Hardware resource allocation method and device applied to edge server

Abstract

The embodiment of the invention provides a hardware resource allocation method and device applied to an edge server, and belongs to the field of application of a virtualization technology in factory production operation, and the method comprises the steps: carrying out the statistics of the total amount of CPU and memory resources of all edge server nodes in a current target region range; the method comprises the following steps: acquiring hardware resource occupation data of a Docker service, a NaaOS service and a UniKraft service; solving a pre-constructed resource allocation objective function by using the hardware resource occupation data, determining a corresponding objective function coefficient, and generating a current hardware resource allocation function; and according to the hardware resource allocation function, hardware resources occupied by the Docker service, the NaaOS service and the UniKraft service are reallocated. Therefore, the optimal distribution of hardware resources among the Docker service, the NaaOS service and the UniKraft service can be realized, the production efficiency of a factory is improved, and the operation cost is reduced.

Description

Technical Field

[0001] The embodiments of this application relate to the application of virtualization technology in factory production operations, and more particularly to a hardware resource allocation method and apparatus for edge servers. Background Technology

[0002] In the wave of industrial digital transformation, factories are increasingly reliant on web services for quality and appearance inspection of various products. From real-time monitoring of production processes to collaborative operations in supply chain management, various web services have become key factors in ensuring the efficient operation of factories. However, factory resources are limited, and how to deploy as many web services as possible under limited resource conditions has become an urgent problem to be solved.

[0003] Traditional resource allocation methods often rely on experience or simple rules, lacking precise quantitative analysis. When faced with complex factory environments and diverse web services, this approach can easily lead to unreasonable resource allocation, resulting in an oversupply of some services and a shortage of others, severely impacting factory production efficiency and operating costs.

[0004] Docker container technology is widely used in factories due to its lightweight and easy-to-deploy characteristics, but it has certain limitations in fine-grained resource management. Although Unikraft and Nanos unikernel technologies can provide more efficient resource utilization, there is currently no mature solution for how to work with Docker container technology to achieve optimal resource allocation in practical applications.

[0005] Therefore, it is urgent to develop a technology that can comprehensively consider factory resource conditions and Web service needs, and achieve optimal resource allocation through scientific methods. Summary of the Invention

[0006] In view of this, embodiments of this application provide a hardware resource allocation scheme for edge servers, which enables the Unikraft service, Nanos unikernel service and Docker service to work together and achieve optimal allocation of hardware resources.

[0007] In a first aspect of this application, a hardware resource allocation method for an edge server is provided, wherein the web services occupying the hardware resources include Docker service, NaaOS service, and UniKraft service, and the method includes: Calculate the total CPU and memory resources of all edge server nodes within the current target area; Obtain hardware resource usage data for Docker, NaaOS, and UniKraft services; The pre-constructed resource allocation objective function is solved using the hardware resource occupancy data to determine the corresponding objective function coefficients and generate the current hardware resource allocation function. The hardware resources occupied by the Docker service, NaaOS service, and UniKraft service are reallocated according to the hardware resource allocation function.

[0008] In some embodiments, the hardware resource usage data includes CPU utilization and / or memory usage.

[0009] In some embodiments, the pre-built resource allocation objective function aims to minimize the total CPU and memory usage, with constraints that the resource requirements of the Web service cannot exceed the maximum value of the total CPU and memory, and that the CPU and memory resources occupied by the Docker service, NaaOS service, and UniKraft service cannot exceed their respective maximum thresholds.

[0010] In some embodiments, the pre-built resource allocation objective function is: , in, This indicates the CPU utilization rate when service i is deployed. This indicates the memory usage when service i is deployed. Binary decision variables; The constraints of the objective function are: , , in, This represents the maximum amount of memory that can be used.

[0011] In some embodiments, solving the pre-constructed resource allocation objective function using the hardware resource occupancy data includes: The Gurobi solver is used to solve the pre-built resource allocation objective function based on the hardware resource usage data to obtain the minimum total CPU and memory resource utilization.

[0012] In some embodiments, after obtaining the hardware resource usage data of the Docker service, NaaOS service, and UniKraft service, the method further includes: The hardware resource usage data is preprocessed, including the removal of outliers and erroneous data.

[0013] In some embodiments, it also includes: During the operation of Web services, resource usage and business performance indicators are monitored in real time. If unreasonable resource allocation or business performance decline is detected, the adjustment mechanism is triggered in a timely manner to re-collect data, reconstruct the objective function, and solve the allocation scheme to achieve dynamic optimization of resource allocation.

