CN113722096B - CPU frequency adjusting method, system, equipment and storage medium of edge computing node - Google Patents

CPU frequency adjusting method, system, equipment and storage medium of edge computing node Download PDF

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Publication number
CN113722096B
CN113722096B CN202110983338.3A CN202110983338A CN113722096B CN 113722096 B CN113722096 B CN 113722096B CN 202110983338 A CN202110983338 A CN 202110983338A CN 113722096 B CN113722096 B CN 113722096B
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cpu
edge computing
computing node
working frequency
frequency
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CN113722096A (en
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申连腾
李哲
黄天航
钱声攀
李凌
翟天一
底晓梦
张鑫
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
    • G06F1/3206—Monitoring of events, devices or parameters that trigger a change in power modality
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
    • G06F1/3234—Power saving characterised by the action undertaken
    • G06F1/324—Power saving characterised by the action undertaken by lowering clock frequency
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Power Sources (AREA)

Abstract

The application provides a CPU frequency adjusting method, a system, equipment and a storage medium of an edge computing node, wherein the method comprises the following steps: acquiring all influence factors for controlling the working frequency of the CPU, wherein the influence factors are determined by different service scenes; according to the characteristics of the service scene, the adaptive range of the influence factors is set for each influence factor, and the CPU working frequency is dynamically set according to the adaptive range.

Description

CPU frequency adjusting method, system, equipment and storage medium of edge computing node
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, a system, an apparatus, and a storage medium for adjusting a CPU frequency of an edge computing node.
Background
In the traditional power calculation, data collected by the terminals of each power are transmitted to the main station for unified centralized processing. In the ubiquitous power internet of things, various growing power terminal devices and business applications can generate massive data, the data transmission and processing can cause huge pressure on a master station, and the requirements of new business forms cannot be met due to high time delay and safety.
And the edge calculation provides service for the nearby user side, so that the analysis processing of real-time data and the service requirement of low time delay are met, the safety of the data can be ensured, and the power grid is helped to avoid risks. Based on the advantages of edge calculation, the method has rich application scenes in electric power. The method has rich application scenes in comprehensive energy management, real-time monitoring of the transmission line, active low-voltage fault research and judgment and multi-station fusion.
However, the edge computing also has technical characteristics different from the cloud computing, on one hand, the place where the edge computing node is deployed cannot generally provide stable power supply like a data center. The specification and redundancy of the power supply line are difficult to ensure. Edge computing nodes cannot consume as much power as computing nodes within a cloud computing data center. On the other hand, the edge computing node is generally used for processing the application with strong burstiness, instantaneity and higher time delay requirement, so that the processing capacity of the edge computing node needs to be well ensured. In general, the edge computing node needs to reduce the power consumption of the edge computing node on the premise of effectively guaranteeing timely processing of a large number of burst tasks.
The CPU is the main power consumption unit in the server and is also the most main calculation unit, and the analysis of the power consumption and the performance of the edge calculation node is focused on the CPU. The CPU performs various tasks by executing a series of machine instructions, the speed at which the instructions are executed being determined by the CPU frequency. The inventor analyzes the working mechanism of the CPU, and designs a method which can timely increase the CPU frequency to complete the calculation task when the task load is large and reduce the CPU frequency and the power consumption of the edge calculation node when no task exists.
The edge computing node provides service near the user side, a large amount of computation is put down to the edge end to finish, the pressure of the data center is reduced, the analysis processing of real-time data and the service requirement of low time delay are met, the operation and maintenance cost is reduced, and the system efficiency is improved. However, the power supply of the edge computing node cannot be stable and durable like a data center, so that a better strategy is needed for adjusting the working frequency of the CPU of the edge computing node, and the purpose of reducing the power consumption of the edge computing node while meeting the computing requirement is achieved.
Disclosure of Invention
Aiming at the defects existing in the prior art, the application designs a CPU frequency adjusting method, a system, equipment and a storage medium of an edge computing node.
