System resource management method and device, equipment and storage medium
The prediction model established by the LSTM neural network solves the problems of static threshold limit, underfitting, overfitting and single indicator dependence in system resource management, and realizes intelligent and efficient management of dynamic resource allocation.
Patent Information
- Application Number
- CN202510532992.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-25
- Publication Date
- 2025-08-01
AI Technical Summary
In the prior art, system resource management has problems such as static threshold limits, underfitting or overfitting, lack of prediction ability and single indicator dependence, resulting in inaccurate resource allocation and delayed response.
By collecting historical monitoring data on system resource usage information, using LSTM neural network to establish a prediction model, formulating a dynamic resource allocation decision-making mechanism, and adjusting and optimizing through feedback data.
It realizes an intelligent resource allocation method, improves the accuracy and response speed of resource management, and adapts to changes in system and user needs.
Smart Images
Figure CN120407182A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of computer data processing, and particularly to a system resource management method, device, equipment and computer-readable storage medium. Background Art
[0002] With the development of information technology, the problem of system resource exhaustion has become increasingly prominent. The service model of a computer system resource allocates computing resources on demand, where the computing resources include servers, databases, storage, platforms, architectures, and applications, etc. There are the following four problems in common prior arts: (1) Static threshold limitation. Many existing scaling strategies are based on static thresholds (such as CPU utilization exceeding 80% or 90%). This way may not accurately reflect the dynamic requirements of applications. (2) Underfitting or overfitting. Simple rules or models may lead to too high frequency (overfitting) or slow response (underfitting) of scaling operations, resulting in resource waste or delayed response. (3) Lack of prediction ability. Many current systems do not have the ability to predict future load changes, resulting in insufficient preparation for sudden traffic. (4) Dependence on a single metric. Relying on a single monitoring metric (such as CPU or memory) is not sufficient to comprehensively evaluate resource requirements and may ignore other important metrics (such as I / O and network latency).
[0003] In summary, how to provide an efficient and stable system resource management method has become increasingly urgent. Summary of the Invention
[0004] The present invention provides a system resource management method, device, equipment and computer-readable storage medium. Through this system resource management method, the resource allocation method is intelligently predicted, adjusted and optimized.
[0005] In a first aspect, a system resource management method is provided, including: collecting historical monitoring data of system resource usage information, where the historical monitoring data includes: processor utilization rate, memory usage, resource occupation request duration, service data traffic, number of data processing tasks, and network latency duration, and performing cleaning and normalization operations on the historical monitoring data; splitting the historical monitoring data into a training set and a test set, establishing a system resource prediction model using an LSTM neural network, estimating the usage of system resources within a certain future time period, and obtaining the estimated system resource usage information; formulating a dynamic resource allocation decision mechanism based on the estimated system resource usage information and the current system resource usage information; evaluating the effectiveness of the dynamic resource allocation decision mechanism, and making adjustments and optimizations according to the feedback data to adapt to changes in system resources and user requirements.
[0006] In some embodiments, cleaning and normalizing the historical monitoring data includes: cleaning in a data processing module to remove invalid or incorrect records.
[0007] In some embodiments, splitting the historical monitoring data into a training set and a test set, and establishing a system resource prediction model using an LSTM neural network includes: randomly dispersing the data of the system resource usage in a critical state, an initial state, a busy state, and an idle state in the historical monitoring data in the training set and the test set; selecting a machine learning algorithm of the LSTM neural network for training to establish a prediction model for the usage of each system resource.
[0008] In some embodiments, the dynamic resource allocation decision mechanism includes a first dynamic resource allocation decision mechanism and a second dynamic resource allocation decision mechanism. Among them, when the processor utilization rate is greater than 80%, the first dynamic resource allocation decision mechanism issues a notice that the system resources need to be expanded and reports it; when the memory usage is less than 50%, the second dynamic resource allocation decision mechanism issues a notice that the system resources need to be scaled down and reports it.
[0009] In some embodiments, it further includes: calculating the expansion amount according to the first dynamic resource allocation decision mechanism, where the expansion amount needs to be less than the maximum threshold, and the maximum threshold is equal to the sum of the average value of the historical monitoring data and twice the standard deviation; and calculating the scaling-down amount according to the second dynamic resource allocation decision mechanism, where the scaling-down amount is less than a predetermined safety lower limit.
[0010] In some embodiments, evaluating the effectiveness of the dynamic resource allocation decision mechanism and adjusting and optimizing it according to the feedback data includes: determining that the dynamic resource allocation decision mechanism is effective when the estimated system resource usage information is less than the maximum threshold; adjusting and optimizing according to the feedback data to make the estimated system resource usage information tend to a stable threshold.
[0011] In some embodiments, the stable threshold is equal to the sum of the average value of the historical monitoring data and one standard deviation.
