WO2021128084A1 - Traitement de données, acquisition, apprentissage de modèle et procédés de commande de consommation d'énergie, système et dispositif - Google Patents

Traitement de données, acquisition, apprentissage de modèle et procédés de commande de consommation d'énergie, système et dispositif Download PDF

Info

Publication number
WO2021128084A1
WO2021128084A1 PCT/CN2019/128400 CN2019128400W WO2021128084A1 WO 2021128084 A1 WO2021128084 A1 WO 2021128084A1 CN 2019128400 W CN2019128400 W CN 2019128400W WO 2021128084 A1 WO2021128084 A1 WO 2021128084A1
Authority
WO
WIPO (PCT)
Prior art keywords
power consumption
processing unit
measurement information
frequency
inference model
Prior art date
Application number
PCT/CN2019/128400
Other languages
English (en)
Chinese (zh)
Inventor
王加龙
张云
朱昊
李栈
任志星
宋军
Original Assignee
阿里巴巴集团控股有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 阿里巴巴集团控股有限公司 filed Critical 阿里巴巴集团控股有限公司
Priority to PCT/CN2019/128400 priority Critical patent/WO2021128084A1/fr
Publication of WO2021128084A1 publication Critical patent/WO2021128084A1/fr

Links

Images

Classifications

    • 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

Definitions

  • This application relates to the field of computer technology, and in particular to a method, system, and equipment for data processing, acquisition, model training, and power consumption control.
  • the embodiments of the present application provide a data processing, acquisition, model training, and power consumption control method, system, and equipment to solve or improve the problems in the prior art.
  • a data processing method includes:
  • the frequency of the processing unit of the first device is determined.
  • a data processing system in another embodiment, includes:
  • the first device is used to generate measurement information during work
  • the first management device is configured to obtain measurement information related to the first device, and determine the frequency of the processing unit of the first device according to the measurement information.
  • a data processing method includes:
  • the measurement information is used as an input of an inference model, and the inference model is executed to obtain the processing unit frequency of the first device.
  • a data acquisition method includes:
  • the processing unit frequency and the measurement information are used as a sample pair in the training samples used to train the inference model to be trained.
  • model training methods include:
  • the training samples including multiple sample pairs, the sample pairs including test information and processing unit frequencies corresponding to the test information;
  • the trained inference model is used to determine the frequency of the processing unit of the first device according to the measurement information with the first device.
  • test system includes:
  • the second device for testing including the same hardware structure and performance as the first device, is used to run the test program to load the corresponding test load;
  • the second management device for testing is connected to the second device, and is used to obtain the processing unit frequency of the second device under the test load and the measurement information related to the second device; and the frequency of the processing unit And the measurement information is used as a sample pair in the training samples used to train the inference model to be trained.
  • a device power consumption control method includes:
  • the power consumption capping control for the first device is executed by using the first power consumption capping value after resetting.
  • an electronic device includes: a memory and a processor, among which,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the frequency of the processing unit of the first device is determined.
  • an electronic device includes: a memory and a processor, among which,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the measurement information is used as an input of an inference model, and the inference model is executed to obtain the processing unit frequency of the first device.
  • an electronic device includes: a memory and a processor, among which,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the processing unit frequency and the measurement information are used as a sample pair in the training samples used to train the inference model to be trained.
  • an electronic device includes: a memory and a processor, among which,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the training samples including multiple sample pairs, the sample pairs including test information and processing unit frequencies corresponding to the test information;
  • the trained inference model is used to determine the frequency of the processing unit of the first device according to the measurement information with the first device.
  • an electronic device includes: a memory and a processor, among which,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the power consumption capping control for the first device is executed by using the first power consumption capping value after resetting.
  • the inventor who implements the technical solutions provided by the embodiments of the present application discovered through a lot of creative work that the power of the processing unit of the device is related to measurement information, where the measurement information is measurable information related to the device.
  • the processing unit power mentioned in this article that is, the clock frequency of the processing unit, is simply the operating frequency of the processing unit during operation. Therefore, in the technical solution provided by an embodiment of the present application, measurement information related to the first device is acquired; and the processing unit frequency of the first device is determined based on the measurement information.
  • the processing unit frequency obtained by using the technical solution provided in this embodiment is more accurate than the existing processing unit frequency read from the BMC (baseboard management controller); Helps improve the power management capabilities of the device.