[0014] In a second aspect of this application, a hardware resource allocation apparatus for an edge server is provided, wherein the web services occupying the hardware resources include Docker service, NaaOS service, and UniKraft service, and the apparatus includes: The statistics module is used to calculate the total CPU and memory resources of all edge server nodes within the current target area. The data acquisition module is used to acquire hardware resource usage data for Docker, NaaOS, and UniKraft services. The resource allocation function generation module is used to solve the pre-constructed resource allocation objective function using the hardware resource occupancy data, determine the corresponding objective function coefficients, and generate the current hardware resource allocation function. The resource allocation module is used to reallocate the hardware resources occupied by the Docker service, NaaOS service, and UniKraft service according to the hardware resource allocation function.

[0015] In a third aspect of this application, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0016] In a fourth aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to the first aspect of this application.

[0017] The hardware resource allocation method for edge servers provided in this application involves: statistically analyzing the total CPU and memory resources of all edge server nodes within a target area; obtaining hardware resource usage data for Docker, NaaOS, and UniKraft services; solving a pre-built resource allocation objective function using this data to determine the corresponding objective function coefficients and generate a current hardware resource allocation function; and reallocating the hardware resources used by Docker, NaaOS, and UniKraft services according to this hardware resource allocation function. This enables optimal allocation of hardware resources among Docker, NaaOS, and UniKraft services, thereby improving factory production efficiency and reducing operating costs.

[0018] It should be understood that the description in the Summary Section is not intended to limit the key or essential features of the embodiments of this application, nor is it intended to restrict the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A system architecture diagram relating to the methods provided in the embodiments of this application; Figure 2 A flowchart illustrating a hardware resource allocation method applied to an edge server according to an embodiment of this application; Figure 3 This is a block diagram of a hardware resource allocation apparatus applied to an edge server according to an embodiment of this application; Figure 4 A schematic diagram of the structure of a terminal device or server suitable for implementing the embodiments of this application; Figure 5 This is a flowchart illustrating a specific application example of the hardware resource allocation method for edge servers, as described in this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0021] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0022] Figure 1 A schematic diagram of an exemplary operating environment 100 in which embodiments of the present disclosure can be implemented is shown. The operating environment 100 includes a client 110, a communication module 120, a server 130, and a device 140.

[0023] Among them, client 110 can be the current user terminal, used to report instructions to server 130; Server 130 can be a cloud server used to maintain information about currently connected devices in the cloud, such as the online / offline status and device lock status of devices. When a device goes online or offline, the online / offline status of the device is updated, and the device lock status information is maintained. Furthermore, server 130 also includes: The device locking module is used to issue lock / unlock tasks to device 140, ensuring the consistency of device 140's status with that of server 130. When device 140 is offline, its lock status is synchronized to server 130, and a lock task is created. When device 140 comes online, the lock task is issued. The lock task is completed when device 140 returns a successful lock status.

[0024] Device 140 can be a target user terminal, used to receive instructions issued by server 130, perform corresponding operations according to the instructions, and report operation result information to server 130.

[0025] The communication module 120 is used to maintain the communication link between the device 140 and the server 130. When the device 140 goes online or offline, it reports callback information to the server 130. That is, the server 130 can connect to the device 140 through the communication module 120.

[0026] Figure 2 A flowchart illustrating a hardware resource allocation method applied to an edge server according to an embodiment of this application is shown. The method can be... Figure 1 The method is executed on server 130, wherein the web services consuming the hardware resources include Docker service, NaaOS service, and UniKraft service, and the method includes: S201: Calculate the total CPU and memory resources of all edge server nodes within the current target area.

[0027] This application's embodiment focuses on a web service for printing instruction detection deployed in an air switch production workshop environment to inspect the quality of printed products on the production line. To maximize the utilization efficiency of node CPU and memory resources and run more printing quality inspection services on the same hardware resources, this application proposes an innovative solution combining Docker container technology, Kraft Unikernel, and Nanos Unikernel technology. The CPU utilization and memory usage of the three virtualization technologies differ when running services. By statistically analyzing the resource consumption data of the three virtualization technologies during service deployment, a function is constructed with the total CPU and memory resource utilization as the objective. Resource allocation constraints, such as the maximum number of CPU cores and maximum memory usage of server nodes, are set. The above data is input into the Gurobi solver for optimization, obtaining the minimum value of the objective function and the minimum total CPU and memory resource utilization. This enables the running of more service instances on fixed hardware resources, thereby achieving higher resource utilization.

[0028] In implementing the technical solution of this application embodiment, it is first necessary to obtain the total CPU and memory resources of all edge server nodes within the target area (air switch production workshop). For example, the total resources of each resource node (such as server, storage device, etc.) can be statistically analyzed in detail, including the number of CPU cores, memory capacity, disk space and network bandwidth.

[0029] S202: Obtain hardware resource usage data for Docker, NaaOS, and UniKraft services.