In order to achieve the above purpose, the application adopts the following technical scheme:
a method for adjusting the CPU frequency of an edge computing node, comprising the steps of:
acquiring all influence factors for controlling the working frequency of the CPU, wherein the influence factors are determined by different service scenes;
and setting an adaptation range of the influence factors for each influence factor according to the characteristics of the service scene, and dynamically setting the CPU working frequency according to the adaptation range.
As a further improvement of the present application, the influencing factors include one or more of network traffic, CPU utilization, disk IO traffic, CPU load, hardware information, or traffic information.
As a further development of the application, the influence factors are determined by different traffic scenarios in particular:
for network intensive tasks, taking network traffic as an influence factor of CPU frequency adjustment;
for a disk IO intensive task, taking the disk IO flow as an influence factor of CPU frequency adjustment;
for other edge computing scenarios, the hardware information or the traffic information is used as an influencing factor for determining the CPU frequency.
As a further improvement of the present application, the dynamically setting the CPU operating frequency according to the adaptation range specifically includes:
for a network intensive task scene, combining the reference CPU utilization rate data with the network flow rate data to correspond to the CPU working frequency, and obtaining the current working frequency of the edge computing node according to the duty ratio of the real-time CPU utilization rate data, the network flow rate data and the reference;
for a disk IO intensive task scene, setting an adaptation range of disk IO flow and CPU utilization rate for each CPU working frequency, and dynamically setting the current working frequency of the edge computing node according to the adaptation range.
As a further improvement of the application, the acquisition of all the influencing factors for controlling the CPU operating frequency is obtained by the IoT device with its own sensor acquisition; the acquired influence factors send information to the edge computing nodes through a network.
As a further improvement of the application, the set CPU operating frequency is sent to a cloud computing node.
A CPU frequency adjustment system of an edge computing node, comprising:
the acquisition module is used for acquiring all influence factors for controlling the working frequency of the CPU, wherein the influence factors are determined by different service scenes;
the control module is used for setting the adaptation range of the influence factors for each influence factor according to the characteristics of the service scene, and dynamically setting the CPU working frequency according to the adaptation range.
As a further development of the application, in the control module,
the influence factors are determined by different business scenes and specifically are as follows:
for network intensive tasks, taking network traffic as an influence factor of CPU frequency adjustment;
for a disk IO intensive task, taking the disk IO flow as an influence factor of CPU frequency adjustment;
for other edge computing scenarios, the hardware information or the traffic information is used as an influencing factor for determining the CPU frequency.
The dynamically setting of the CPU working frequency according to the adaptive range is specifically as follows:
for a network intensive task scene, combining the reference CPU utilization rate data with the network flow rate data to correspond to the CPU working frequency, and obtaining the current working frequency of the edge computing node according to the duty ratio of the real-time CPU utilization rate data, the network flow rate data and the reference;
for a disk IO intensive task scene, setting an adaptation range of disk IO flow and CPU utilization rate for each CPU working frequency, and dynamically setting the current working frequency of the edge computing node according to the adaptation range.
An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method for CPU frequency adjustment of the edge computing node when the computer program is executed.
A computer readable storage medium storing a computer program which when executed by a processor implements the steps of a CPU frequency adjustment method of the edge computing node.
Compared with the prior art, the application has the beneficial effects that:
the application can more accurately control the CPU frequency of the edge computing node by acquiring all relevant information for controlling the CPU working frequency. The method and the device realize the purposes of timely improving the CPU frequency to meet the requirement of calculation tasks when burst tasks occur and automatically reducing the CPU frequency and saving the power consumption of edge nodes when no tasks occur. According to the application, the CPU frequency can be accurately regulated according to the load condition, and the CPU frequency regulation mechanism of the edge computing node is optimized according to the characteristics of the edge computing scene, so that the purpose that the edge node can better complete the computing task and reduce the power consumption is realized.
Drawings
FIG. 1 is a flow chart of a CPU frequency adjustment method of an edge computing node of the present application;
FIG. 2 is a block diagram of edge computation of the present application;
FIG. 3 is a schematic diagram of an edge compute node CPU frequency modulation overall framework of the present application;
FIG. 4 is a schematic diagram of a CPU frequency adjustment system of an edge computing node according to the present application;
fig. 5 is a schematic structural diagram of an electronic device according to the present application.