  • the self-learning ability of the training model is used to self-learn the correlation between the measurement information and the frequency of the processing unit; and then the inference model completed by the training is used to combine the measurement information related to the first device As an input of the inference model, the processing unit frequency of the first device is obtained by executing the inference model.
  • the processing unit frequency obtained by the technical solution provided in this embodiment has a higher accuracy rate.
  • the frequency of the processing unit of the second device under different load conditions and related measurement information is simulated by adding a load to the second device for testing, and the frequency of the processing unit and the measurement information are taken as
  • the training samples of the inferred model can learn more accurately the relationship between the measurement information and the frequency of the processing unit, thereby improving the calculation accuracy of the frequency of the processing unit.
  • the measurement information related to the first device is used to determine the processing unit frequency of the first device, and then based on the determined processing unit frequency, the power consumption capping control is reset.
  • the reference first power consumption cap value due to the high accuracy of the processing unit frequency, the accurate processing unit frequency is used to reset the first power consumption cap value, which is closer to the actual situation of the first device; it can be seen that the embodiment of the application is adopted.
  • FIG. 1 is a schematic flowchart of a data processing method provided by an embodiment of this application
  • FIG. 2 is a schematic structural diagram of a data processing system provided by an embodiment of this application.
  • FIG. 3 is a schematic flowchart of a data processing method provided by another embodiment of this application.
  • FIG. 4 is a schematic flowchart of a data acquisition method provided by an embodiment of the application.
  • FIG. 5 is a schematic flowchart of a model training method provided by an embodiment of this application.
  • FIG. 6 is a schematic structural diagram of a data processing system provided by another embodiment of this application.
  • FIG. 7 is a schematic diagram of a training sample generation process provided by an embodiment of the application.
  • FIG. 8 is a schematic diagram of an inference model training process provided by an embodiment of the application.
  • FIG. 9 is a schematic diagram of a real-time processing unit frequency inference process when using an inference model to perform power capping control on a server according to an embodiment of the application;
  • FIG. 10 is a schematic flowchart of a method for controlling power consumption of a device according to an embodiment of the application.
  • FIG. 11 is a schematic structural diagram of a data processing device provided by an embodiment of the application.
  • FIG. 12 is a schematic structural diagram of a data processing device provided by another embodiment of this application.
  • FIG. 13 is a schematic structural diagram of a data acquisition device provided by an embodiment of this application.
  • FIG. 14 is a schematic structural diagram of a model training device provided by an embodiment of the application.
  • 15 is a schematic structural diagram of a device power consumption control apparatus provided by an embodiment of the application.
  • FIG. 16 is a schematic structural diagram of an electronic device provided by an embodiment of the application.
  • the power consumption management in the prior art includes three main parts: setting a capping value, monitoring operating power consumption, and performing power capping actions. That is, first set the power consumption cap value of each rack server according to the power distribution requirements of the cabinet, the actual power consumption of the normal operation of the server, business pressure requirements, etc., and then write the cap value into the out-of-band management device as the upper limit power consumption of the server operation .
  • the out-of-band management device monitors the power consumption of the whole machine, and if it finds that the power consumption exceeds the capping value, it performs the capping action.
  • the out-of-band management device has limited access to the server status. For example, the out-of-band management device cannot accurately and directly obtain the frequency of the processing unit. In power management, it is crucial to accurately obtain the processing unit frequency.
  • each embodiment of the present application provides a solution to obtain a processing unit frequency with a higher accuracy rate, so as to perform power consumption management more accurately.
  • Fig. 1 shows a schematic flowchart of a data processing method provided by an embodiment of the present application. As shown in Figure 1, the data processing method includes:
  • the measurement information related to the first device can be understood as: all measurable information in the working process of the first device, including but not limited to: processing unit power consumption, processing unit temperature value, processing unit utilization Speed, motherboard temperature information, fan speed, etc.
  • the foregoing step 101 "obtain measurement information related to the first device" may include:
  • start power capping control In a case where the power consumption of the first device exceeds the first power capping threshold value (power capping threshold value), start power capping control.
  • the power consumption capping control can be simply understood as: monitoring the power consumption of the first device, and controlling the power consumption of the first device to not exceed the first power consumption capping value.
  • the processing unit frequency that is, the clock frequency of the processing unit, is simply the abbreviation of the operating frequency (the number of synchronization pulses generated in 1 second) of the processing unit during operation; it determines the operating speed of the computer.