[0012] In a second aspect, a device for system resource management is provided, including: a data collection module that collects historical monitoring data of system resource usage information, where the historical monitoring data includes: processor utilization rate, memory usage, resource occupancy request duration, service data traffic, number of data processing tasks, and network latency duration, and the historical monitoring data is subjected to cleaning and normalization operations; a model establishment module that divides the historical monitoring data into a training set and a test set, uses an LSTM neural network to establish a system resource prediction model, estimates the usage of system resources within a certain future time period, and obtains the estimated system resource usage information; a decision generation module that formulates a dynamic resource allocation decision mechanism based on the estimated system resource usage information and the current system resource usage information; an evaluation and optimization module that evaluates the effectiveness of the dynamic resource allocation decision mechanism, and makes adjustments and optimizations according to the feedback data to adapt to changes in system resources and user requirements.
[0013] In a third aspect, the present invention provides an electronic device, which includes: a processor; a memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the above system resource management method is implemented.
[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium, characterized in that program code is stored in the computer-readable storage medium, and the program code can be called by a processor to execute the above system resource management method.
[0015] Compared with the prior art, the present invention can at least achieve the following beneficial effects: Through the system resource management method, the resource allocation method is intelligently predicted, and adjustments and optimizations are made.
[0016] The summary of the invention is provided to introduce, in a simplified form, a selection of concepts that will be further described in the detailed implementation below. The summary of the invention is not intended to identify the key features or essential features of the present disclosure, nor is it intended to limit the scope of the present disclosure. Brief Description of the Drawings
[0017] By describing the exemplary embodiments of the present disclosure in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present disclosure will become more apparent, where in the exemplary embodiments of the present disclosure, the same reference numerals generally represent the same components.
[0018] Figure 1 Shows the flowchart of the system resource management method provided by the embodiment of the present application;
[0019] Figure 2 Is the schematic block diagram of the device for system resource management provided by the embodiment of the present application;
[0020] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners
[0021] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure will be more thorough and complete, and can fully convey the scope of the present disclosure to those skilled in the art.
[0022] The term "including" and its variants used herein mean open inclusion, that is, "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "at least partially based on". The term "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. There may also be other explicit and implicit definitions hereinafter.
[0023] The present disclosure aims to solve the following problems in the process of existing technology for system resource management: (1) Static threshold limitation. Many existing scaling strategies are based on static thresholds (such as CPU utilization exceeding 80% or 90%). This method may not accurately reflect the dynamic requirements of applications. (2) Underfitting or overfitting. Simple rules or models may lead to too high frequency (overfitting) or slow response (underfitting) of scaling operations, resulting in resource waste or delayed response. (3) Lack of prediction ability. Many current systems do not have the ability to predict future load changes, resulting in insufficient preparation for sudden traffic. (4) Dependence on a single metric. Relying on a single monitoring metric (such as CPU or memory) is not sufficient to comprehensively evaluate resource requirements and may ignore other important metrics (such as I / O and network latency).
[0024] The present application provides a system resource management method. The method includes: collecting historical monitoring data of system resource usage information, where the historical monitoring data includes: processor utilization rate, memory usage, resource occupancy request duration, service data traffic, number of data processing tasks, and network latency duration, and performing cleaning and normalization operations on the historical monitoring data; splitting the historical monitoring data into a training set and a test set, establishing a system resource prediction model using an LSTM neural network, estimating the usage of system resources within a certain future time period, and obtaining the estimated system resource usage information; formulating a dynamic resource allocation decision mechanism based on the estimated system resource usage information and the current system resource usage information; evaluating the effectiveness of the dynamic resource allocation decision mechanism, and adjusting and optimizing according to the feedback data to adapt to changes in system resources and user requirements.
[0025] Please refer to Figure 1 , which is a schematic diagram of an embodiment of the present application. The following will be described in detail with reference to Figure 1 a system resource management method 100 provided by an embodiment of the present application.
[0026] Step 102: Collect historical monitoring data of system resource usage information; specifically, collect historical monitoring data of system resource usage information, where the historical monitoring data includes: processor utilization rate, memory usage, resource occupancy request duration, service data traffic, number of data processing tasks, and network latency duration, and perform cleaning and normalization operations on the historical monitoring data.
[0027] Step 104: Establish a system resource prediction model using an LSTM neural network; specifically, split the historical monitoring data into a training set and a test set, establish a system resource prediction model using an LSTM neural network, estimate the usage of system resources within a certain future time period, and obtain the estimated system resource usage information.
[0028] Step 106: Formulate a dynamic resource allocation decision mechanism; specifically, formulate a dynamic resource allocation decision mechanism based on the estimated system resource usage information and the current system resource usage information.
