and ground terminal user n. n,m Let c be the straight-line distance from ground terminal user n to satellite m, and c be the speed of light. For the computational delay of the mission on the satellite, c n The total number of CPUs required to complete the computational task, f n,m The computing resources allocated to ground terminal user n for satellite m. The energy consumption for computation on the satellite is given by ε, where ε is the energy factor. p represents the transmission energy consumption for transmitting computing tasks from ground terminal user n to satellite m. n Let n be the transmission power of the ground terminal user. For local calculation costs, For local calculation latency, Let n be the transmission energy consumption of the ground terminal user. To unload the total latency, To offload the total energy consumption, The unloading cost is defined as the weighted sum of task completion delay and energy consumption, ω. T ω represents the weight of delay in the system cost function. E This represents the weight of energy consumption in the system cost function.

[0032] Furthermore, in step S4, the calculation formula for updating the parameters of the resource allocation and unloading model using the Adam optimization algorithm to obtain the optimal solution includes:

[0033]

[0034] In the formula, Q(k,a,s) represents the system utility, N represents the total number of ground terminal users, M represents the total number of satellites, n represents the nth ground terminal user, m represents the mth satellite, s represents the satellite's bandwidth and computing resources, and a represents the system utility. nm For the unloading decision of ground terminal user n, The total cost of unloading, For local calculation costs, B n,mf is the channel bandwidth allocated by satellite m to ground terminal user n. n,m Let C1 be the computing resources allocated by satellite m to ground terminal user n, and C2 be the constraint that each terminal can select at most one satellite. To unload the total latency, T cover C3 represents the satellite coverage area, and C4 represents the latency constraint for computing tasks on the satellite. For local computation latency, t nmax C4 represents the maximum permissible delay for the mission, and C4 represents the bandwidth resource constraint for low-Earth orbit satellites. C5 represents the total bandwidth of satellite m, and C5 represents the resource constraints for low-Earth orbit satellites. C6 represents the total computing resources for satellite m, where C6 is the constraint that allocated bandwidth and computing resources cannot be negative, and C7 represents the maximum number of connections each satellite can access. max Terminal constraints.

[0035] Secondly, this application provides an electronic device, comprising:

[0036] Memory, used to store one or more programs;

[0037] processor;

[0038] When one or more of the above programs are executed by the above processor, a method for allocating and offloading low-orbit satellite edge computing resources as described in any of the first aspects above is implemented.

[0039] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for allocating and offloading low-orbit satellite edge computing resources as described in any of the first aspects above.

[0040] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0041] This invention discloses a method for resource allocation and offloading of low-Earth orbit (LEO) satellite edge computing. It establishes a LEO satellite communication model by acquiring bandwidth and computing resources, and establishes a resource allocation and offloading model by determining local computing latency and transmission energy consumption. This enables joint offloading of multiple satellites. Finally, the Adam optimization algorithm is used to update the parameters of the resource allocation and offloading model to obtain the optimal solution. This optimizes the overall system latency for multiple ground terminal users. Considering satellite mobility, it achieves task offloading in a time-slot manner. Under limited resource constraints, it considers different parameter differences, cooperation between multiple LEO satellites, and coverage time issues under LEO satellite movement, minimizing the final system time processing latency and effectively completing task offloading and resource allocation. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating the steps of a low-orbit satellite edge computing resource allocation and offloading method according to the present invention.

[0044] Figure 2 This is a schematic structural block diagram of an electronic device according to an embodiment of the present invention.

[0045] Icons: 101, memory; 102, processor; 103, communication interface. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0047] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0048] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0049] It should be noted that, in this document, the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0050] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the various embodiments and features described below can be combined with each other.

[0051] Example 1

[0052] Please see Figure 1 , Figure 1 The diagram shows the steps of a method for allocating and offloading edge computing resources for low-orbit satellites according to an embodiment of this application.

[0053] This application provides a method for allocating and offloading edge computing resources for low-Earth orbit satellites, which includes the following steps:

[0054] S1. Obtain bandwidth and computing resources based on the computing task request sent by the ground terminal to the low-orbit satellite, and establish a low-orbit satellite communication model.

[0055] S2. Determine local computation latency and transmission power consumption based on a low-Earth orbit satellite communication model;

[0056] S3. Establish a resource allocation and offloading model based on local computing latency and transmission energy consumption;

[0057] S4. Use the Adam optimization algorithm to update the parameters of the resource allocation and unloading model to obtain the optimal solution;

[0058] S5. Allocate and offload satellite bandwidth and computing resources based on the optimal solution.