  • step 102 determine the frequency of the processing unit of the first device according to the measurement information.
  • the "obtaining inference model" in the above step 1021 may specifically include:
  • training samples include: processing unit frequencies and measurement information corresponding to the processing unit frequency samples;
  • the inference model completes training
  • the inventor who implements the technical solutions provided by the embodiments of the present application discovered through a lot of creative work that the power of the processing unit of the device is related to measurement information, where the measurement information is measurable information related to the device. Therefore, in the technical solution provided by an embodiment of the present application, measurement information related to the first device is acquired; and the processing unit frequency of the first device is determined based on the measurement information. Practice has proved that the processing unit frequency obtained by using the technical solution provided in this embodiment is more accurate than the existing processing unit frequency read from the BMC (baseboard management controller); Helps improve the power management capabilities of the device.
  • BMC baseboard management controller
  • the method provided in this embodiment may further include the following steps:
  • the first preset condition may be: whether it is within a set value range. It is assumed that the value range is between a first preset value and a second preset value, where the first preset value is smaller than the second preset value.
  • the above step 103 "resetting the first power consumption cap value when the frequency of the processing unit does not meet the first preset condition" may specifically include:
  • the first preset value and the second preset value may be empirical values or values obtained through multiple experiments, etc., which are not specifically limited in this embodiment.
  • the increase and decrease of the first power consumption cap value may be determined based on a preset reset rule.
  • the reset rule is: each adjustment is increased or decreased by a fixed value or a fixed ratio.
  • the processing unit mentioned in this article can be a general-purpose processor, such as a CPU; it can also be a dedicated processor or a heterogeneous computing unit, such as DSP (Digital Signal Processing, digital signal processor), ASIC (dedicated Integrated circuit), GPU (Graphics Processing Unit, graphics processor), FPGA (Field-Programmable Gate Array, field programmable gate array), network card acceleration chip, etc.
  • DSP Digital Signal Processing, digital signal processor
  • ASIC dedicated Integrated circuit
  • GPU Graphics Processing Unit, graphics processor
  • FPGA Field-Programmable Gate Array, field programmable gate array
  • network card acceleration chip etc.
  • the data processing system includes:
  • the first device 201 is used to generate measurement information during work
  • the first management device 202 is configured to obtain measurement information related to the first device, and determine the frequency of the processing unit of the first device according to the measurement information.
  • the first management device 202 is further configured to send a power consumption capping control instruction to the first device when the power consumption of the first device exceeds the first power consumption cap value.
  • the first device 201 is also configured to perform a power capping operation according to the control instruction.
  • the first management device 202 is further configured to obtain measurement information related to the first device when the power consumption capping control is activated by the first device, and determine the first device according to the measurement information The processing unit frequency.
  • the first device is a server in a server cluster; the first management device is an out-of-band management device.
  • the server includes multiple pieces of hardware, including but not limited to the following: motherboard, processing unit, power consumption, temperature sensor, and fan.
  • the measurement information related to the first device acquired by the out-of-band management apparatus may include: power consumption of the processing unit, temperature value of the processing unit, utilization of the processing unit, temperature information of the main board, fan speed, and so on.
  • a first interface may be provided in the first device, and the first interface is used to connect to the BMC.
  • BMC can realize the collection function of part or all of the measurement information.
  • the first interface may include, but is not limited to: a USB interface and a PCI (Peripheral Component Interconnect) slot.
  • the out-of-band management implemented by the out-of-band management device may include: out-of-band management of the power consumption of the device according to the out-of-band information sent by the server (that is, all measurement information related to the first device that can be measured), and/or Hardware shared by at least one server, such as a fan, is managed out-of-band.
  • out-of-band management device provided in this embodiment can implement other functions in addition to the above functions.
  • out-of-band management device can implement other functions in addition to the above functions.
  • FIG. 3 shows a schematic flowchart of a data processing method provided by another embodiment of the present application. As shown in the figure, the data processing method includes:
  • the above step 301 "obtain measurement information related to the first device" may include:
  • start power consumption capping control In the case where the power consumption of the first device exceeds the first power consumption cap value, start power consumption capping control;
  • the measurement information related to the first device can be understood as: all the information that can be measured during the working process of the first device, including but not limited to: processing unit power consumption, processing unit temperature value, processing unit utilization rate, main board Temperature information, fan speed, etc.