Detailed Description
The application will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the application. Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the application.
The CPU is a key component for the edge computing node to complete the computing task, and the working frequency is a key index for determining the running speed and the power consumption of the CPU. The CPU frequency is increased to enable the CPU to complete the calculation task faster, and consume more energy, and conversely, the purpose of saving the power consumption can be achieved. The current CPU frequency is regulated by operating system software, which controls the CPU operating frequency by monitoring CPU utilization only for Linux systems. When the system finds that the CPU utilization rate is improved, a higher working frequency is set for the CPU according to a set rule, and when the CPU utilization rate is reduced, a lower working frequency is set. This approach is applicable for most situations, but has limitations for edge compute nodes that handle bursty large concurrent tasks. The method is mainly characterized in that for the service scene with burst large flow, the frequency modulation action is delayed to the service caused by the method of adjusting the CPU working frequency through the CPU utilization rate. The reasons for hysteresis mainly include two types of delays: the delay of the service arrival time and the CPU utilization rate lifting time and the delay existing in the actual frequency modulation action. For the reasons mentioned above, for the scene of burst large service, in order to ensure service performance, a method of turning off dynamic frequency modulation of the CPU is generally adopted, i.e. the CPU frequency is kept at a higher value, so as to meet the requirement of processing burst service. This necessarily causes an increase in power consumption of the edge computing node.
In order to achieve the purpose of reducing the average power consumption of the edge computing node and simultaneously processing burst tasks in time, as shown in fig. 1, a first object of the present application is to provide a method for adjusting the CPU frequency of the edge computing node, which comprises the following steps:
acquiring all influence factors for controlling the working frequency of the CPU, wherein the influence factors are determined by different service scenes;
and setting an adaptation range of the influence factors for each influence factor according to the characteristics of the service scene, and dynamically setting the CPU working frequency according to the adaptation range.
The influence factors comprise one or more of network traffic, CPU utilization, disk IO traffic, CPU load, hardware information or service information.
On the basis of CPU utilization rate, more relevant parameters are added to jointly determine the working frequency of the CPU.
The influence factor selection principle is as follows:
for network intensive tasks, taking network traffic as an influence factor of CPU frequency adjustment;
for a disk IO intensive task, taking the disk IO flow as an influence factor of CPU frequency adjustment;
for other edge computing scenarios, the hardware information or the traffic information is used as an influencing factor for determining the CPU frequency.
As a preferred embodiment, the dynamically setting the CPU operating frequency according to the adaptation range specifically includes:
for a network intensive task scene, combining the reference CPU utilization rate data with the network flow rate data to correspond to the CPU working frequency, and obtaining the current working frequency of the edge computing node according to the duty ratio of the real-time CPU utilization rate data, the network flow rate data and the reference;
for a disk IO intensive task scene, setting an adaptation range of disk IO flow and CPU utilization rate for each CPU working frequency, and dynamically setting the current working frequency of the edge computing node according to the adaptation range. Wherein, the obtaining all the influencing factors for controlling the CPU working frequency is obtained by the IoT device by utilizing its own sensor acquisition; the acquired influence factors send information to the edge computing nodes through a network.
And the set CPU working frequency is sent to the cloud computing node by the edge computing node.
As shown in fig. 2, the present application further provides a CPU frequency adjustment system of an edge computing node, including:
the acquisition module is used for acquiring all influence factors for controlling the working frequency of the CPU, wherein the influence factors are determined by different service scenes;
the control module is used for setting the adaptation range of the influence factors for each influence factor according to the characteristics of the service scene, and dynamically setting the CPU working frequency according to the adaptation range.
In the control module, the control unit is connected with the control unit,
the influence factors are determined by different business scenes and specifically are as follows:
for network intensive tasks, taking network traffic as an influence factor of CPU frequency adjustment;
for a disk IO intensive task, taking the disk IO flow as an influence factor of CPU frequency adjustment;
for other edge computing scenarios, the hardware information or the traffic information is used as an influencing factor for determining the CPU frequency.