[0059] As a preferred implementation, the expression for the low-Earth orbit satellite communication model is:

[0060]

[0061] In the formula, r n,m Let μ be the transmission rate of the link from the ground terminal to the satellite, and B be the uplink bandwidth resource allocated to the ground terminal user n. n,m p is the channel bandwidth allocated by satellite m to ground terminal user n. n For the transmission power of ground terminal user n, g n,m For channel gain, σ 2 For the system, white Gaussian noise, I n,m This represents the interference noise power of other terminals.

[0062] In a preferred embodiment, step S2 includes the following formulas for calculating local computation latency and transmission power consumption:

[0063]

[0064] In the formula, For local computation latency, cn The total number of CPUs required to complete the computational task. For the computing resources of ground terminal user n, Let κ be the transmission energy consumption of ground terminal user n, and κ be a constant.

[0065] As a preferred implementation, step S3, the calculation process for establishing the resource allocation and unloading model includes:

[0066] Task unloading delay:

[0067]

[0068] Satellite computing power consumption:

[0069]

[0070] Satellite transmission power consumption:

[0071]

[0072] Local computing cost:

[0073]

[0074] Calculate the total unloading delay and total energy consumption:

[0075]

[0076] Calculate unloading costs:

[0077]

[0078] In the formula, For the communication delay of transmitting mission data on the ground-to-satellite wireless link, d n r represents the data size of the task. n,m Let m be the transmission rate of the link from ground terminal user n to satellite m. Let d be the propagation delay between satellite m and ground terminal user n. n,m Let c be the straight-line distance from ground terminal user n to satellite m, and c be the speed of light. For the computational delay of the mission on the satellite, c n The total number of CPUs required to complete the computational task, f n,m The computing resources allocated to ground terminal user n for satellite m. The energy consumption for computation on the satellite is given by ε, where ε is the energy factor. p represents the transmission energy consumption for transmitting computing tasks from ground terminal user n to satellite m. n Let n be the transmission power of the ground terminal user. For local calculation costs, For local calculation latency, Let n be the transmission energy consumption of the ground terminal user. To unload the total latency, To offload the total energy consumption, The unloading cost is defined as the weighted sum of task completion delay and energy consumption, ω. T ω represents the weight of delay in the system cost function. E This represents the weight of energy consumption in the system cost function.

[0079] In a preferred implementation, step S4, the calculation formula for updating the parameters of the resource allocation and unloading model using the Adam optimization algorithm to obtain the optimal solution includes:

[0080]

[0081] In the formula, Q(k,a,s) represents the system utility, N represents the total number of ground terminal users, M represents the total number of satellites, n represents the nth ground terminal user, m represents the mth satellite, s represents the satellite's bandwidth and computing resources, and a represents the system utility. nm For the unloading decision of ground terminal user n, The total cost of unloading, For local calculation costs, B n,m f is the channel bandwidth allocated by satellite m to ground terminal user n. n,m Let C1 be the computing resources allocated by satellite m to ground terminal user n, and C2 be the constraint that each terminal can select at most one satellite. To unload the total latency, T cover C3 represents the satellite coverage area, and C4 represents the latency constraint for computing tasks on the satellite. For local computation latency, t nmax C4 represents the maximum permissible delay for the mission, and C4 represents the bandwidth resource constraint for low-Earth orbit satellites. C5 represents the total bandwidth of satellite m, and C5 represents the resource constraints for low-Earth orbit satellites. C6 represents the total computing resources for satellite m, where C6 is the constraint that allocated bandwidth and computing resources cannot be negative, and C7 represents the maximum number of connections each satellite can access. max Terminal constraints.

[0082] Example 2

[0083] Please see Figure 2 , Figure 2 This is a schematic structural block diagram of an electronic device provided in an embodiment of this application.

[0084] An electronic device includes a memory 101, a processor 102, and a communication interface 103. The memory 101, processor 102, and communication interface 103 are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules. The processor 102 executes the software programs and modules stored in the memory 101 to perform various functional applications and data processing. The communication interface 103 can be used for signaling or data communication with other node devices.

[0085] The memory 101 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0086] The processor 102 can be an integrated circuit chip with signal processing capabilities. The processor 102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0087] It is understood that the structure shown in the figure is for illustrative purposes only. A method for allocating and offloading edge computing resources for low-Earth orbit satellites may include more or fewer components than shown in the figure, or have a different configuration. The components shown in the figure may be implemented in hardware, software, or a combination thereof.

[0088] In the embodiments provided in this application, it should be understood that the disclosed methods can also be implemented in other ways. The embodiments described above are merely illustrative. For example, the flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0089] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0090] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 described in the various embodiments of this application. 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.