[0029] Step 108: Evaluate and optimize; specifically, evaluate the effectiveness of the dynamic resource allocation decision mechanism, and adjust and optimize according to the feedback data to adapt to changes in system resources and user requirements.
[0030] In some embodiments, performing cleaning and normalization operations on the historical monitoring data includes: performing cleaning in a data processing module to remove invalid or incorrect records.
[0031] In some embodiments, the historical monitoring data is segmented into a training set and a test set, and an LSTM neural network is used to establish a system resource prediction model, including: randomly dispersing the data of the system resource usage in the critical state, initial state, busy state, and idle state in the historical monitoring data into the training set and the test set; selecting a machine learning algorithm of the LSTM neural network for training to establish a prediction model for the usage of each system resource.
[0032] In some embodiments, the dynamic resource allocation decision mechanism includes a first dynamic resource allocation decision mechanism and a second dynamic resource allocation decision mechanism. Among them, when the processor utilization rate is greater than 80%, the first dynamic resource allocation decision mechanism issues a notice that the system resources need to be expanded and reports it; when the memory usage is less than 50%, the second dynamic resource allocation decision mechanism issues a notice that the system resources need to be scaled down and reports it.
[0033] In some embodiments, it further includes: calculating the expansion amount according to the first dynamic resource allocation decision mechanism, where the expansion amount needs to be less than the maximum threshold, and the maximum threshold is equal to the sum of the average value of the historical monitoring data and twice the standard deviation; and calculating the scaling-down amount according to the second dynamic resource allocation decision mechanism, where the scaling-down amount is less than a predetermined safety lower limit.
[0034] In some embodiments, the effectiveness of the dynamic resource allocation decision mechanism is evaluated and adjusted and optimized according to the feedback data, including: when the estimated system resource usage information is less than the maximum threshold, it is determined that the dynamic resource allocation decision mechanism is effective; adjusting and optimizing according to the feedback data to make the estimated system resource usage information tend to the stable threshold. [[ID=X]]
[0035] In some embodiments, the stable threshold is equal to the sum of the average value of the historical monitoring data and one standard deviation.
[0036] Figure 2 It is a schematic block diagram of a device 200 for system resource management shown according to an exemplary embodiment. The device 200 for system resource management includes: a data acquisition module 202, a model establishment module 204, a decision generation module 206, and an evaluation and optimization module 208, where:
[0037] The data acquisition module 202 collects historical monitoring data of system resource usage information. Among them, the historical monitoring data includes: processor utilization rate, memory usage, resource occupation request duration, service data traffic, data processing task quantity, and network delay duration. Among them, cleaning and normalization operations are performed on the historical monitoring data;
[0038] The model building module 204 divides the historical monitoring data into a training set and a test set, and uses an LSTM neural network to build a system resource prediction model to estimate the usage of system resources in a future period, obtaining the estimated system resource usage information;
[0039] The decision-making generation module 206 formulates a dynamic resource allocation decision mechanism based on the estimated system resource usage information and the current system resource usage information;
[0040] The evaluation and optimization module 208 evaluates the effectiveness of the dynamic resource allocation decision mechanism, and adjusts and optimizes it according to the feedback data to adapt to the changes in system resources and user requirements.
[0041] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As Figure 3 shown, the electronic device 300 may include a processor 3001 and a memory 3002. Optionally, the electronic device 300 may further include a transceiver 3003. Among them, the processor 3001, the memory 3002, and the transceiver 3003 may be connected through a communication bus, for example. Computer-readable instructions are stored on the memory 3002, and when the computer-readable instructions are executed by the processor 3001, the steps of the system resource management method as described above are implemented.
[0042] In a specific implementation, as an embodiment, the processor 3001 may include one or more CPUs, such as Figure 3 CPU0 and CPU1 shown in
[0043] In a specific implementation, as an embodiment, the electronic device 300 may also include multiple processors, such as Figure 3 the processor 3001 and the processor 3004 shown in
[0044] Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, the processor may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).
[0045] The transceiver 3003 is used to communicate with a network device or with a terminal device.
[0046] Optionally, the transceiver 3003 may include a receiver and a transmitter. Among them, the receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.
[0047] Optionally, the transceiver 3003 may be integrated with the processor 3001 or exist independently and be coupled to the processor 3001 through the interface circuit of the electronic device 300. The embodiments of the present invention do not make specific limitations on this.
[0048] It should be noted that Figure 3 the structure of the electronic device 300 shown in does not constitute a limitation on the electronic device. The actual electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. In addition, the technical effects of the electronic device 300 may refer to the technical effects of the above method embodiments and will not be elaborated here.
[0049] In an exemplary embodiment, the present invention further provides a computer-readable storage medium. At least one instruction is stored in the computer-readable storage medium, and the at least one instruction is loaded and executed by a processor to implement the steps of the system resource management method as described above. For example, the computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.