The dynamically setting of the CPU working frequency according to the adaptive range is specifically as follows:
for a network intensive task scene, combining the reference CPU utilization rate data with the network flow rate data to correspond to the CPU working frequency, and obtaining the current working frequency of the edge computing node according to the duty ratio of the real-time CPU utilization rate data, the network flow rate data and the reference;
for a disk IO intensive task scene, setting an adaptation range of disk IO flow and CPU utilization rate for each CPU working frequency, and dynamically setting the current working frequency of the edge computing node according to the adaptation range.
Specifically, the acquisition module is used for acquiring information which can embody the system load. In addition to the CPU utilization data that is currently in use, some information that is strongly related to the business characteristics is also collected.
For example:
for edge computing nodes running network intensive tasks, the network traffic of the nodes needs to be taken into account. For the edge computing node running disk IO intensive tasks, the read-write traffic of the disk needs to be taken into account. Other data such as CPU load, memory utilization, etc. can be collected as needed.
For network intensive tasks, there are two advantages to using network traffic as an influencing factor for CPU frequency adjustment. First, the CPU frequency can be adjusted in time. When the dense network processing task arrives, the network traffic is firstly lifted, then the CPU processes the data, and the CPU utilization rate is improved. The frequency of the CPU can be controlled earlier by incorporating network traffic into the impact factor. Secondly, the CPU frequency requirement can be evaluated more accurately, and for network tasks with lighter calculation task amount, even larger network traffic can not cause larger improvement of CPU utilization rate, and in this case, the network tasks can obtain better processing speed by improving the CPU frequency based on the network traffic data.
For disk IO intensive tasks, similar benefits can be obtained by taking IO traffic as an influencing factor for CPU frequency adjustment.
For other specific edge computing scenarios, other relevant hardware information or business information may be collected as factors that determine the CPU frequency.
The control module is used for comprehensively determining the working frequency of the CPU, and the control module sets the adaptation range of the influence factors for each CPU working frequency according to different scenes. The control module dynamically sets the CPU working frequency according to the adaptive scheme. For example:
for a network intensive task scenario, an adaptation range of network traffic and CPU utilization is set for each CPU operating frequency.
And setting an adaptation range of disk IO flow and CPU utilization rate for each CPU working frequency for a disk IO intensive task scene.
The application can control the CPU frequency of the edge computing node more accurately. The method and the device realize the purposes of timely improving the CPU frequency to meet the requirement of calculation tasks when burst tasks occur and automatically reducing the CPU frequency and saving the power consumption of edge nodes when no tasks occur.
For the purpose of making the technical solution and effect of the present application clearer, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the detailed description is intended to illustrate the application, and not to limit the application.
Examples
The application aims to provide a method for adjusting the CPU working frequency of an edge computing node, which enables the edge computing node to timely process burst tasks and simultaneously reduce power consumption when no task exists.
Fig. 3 depicts the relationship of IoT devices, edge computing nodes, cloud computing nodes.
IoT devices utilize their own sensors to gather useful information of the locale. The information is sent to the edge computing node over the network. The edge computing node performs preliminary processing on the acquired information by utilizing the computing capability of the edge computing node. The collected information is firstly processed at the edge computing node, so that the data volume transmitted to the cloud computing node can be effectively reduced, the transmission cost is reduced, and the safety of the data is ensured. Meanwhile, the response speed to key information can be improved, timely discovery and timely processing can be achieved. The cloud computing nodes are located in the cloud computing data center, are far away from the edge computing nodes, and delay and cost of network transmission are high. After the original information is processed by the edge computing node, only the processing result is sent to the cloud computing node, so that the transmission cost can be effectively reduced.
Fig. 4 is an overall framework of the present application, where IoT devices send collected information to edge computing nodes over a network for processing. The acquisition module in the edge computing node acquires the state information of the edge computing node in real time, and the specific information can comprise network flow, CPU utilization rate, disk IO flow, CPU load and the like. For network intensive tasks, changes in network traffic can significantly affect the load of edge computing nodes. The problem of task processing delay is caused by not lifting the CPU working frequency in time when the network flow becomes large. For such a scenario, disk IO traffic and CPU load data have less impact on evaluating computing node load, and need not be taken into account.