[0030] In this embodiment, after obtaining the total CPU and memory resources of all edge server nodes within the target area, it is necessary to further obtain the CPU utilization and memory usage of the three virtualization technologies when running services, in order to statistically analyze the resource consumption data of the three virtualization technologies during service deployment. For example, the resource utilization, such as CPU utilization and memory usage, can be monitored separately when the Web service runs in Docker container, Unikraft, and Nanos unikernel environments.

[0031] After data collection is completed, the data can be organized and preprocessed to remove outliers and erroneous data, ensuring the accuracy and integrity of the data and providing a reliable basis for subsequent analysis and decision-making.

[0032] S203: Solve the pre-constructed resource allocation objective function using the hardware resource occupancy data, obtain the values ​​of binary decision variables, determine the corresponding objective function coefficients, and generate the current hardware resource allocation function.

[0033] In this embodiment, the objective function can be pre-built or built after data acquisition. During the construction process, various limiting factors in practical applications are fully considered, and constraints are set such that the resource requirements of Web services cannot exceed the supply of resources by the resource nodes to ensure the feasibility of resource allocation. Simultaneously, resource allocation upper limits are set for the characteristics and performance limitations of each virtualization technology to avoid over-allocation leading to system performance degradation. Furthermore, based on the importance and resource requirements of different types of Web services, their weights in the objective function are reasonably set. For example, higher weights are given to Web services related to core production business to ensure resource supply for critical business operations. Specifically, the objective can be to minimize the total CPU and memory usage, with constraints such as the Web service resource requirements not exceeding the total maximum CPU and memory usage, and the CPU and memory resources occupied by Docker, NaaOS, and UniKraft services not exceeding their respective maximum thresholds (not exceeding the total CPU of the resource nodes, and the memory allocated to each virtualization technology not exceeding its upper limit, etc.).

[0034] The constructed objective function for resource allocation is: , in, This indicates the CPU utilization rate when service i is deployed. This indicates the memory usage when service i is deployed. For binary decision variables; the mathematical definition of resource allocation strategy is a decision variable. The value combination. In this embodiment, for different The values ​​are combined to calculate the objective function values ​​under different allocation schemes, and then the optimal solution is selected.

[0035] The constraints of the objective function are: , , in, This represents the maximum amount of memory that can be used.

[0036] After constructing the resource allocation objective function, the hardware resource usage data can be used to solve the pre-constructed objective function. For example, the Gurobi solver can be used to solve the pre-constructed objective function based on the hardware resource usage data to obtain the minimum total CPU and memory resource utilization. Specifically, the constructed objective function is input into the Gurobi solver, and the solution parameters and optimization objectives are set. The solver quickly derives the optimal allocation scheme for the web service of print quality inspection across different virtualization technologies through iterative calculations. The solution results are verified and analyzed to ensure that the allocation scheme conforms to the actual situation and constraints.

[0037] S204: Reallocate the hardware resources occupied by the Docker service, NaaOS service, and UniKraft service according to the hardware resource allocation function.

[0038] In this embodiment, after solving the resource allocation objective function, automated deployment tools can be used to deploy the Web service to the corresponding virtualization environment according to the allocation scheme. After deployment, a real-time monitoring system can be started to continuously track the resource usage and business performance indicators of the Web service. The monitoring data is displayed through a visual interface, making it convenient for administrators to identify problems in a timely manner.

[0039] The hardware resource allocation method for edge servers in this application embodiment can achieve optimal allocation of hardware resources among Docker service, NaaOS service and UniKraft service, thereby improving factory production efficiency and reducing operating costs.

[0040] Furthermore, as an optional embodiment of this application, the above embodiments also include: During the operation of Web services, resource usage and business performance indicators are monitored in real time. If unreasonable resource allocation or business performance decline is detected, the adjustment mechanism is triggered in a timely manner to re-collect data, reconstruct the objective function, and solve the allocation scheme to achieve dynamic optimization of resource allocation.

[0041] like Figure 5The diagram shows a flowchart of a specific application example of the hardware resource allocation method for edge servers according to an embodiment of this application. When applying the method of this embodiment, data is first collected and processed. The collected data includes the total number of statistical resource nodes and the service occupancy rate. The collected data is processed to remove outliers and erroneous data, ensuring the accuracy and completeness of the data. Then, an objective function is constructed, with the goal of minimizing the amount of resources used. Constraints can be set, and resource weights for services can be assigned. The solution is then obtained using the Gurobi solver. After the solution is obtained, the Web service is deployed to the corresponding virtualization environment according to the allocation scheme. Monitoring is implemented to continuously track the resource usage and business performance indicators of the Web service. When abnormal resource usage or underperformance of the Web service for print quality inspection is detected, a dynamic adjustment process is initiated. Data is collected again, the objective function and constraints are updated, the allocation scheme is solved again using the Gurobi solver, and the deployment of the Web service is adjusted according to the new scheme to achieve continuous optimization of resource allocation.