[0091] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0092] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for allocating and offloading edge computing resources for low-Earth orbit satellites, characterized in that, Includes the following steps: S1. Obtain bandwidth and computing resources based on the computing task request sent by the ground terminal to the low-orbit satellite, and establish a low-orbit satellite communication model. S2. Determine local computation latency and transmission power consumption based on a low-Earth orbit satellite communication model; S3. Establish a resource allocation and offloading model based on local computing latency and transmission energy consumption; S4. Use the Adam optimization algorithm to update the parameters of the resource allocation and unloading model to obtain the optimal solution; S5. Allocate and offload satellite bandwidth and computing resources based on the optimal solution.

2. The method for allocating and offloading edge computing resources for low-Earth orbit satellites as described in claim 1, characterized in that, The expression for the low-Earth orbit satellite communication model is: In the formula, r n,m Let μ be the transmission rate of the link from the ground terminal to the satellite, and B be the uplink bandwidth resource allocated to the ground terminal user n. n,m p is the channel bandwidth allocated by satellite m to ground terminal user n. n For the transmission power of ground terminal user n, g n,m For channel gain, σ 2 For the system, white Gaussian noise, I n,m This represents the interference noise power of other terminals.

3. The method for allocating and offloading edge computing resources for low-Earth orbit satellites as described in claim 1, characterized in that, In step S2, the calculation formulas for local computation latency and transmission power consumption are... include: In the formula, For local computation latency, c n The total number of CPUs required to complete the computational task. For the computing resources of ground terminal user n, Let κ be the transmission energy consumption of ground terminal user n, and κ be a constant.

4. The method for allocating and offloading edge computing resources for low-Earth orbit satellites as described in claim 1, characterized in that, In step S3, the calculation process for establishing the resource allocation and unloading model includes: Task unloading delay: Satellite computing power consumption: Satellite transmission power consumption: Local computing cost: Calculate the total unloading delay and total energy consumption: Calculate unloading costs: In the formula, For the communication delay of transmitting mission data on the ground-to-satellite wireless link, d n r represents the data size of the task. n,m Let m be the transmission rate of the link from ground terminal user n to satellite m. Let d be the propagation delay between satellite m and ground terminal user n. n,m Let c be the straight-line distance from ground terminal user n to satellite m, and c be the speed of light. For the computational latency of the mission on the satellite, c n The total number of CPUs required to complete the computational task, f n,m The computing resources allocated to ground terminal user n for satellite m. The energy consumption for computation on the satellite is given by ε, where ε is the energy factor. p represents the transmission energy consumption for transmitting computing tasks from ground terminal user n to satellite m. n Let n be the transmission power of the ground terminal user. For local calculation costs, For local calculation latency, Let n be the transmission energy consumption of the ground terminal user. To unload the total latency, To offload the total energy consumption, The unloading cost is defined as the weighted sum of task completion delay and energy consumption, ω. T ω represents the weight of delay in the system cost function. E This represents the weight of energy consumption in the system cost function.

5. The method for allocating and offloading edge computing resources for low-Earth orbit satellites as described in claim 1, characterized in that, In step S4, the calculation formula for updating the parameters of the resource allocation and unloading model using the Adam optimization algorithm to obtain the optimal solution includes: In the formula, Q(k,a,s) represents the system utility, N represents the total number of ground terminal users, M represents the total number of satellites, n represents the nth ground terminal user, m represents the mth satellite, s represents the satellite's bandwidth and computing resources, and a represents the system utility. nm For the unloading decision of ground terminal user n, Total unloading cost, For local calculation costs, B n,m f is the channel bandwidth allocated by satellite m to ground terminal user n. n,m Let C1 be the computing resources allocated by satellite m to ground terminal user n, and C2 be the constraint that each terminal can select at most one satellite. To unload the total latency, T cover C3 represents the satellite coverage area, and C4 represents the latency constraint for computing tasks on the satellite. For local calculation latency, t nmax C4 represents the maximum permissible delay for the mission, and C4 represents the bandwidth resource constraint for low-Earth orbit satellites. C5 represents the total bandwidth of satellite m, and C5 represents the resource constraints for low-Earth orbit satellites. C6 represents the total computing resources for satellite m, where C6 is the constraint that allocated bandwidth and computing resources cannot be negative, and C7 represents the maximum number of connections each satellite can access. max Terminal constraints.

6. An electronic device, characterized in that, include: Memory, used to store one or more programs; processor; When the processor executes the one or more programs, it implements a method for allocating and offloading low-orbit satellite edge computing resources as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for allocating and offloading edge computing resources for low-orbit satellites as described in any one of claims 1-5.

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