The control module combines the CPU utilization data serving as a reference with the network flow data to comprehensively determine the current working frequency of the edge computing node.
Specifically, for a network intensive task scene, combining the reference CPU utilization rate data with the network flow rate data to correspond to the CPU working frequency, and obtaining the current working frequency of the edge computing node according to the duty ratio of the real-time CPU utilization rate data, the network flow rate data and the reference; for example, for CPU operating frequency 2.5Ghz, the application range of network traffic is set to 1Mbit/s-900Mbit/s, the range of CPU utilization is 50% -40%, for CPU operating frequency 2.4Ghz, the application range of network traffic is set to 899Kbit/s-800Kbit/s, the range of CPU utilization is 39% -30%, for CPU operating frequency 2.3Ghz, the application range of network traffic is set to 799Kbit/s-700Kbit/s, the range of CPU utilization is 29% -20%, and so on. The control module sets the CPU working frequency according to the current data of all the influencing factors according to the data acquired by the acquisition module in real time.
As shown in fig. 5, a third object of the present application is to provide an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the CPU frequency adjustment method of the edge computing node when executing the computer program.
The CPU frequency adjusting method of the edge computing node comprises the following steps:
acquiring all influence factors for controlling the working frequency of the CPU, wherein the influence factors are determined by different service scenes;
and setting an adaptation range of the influence factors for each influence factor according to the characteristics of the service scene, and dynamically setting the CPU working frequency according to the adaptation range.
A fourth object of the present application is to provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the CPU frequency adjustment method of the edge computing node.
The CPU frequency adjusting method of the edge computing node comprises the following steps:
acquiring all influence factors for controlling the working frequency of the CPU, wherein the influence factors are determined by different service scenes;
and setting an adaptation range of the influence factors for each influence factor according to the characteristics of the service scene, and dynamically setting the CPU working frequency according to the adaptation range.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.

Claims (6)

1. A method for adjusting the CPU frequency of an edge computing node, comprising the steps of:
acquiring all influence factors for controlling the working frequency of the CPU, wherein the influence factors are determined by different service scenes;
setting an adaptation range of the influence factors for each influence factor according to the characteristics of the service scene, and dynamically setting the CPU working frequency according to the adaptation range;
the influence factors comprise one or more of network flow, CPU utilization, disk IO flow, CPU load, hardware information or service information;
the influence factors are determined by different business scenes and specifically are as follows:
for network intensive tasks, taking network traffic as an influence factor of CPU frequency adjustment;
for a disk IO intensive task, taking the disk IO flow as an influence factor of CPU frequency adjustment;
for other edge computing scenes, taking hardware information or service information as an influence factor for determining CPU frequency;
the dynamically setting of the CPU working frequency according to the adaptive range is specifically as follows:
for a network intensive task scene, combining the reference CPU utilization rate data with the network flow rate data to correspond to the CPU working frequency, and obtaining the current working frequency of the edge computing node according to the duty ratio of the real-time CPU utilization rate data, the network flow rate data and the reference;
for a disk IO intensive task scene, setting an adaptation range of disk IO flow and CPU utilization rate for each CPU working frequency, and dynamically setting the current working frequency of the edge computing node according to the adaptation range.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the obtaining all the influence factors for controlling the CPU working frequency is obtained by the IoT device through acquisition by using own sensors; the acquired influence factors send information to the edge computing nodes through a network.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
and the set CPU working frequency is sent to the cloud computing node.
4. A CPU frequency adjustment system of an edge computing node, based on the method of any of claims 1 to 3, comprising:
the acquisition module is used for acquiring all influence factors for controlling the working frequency of the CPU, wherein the influence factors are determined by different service scenes;
the control module is used for setting the adaptation range of the influence factors for each influence factor according to the characteristics of the service scene, and dynamically setting the CPU working frequency according to the adaptation range.
5. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the CPU frequency adjustment method of the edge computing node of any of claims 1-3 when the computer program is executed.
6. A computer readable storage medium storing a computer program which when executed by a processor implements the steps of the CPU frequency adjustment method of an edge computing node of any of claims 1-3.
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