  • step 302 "obtain an inference model that uses training samples to complete training” may include:
  • the inference model completes training
  • the inference model to be trained in this embodiment can be a neural network model in the prior art, such as a convolutional neural network CNN, a long short-term memory network LSTM, etc., which is not specifically limited in this embodiment .
  • the model training process can also refer to related content in the prior art.
  • FIG. 4 shows a schematic flowchart of a data acquisition method provided by an embodiment of the present application.
  • the second device in this embodiment is a test device, which has the same type of processing unit and other hardware as the actual server (that is, a server that needs to be used on site and requires power consumption management), and it has a processing unit that can read accurately Frequency in-band sensor.
  • the method includes:
  • processing unit frequency and the measurement information as a sample pair in the training samples used to train the inference model to be trained.
  • a test program such as a Benchmark tool
  • a Benchmark tool can be loaded and run on the second device.
  • the content of the Benchmark tool please refer to the prior art, which will not be repeated in this article.
  • the frequency of the processing unit can be obtained by an in-band sensor.
  • the measurement information related to the first device can be understood as: all the information that can be measured during the working process of the first device, including but not limited to: processing unit power consumption, processing unit temperature value, processing unit utilization rate, main board Temperature information, fan speed, etc.
  • the measurement information related to the first device is information that can be obtained by the out-of-band management device.
  • the method provided in this embodiment may further include the following steps:
  • step 402 obtaining the frequency of the processing unit of the second device under test load and measurement information related to the second device.
  • the "acquiring the frequency of the processing unit of the second device and the measurement information related to the second device" in the foregoing 4022 may specifically be:
  • test period is a period of time from when the power consumption capping control is activated to when the test load of the second device is loaded to a preset maximum load; or from when the power consumption capping control is activated to the first
  • the test load of the second device is loaded to the preset maximum load and continues for a period of time after the set duration.
  • the aforementioned continuous setting duration may be an empirical value, which is not specifically limited in this embodiment.
  • the method provided in this embodiment may further include the following steps:
  • the next round of testing returns to the above step 401 to repeat the above process again on the basis of resetting the first power consumption cap value to obtain a new sample pair.
  • the foregoing threshold may be equal to or greater than the rated power consumption of the second device.
  • Each round of resetting can increase a certain step length, this step length can be a fixed value, or it can be changed appropriately.
  • the rated power consumption of the second device (such as a server) is 500W
  • the first round of testing the first power consumption cap value is set to 350W
  • in the second round of testing set the first power consumption
  • the cap value is reset to 360W;..., in the Nth round of testing, the first cap value of power consumption is reset to 520W.
  • the last threshold can be a little higher than the device's rated power consumption of 500W, because the rated power consumption does not represent the maximum power consumption of the machine in actual operation.
  • the "resetting the first power consumption cap value" in the above steps 405 and 405' may specifically be:
  • the first power consumption cap value is updated to the fourth power consumption cap value.
  • FIG. 5 shows a schematic flowchart of a model training method provided by an embodiment of the present application.
  • the model training method includes:
  • the training sample includes a plurality of sample pairs, and the sample pairs include test information and a processing unit frequency corresponding to the test information.
  • the trained inference model is used to determine the frequency of the processing unit of the first device according to the measurement information with the first device.
  • test system includes:
  • the second test device 601 includes the same hardware structure and performance as the first device, and is used to run the test program to load the corresponding test load;
  • the second management device 602 for testing is connected to the second device, and is used to obtain the frequency of the processing unit of the second device under the test load and the measurement information related to the second device; The frequency and the measurement information are used as a sample pair in the training samples used to train the inference model to be trained.
  • the second management device for testing has the same hardware structure and functions as the first management device. In addition to this, it also has a function that the first management device does not have, that is, the frequency of the processing unit of the second device is acquired. In specific implementation, the frequency of the processing unit of the second device can be obtained through an in-band sensor.
  • the second management device 602 is further configured to set a first power consumption cap value for the second device; the test load is increased until the power consumption of the second device reaches the first power consumption cap value When, sending a power consumption capping control instruction to the second device;
  • the second device 601 is also configured to perform a power consumption capping operation according to the control instruction
  • the second management device 602 is further configured to obtain the processing unit frequency of the second device and the measurement information related to the second device when the power consumption capping control is activated by the second device;
  • the processing unit frequency and the measurement information are used as a sample pair in the training samples used to train the inference model to be trained.