[0050] The embodiments of the present invention further provide an electronic device, which includes: a processor; a memory, and computer-readable instructions are stored on the memory. When the computer-readable instructions are executed by the processor, the above system resource management method is implemented.
[0051] The embodiments of the present invention provide a computer-readable storage medium, characterized in that program code is stored in the computer-readable storage medium, and the program code can be called by a processor to execute the above system resource management method.
[0052] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).
[0053] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.
[0054] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.
[0055] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or a similar expression means any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.
[0056] It should be understood that in various embodiments of the present invention, the magnitude of the sequence numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
[0057] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.
[0058] Those skilled in the art can clearly understand that for the sake of convenience and brevity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.
[0059] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.
[0060] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0061] In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.
[0062] When the above-mentioned functions are implemented in the form of software functional units 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 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. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.
[0063] The embodiments of the present disclosure have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technologies in the market, or to enable other ordinary skill in the art in the technical field to understand the embodiments disclosed herein.
Claims
1. A system resource management method, characterized in that, Including: Collecting historical monitoring data on system resource usage, where the historical monitoring data includes: processor utilization rate, memory usage, resource occupancy request duration, service data traffic, number of data processing tasks, and network latency duration, and performing cleaning and normalization operations on the historical monitoring data; Dividing the historical monitoring data into a training set and a test set, using an LSTM neural network to establish a system resource prediction model, and estimating the usage of system resources within a certain future time period to obtain the estimated system resource usage information; Based on the estimated system resource usage information and the current system resource usage information, formulating a dynamic resource allocation decision mechanism; Evaluating the effectiveness of the dynamic resource allocation decision mechanism, and making adjustments and optimizations according to the feedback data to adapt to changes in system resources and user requirements.
2. The system resource management method according to claim 1, wherein Performing cleaning and normalization operations on the historical monitoring data, including: Performing cleaning in the data processing module to remove invalid or incorrect records.
3. The system resource management method according to claim 1, characterized in that Dividing the historical monitoring data into a training set and a test set, and using an LSTM neural network to establish a system resource prediction model, including: Randomly dispersing the data with the system resource usage in the critical state, initial state, busy state, and idle state in the historical monitoring data in the training set and the test set; Selecting a machine learning algorithm of the LSTM neural network for training to establish a prediction model for the usage of each system resource.
4. The system resource management method according to claim 1, wherein The dynamic resource allocation decision mechanism includes a first dynamic resource allocation decision mechanism and a second dynamic resource allocation decision mechanism. Among them, when the processor utilization rate is greater than 80%, the first dynamic resource allocation decision mechanism issues a notice that the system resources need to be expanded and reports it; when the memory usage is less than 50%, the second dynamic resource allocation decision mechanism issues a notice that the system resources need to be scaled down and reports it.
5. The system resource management method according to claim 4, wherein Also including: Calculating the expansion amount according to the first dynamic resource allocation decision mechanism, where the expansion amount needs to be less than the maximum threshold, and the maximum threshold is equal to the sum of the average value of the historical monitoring data and twice the standard deviation; and Calculating the scaling-down amount according to the second dynamic resource allocation decision mechanism, where the scaling-down amount is less than a predetermined safety lower limit.
6. The system resource management method according to claim 5, wherein Evaluating the effectiveness of the dynamic resource allocation decision mechanism, and making adjustments and optimizations according to the feedback data, including: When the estimated system resource usage information is less than the maximum threshold, it is determined that the dynamic resource allocation decision mechanism is effective; Making adjustments and optimizations according to the feedback data to make the estimated system resource usage information tend to a stable threshold.
7. The system resource management method according to claim 6, characterized in that The stable threshold is equal to the sum of the average value of the historical monitoring data and one times the standard deviation.
8. An apparatus for system resource management, characterized in that, Including: A data acquisition module that collects historical monitoring data on system resource usage, where the historical monitoring data includes: processor utilization rate, memory usage, resource occupancy request duration, service data traffic, number of data processing tasks, and network latency duration, and performing cleaning and normalization operations on the historical monitoring data; A model establishment module that divides the historical monitoring data into a training set and a test set, uses an LSTM neural network to establish a system resource prediction model, estimates the usage of system resources within a certain future time period, and obtains the estimated system resource usage information; A decision generation module that formulates a dynamic resource allocation decision mechanism based on the estimated system resource usage information and the current system resource usage information; An evaluation and optimization module that evaluates the effectiveness of the dynamic resource allocation decision mechanism, and adjusts and optimizes it according to the feedback data to adapt to changes in system resources and user requirements.
9. An electronic device, characterized in that, The electronic device includes: A processor; A memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, the system resource management method according to any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium, characterized in that, Program code is stored in the computer-readable storage medium, and the program code can be called by the processor to execute the system resource management method according to any one of claims 1 to 7.