[0042] This application embodiment, through precise data acquisition and analysis, scientific objective function construction, and efficient solver application, can achieve optimized allocation of Web service resources for printing quality inspection, effectively improve resource utilization, reduce operating costs, and provide strong support for the digital transformation of factories.

[0043] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0044] The above is an introduction to the method embodiments. The following describes the solution described in this application through device embodiments.

[0045] Figure 3 A block diagram of a hardware resource allocation apparatus for an edge server according to an embodiment of this application is shown; the web services occupying the hardware resources include Docker service, NaaOS service, and UniKraft service, and the apparatus includes: The statistics module 301 is used to count the total CPU and memory resources of all edge server nodes within the current target area. Data acquisition module 302 is used to acquire hardware resource usage data of Docker service, NaaOS service and UniKraft service; The resource allocation function generation module 303 is used to solve the pre-constructed resource allocation objective function using the hardware resource occupancy data, determine the corresponding objective function coefficients, and generate the current hardware resource allocation function. The resource allocation module 304 is used to reallocate the hardware resources occupied by the Docker service, NaaOS service and UniKraft service according to the hardware resource allocation function.

[0046] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0047] Figure 4 A schematic diagram of a terminal device or server suitable for implementing embodiments of this application is shown.

[0048] like Figure 4 As shown, the terminal device or server includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from storage section 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the terminal device or server. The CPU 401, ROM 402, and RAM 403 are interconnected via bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0049] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0050] Specifically, according to embodiments of this application, the above method flow steps can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the functions defined in the system of this application.

[0051] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0052] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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 diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0053] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be located in a processor. The names of these units or modules do not, in certain circumstances, constitute a limitation on the unit or module itself.

[0054] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable storage medium stores one or more programs that, when used by one or more processors, execute the methods described in this application.

[0055] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A hardware resource allocation method for edge servers, characterized in that, The web services consuming the hardware resources include Docker service, NaaOS service, and UniKraft service, and the method includes: Calculate the total CPU and memory resources of all edge server nodes within the current target area; Obtain hardware resource usage data for Docker, NaaOS, and UniKraft services; The pre-constructed resource allocation objective function is solved using the hardware resource occupancy data to determine the corresponding objective function coefficients and generate the current hardware resource allocation function. The hardware resources occupied by the Docker service, NaaOS service, and UniKraft service are reallocated according to the hardware resource allocation function.

2. The method according to claim 1, characterized in that, The hardware resource usage data includes at least one of CPU utilization and memory usage.

3. The method according to claim 1, characterized in that, The pre-built resource allocation objective function aims to minimize the total CPU and memory usage, with constraints that the resource requirements of the Web service cannot exceed the maximum value of the total CPU and memory, and that the CPU and memory resources occupied by the Docker service, NaaOS service, and UniKraft service cannot exceed their respective maximum thresholds.

4. The method according to claim 3, characterized in that, The pre-constructed resource allocation objective function is: ， in, This indicates the CPU utilization rate when service i is deployed. This indicates the memory usage of service i during deployment; Binary decision variables; The constraints of the objective function are: ， in, This represents the maximum amount of memory that can be used.

5. The method according to claim 1, characterized in that, The step of solving the pre-constructed resource allocation objective function using the hardware resource usage data includes: The pre-built resource allocation objective function is solved using the Gurobi solver based on the hardware resource usage data to obtain the minimum total CPU and memory resource utilization. The Gurobi solver can be replaced with an open-source solver to adapt to the lightweight deployment requirements of edge servers.

6. The method according to claim 2, characterized in that, After obtaining the hardware resource usage data of the Docker service, NaaOS service, and UniKraft service, the method further includes: The hardware resource usage data is preprocessed, including the removal of outliers and erroneous data.

7. The method according to claim 1, characterized in that, Also includes: During the operation of the Web service, its resource usage and business performance indicators are monitored in real time. When resource usage exceeds the preset threshold or business performance falls below the preset standard, an adjustment mechanism is triggered to re-collect data, reconstruct the objective function, and solve the allocation scheme to achieve dynamic optimization of resource allocation.

8. A hardware resource allocation device for edge servers, characterized in that, The web services that consume the hardware resources include Docker service, NaaOS service, and UniKraft service, and the device includes: The statistics module is used to calculate the total CPU and memory resources of all edge server nodes within the current target area. The data acquisition module is used to acquire hardware resource usage data for Docker, NaaOS, and UniKraft services. The resource allocation function generation module is used to solve the pre-constructed resource allocation objective function using the hardware resource occupancy data, determine the corresponding objective function coefficients, and generate the current hardware resource allocation function. The resource allocation module is used to reallocate the hardware resources occupied by the Docker service, NaaOS service, and UniKraft service according to the hardware resource allocation function.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

10. 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 the method as described in any one of claims 1 to 7.

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