  • test system may further include:
  • the model training device is used to obtain training samples, the training samples include multiple sample pairs, the sample pairs include test information and processing unit frequencies corresponding to the test information; based on the multiple samples, the inference model to be trained is trained for the first A management device provides an inference model for completing the training.
  • the second management device provided in this embodiment may implement other functions in addition to the above functions.
  • the second management device may implement other functions in addition to the above functions.
  • the first part is to generate training samples.
  • the second part is to train the inference model.
  • the third part is the real-time processing unit frequency inference when using the inference model to control the power capping of the server.
  • test server with the same hardware structure and performance as the server in the actual application scenario and a test out-of-band management device with the same hardware structure and function as the out-of-band management device in the actual application scenario.
  • the processing unit is the CPU.
  • the set threshold is equal to the rated power consumption of the test server or 120% of the rated power consumption.
  • the Benchmark can continue for a period of time to allow enough time to record multiple pairs of samples when the power consumption cap takes effect.
  • the data processing process can refer to the corresponding content in the prior art, which is not specifically limited in this embodiment.
  • a machine learning model or any regression equation (ie, the above-mentioned inference model to be trained) is used to establish the relationship between the frequency of the processing unit and the measurement information.
  • the training samples processed by the above S21 are used to train the inference model to be trained.
  • the training process can refer to the related content of the prior art, which is not specifically limited here.
  • the real-time processing unit frequency is inferred when the inference model is used to control the power capping of the server.
  • the measurement information includes at least: processing unit power, processing unit temperature value, processing unit usage rate, temperature information of the main board, fan speed, and so on.
  • the inferred processing unit frequency can be used as a basis for adjusting the existing power consumption cap value. For example, if the inferred frequency of the processing unit is too high, the existing power consumption cap value can be lowered; if the inferred frequency of the processing unit is too low, the existing power consumption cap value can be adjusted higher.
  • FIG. 10 shows a schematic flowchart of a method for controlling power consumption of a device according to an embodiment of the present application.
  • the device power consumption control method includes:
  • step 703 “resetting the first power consumption cap value based on the frequency of the processing unit” may specifically include:
  • the first preset value is less than the second preset value.
  • the above-mentioned first preset value and second preset value may be preset values, which may be empirical values, or calculated through corresponding algorithms, or obtained through multiple experiments, and so on.
  • the increase and decrease of the first power consumption cap value can be determined based on a preset reset rule.
  • the reset rule is: each adjustment is increased or decreased by a fixed value or a fixed ratio.
  • the method provided in the embodiment of the present application that is, the execution premise of each of the foregoing steps 701 may be: when the power consumption capping control for the first device is started, an action of triggering the measurement information related to the first device.
  • step 702 determine the frequency of the processing unit of the first device according to the measurement information.
  • the measurement information is used as the input of the inference model, and the inference model is executed to obtain the processing unit frequency.
  • the measurement information may include but is not limited to at least one of the following: processing unit power consumption, processing unit temperature value, and processing unit utilization rate.
  • the measurement information related to the first device is used to determine the frequency of the processing unit of the first device, and then based on the determined frequency of the processing unit, the first reference to be referenced during power capping control is reset.
  • Power consumption cap value due to the high accuracy of the processing unit frequency, the first power consumption cap value is reset with an accurate processing unit frequency, which is closer to the actual situation of the first device; it can be seen that the technology provided by the embodiment of this application is used.
  • the solution helps to improve the power management capability of the device.
  • FIG. 11 shows a schematic structural diagram of a data processing device provided by an embodiment of the present application.
  • the data processing device includes: an acquisition module 11 and a determination module 12.
  • the obtaining module 11 is used to obtain measurement information related to the first device;
  • the determining module 12 is used to determine the frequency of the processing unit of the first device according to the measurement information.
  • the acquisition module 11 is also used for:
  • the data processing device provided in this embodiment should include a reset module.
  • the reset module is configured to reset the first power consumption cap value when the frequency of the processing unit does not meet the first preset condition.
  • reset module is also used for:
  • the first preset value is smaller than the second preset value.
  • determining module 12 is also used for:
  • the measurement information is used as the input of the inference model, and the inference model is executed to obtain the processing unit frequency.
  • the acquisition module 11 is also used for:
  • training samples include: processing unit frequencies and measurement information corresponding to the processing unit frequency samples;
  • the inference model completes the training
  • the parameters in the inference model are optimized.
  • the measurement information includes at least one of the following: processing unit power consumption, processing unit temperature value, and processing unit utilization rate.
  • FIG. 12 shows a schematic structural diagram of a data processing device provided by another embodiment of the present application.
  • the data processing device includes: an acquisition module 21 and an inference module 22.
  • the acquisition module 21 is used to acquire measurement information related to the first device; and to acquire an inference model that uses training samples to complete training, wherein the training samples include multiple sample pairs, and the sample pairs include measurement information and a processing unit. frequency.
  • the inference module 22 is configured to use the measurement information as an input of an inference model, and execute the inference model to obtain the processing unit frequency of the first device.
  • the self-learning ability of the training model is used to self-learn the association relationship between the measurement information and the frequency of the processing unit; then the trained inference model is used to use the measurement information related to the first device as the inference
  • the input of the model, the frequency of the processing unit of the first device is obtained by executing the inferred model.
  • the processing unit frequency obtained by the technical solution provided in this embodiment has a higher accuracy rate.
  • the acquisition module 21 is also used for:
  • the acquisition module 21 is also used for:
  • the inference model completes the training
  • the parameters in the inference model are optimized; and the next training process is entered.
  • FIG. 13 shows a schematic structural diagram of a data acquisition device provided by an embodiment of the present application.
  • the data acquisition device includes: a loading module 31 and an acquisition module 32.
  • the loading module is used to increase the test load for the second device for testing.
  • the acquisition module is used to acquire the processing unit frequency of the second device under a test load and measurement information related to the second device; and use the processing unit frequency and the measurement information as the processing unit frequency and the measurement information to be used for training.
  • a sample pair in the training sample for training the inference model is used to acquire the processing unit frequency of the second device under a test load and measurement information related to the second device.
  • the second device for testing is added with a load to simulate the processing unit frequency of the second device under different load conditions and related measurement information, and the processing unit frequency and measurement information are used as the inference model.
  • the training samples can learn more accurately the relationship between the measurement information and the frequency of the processing unit, thereby improving the calculation accuracy of the frequency of the processing unit.
  • the data acquisition device provided in this embodiment may further include a setting module.
  • the setting module is used to set a first power consumption cap value for the second device.
  • the acquisition module is also used for:
  • the processing unit frequency of the second device and the measurement information related to the second device are acquired.
  • the acquisition module 32 is also used for:
  • the test period is a period of time from when the power consumption capping control is started to when the test load of the second device is loaded to a preset maximum load; or from when the power consumption capping control is started to the first
  • the test load of the second device is loaded to the preset maximum load and continues for a period of time after the set duration.
  • the data acquisition module provided in this embodiment may further include a reset module.
  • the reset module is used for:
  • reset module is also used for:
  • the first power consumption cap value is updated to the fourth power consumption cap value.
  • the data acquisition device provided in the foregoing embodiment can implement the technical solutions described in the foregoing method embodiments.
  • the specific implementation principles of the foregoing modules or units please refer to the corresponding content in the foregoing method embodiments. No longer.
  • FIG. 14 shows a schematic structural diagram of a model training device provided by an embodiment of the present application.
  • the model training device includes: an acquisition module 41 and a training module 42.
  • the acquisition module 41 is used to acquire training samples
  • the training samples include multiple sample pairs
  • the sample pairs include test information and processing unit frequencies corresponding to the test information.
  • the training module 42 is configured to train the inference model to be trained based on the multiple samples; wherein the trained inference model is used to determine the frequency of the processing unit of the first device according to the measurement information with the first device .
  • FIG. 15 shows a schematic structural diagram of an apparatus for controlling power consumption of a device according to an embodiment of the present application.
  • the device power consumption control device includes: an acquisition module 51, a determination module 52, a reset module 53 and an execution module 54.
  • the obtaining module 51 is used to obtain measurement information related to the first device;
  • the determining module 52 is used to determine the frequency of the processing unit of the first device according to the measurement information;
  • the reset module 53 is used to obtain measurement information based on the processing unit Frequency, reset the first power consumption cap value;
  • the execution module 54 is configured to use the reset first power consumption cap value to execute power consumption cap control for the first device.
  • reset module 53 is also used for:
  • the first preset value is smaller than the second preset value.
  • determining module 52 is also used for:
  • the measurement information is used as the input of the inference model, and the inference model is executed to obtain the processing unit frequency.
  • the measurement information includes at least one of the following: processing unit power consumption, processing unit temperature value, and processing unit utilization rate.
  • FIG. 16 shows a schematic structural diagram of an electronic device provided by an embodiment of the present application.
  • the electronic device includes: a memory 61 and a processor 62, where:
  • the memory 61 is used to store programs
  • the processor 62 is coupled with the memory 61, and is configured to execute the program stored in the memory 61 for:
  • the frequency of the processing unit of the first device is determined.
  • the aforementioned memory 61 may be configured to store other various data to support operations on the electronic device. Examples of such data include instructions for any application or method operating on the electronic device.
  • the memory 61 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable and Programmable read only memory (EPROM), programmable read only memory (PROM), read only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable and Programmable read only memory
  • PROM programmable read only memory
  • ROM read only memory
  • magnetic memory flash memory
  • flash memory magnetic or optical disk.
  • processor 62 executes the program in the memory 61, in addition to the above functions, it may also implement other functions. For details, please refer to the description of the previous embodiments.
  • the electronic device further includes: a communication component 63, a display 64, a power supply component 65, an audio component 66 and other components. Only some components are schematically shown in FIG. 16, which does not mean that the electronic device only includes the components shown in FIG. 16.
  • the structure of the electronic device is similar to the above-mentioned electronic device embodiment, which can be referred to as shown in FIG. 16 above.
  • the electronic device includes a memory and a processor, among which,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the measurement information is used as an input of an inference model, and the inference model is executed to obtain the processing unit frequency of the first device.
  • an embodiment of the present application also provides a computer-readable storage medium storing a computer program, which can implement the steps or functions of the data processing method provided by the foregoing embodiments when the computer program is executed by a computer.
  • the structure of the electronic device is similar to the above-mentioned electronic device embodiment, which can be referred to as shown in FIG. 16 above.
  • the electronic device includes a memory and a processor, among which,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the processing unit frequency and the measurement information are used as a sample pair in the training samples used to train the inference model to be trained.
  • an embodiment of the present application also provides a computer-readable storage medium storing a computer program, which when executed by a computer can implement the steps or functions of the data acquisition method provided in the foregoing embodiments.
  • the structure of the electronic device is similar to the above-mentioned electronic device embodiment, which can be referred to as shown in FIG.
  • the electronic device includes a memory and a processor, among which,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the training samples including multiple sample pairs, the sample pairs including test information and processing unit frequencies corresponding to the test information;
  • the trained inference model is used to determine the frequency of the processing unit of the first device according to the measurement information with the first device.
  • an embodiment of the present application also provides a computer-readable storage medium storing a computer program, which can implement the steps or functions of the model training method provided in the foregoing embodiments when the computer program is executed by a computer.
  • the structure of the electronic device is similar to the above-mentioned electronic device embodiment, which can be referred to as shown in FIG. 16 above.
  • the electronic device includes a memory and a processor, among which,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the power consumption capping control for the first device is executed by using the first power consumption capping value after resetting.
  • an embodiment of the present application also provides a computer-readable storage medium storing a computer program, which when executed by a computer can implement the steps or functions of the device power consumption control method provided by the foregoing embodiments.
  • the device embodiments described above are merely illustrative.
  • 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 it can be distributed to multiple network units.
  • Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments. Those of ordinary skill in the art can understand and implement it without creative work.
  • each implementation manner can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware.
  • the above technical solution essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic A disc, an optical disc, etc., include several instructions to make a computer first device (which may be a personal computer, a server, or a network first device, etc.) execute the methods described in each embodiment or some parts of the embodiment.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Debugging And Monitoring (AREA)
  • Power Sources (AREA)

Abstract

Traitement de données, acquisition, apprentissage de modèle et procédés de commande de consommation d'énergie, système et dispositif. Le procédé de traitement de données consiste à : acquérir des informations de mesure relatives à un premier dispositif (201) (101) ; et selon les informations de mesure, déterminer une fréquence d'unité de traitement du premier dispositif (201) (102). Par comparaison avec la fréquence d'unité de traitement existante lue à partir d'un BMC, la fréquence d'unité de traitement obtenue au moyen de ladite solution a une précision plus élevée, ce qui aide à améliorer la capacité de gestion de la consommation d'énergie de dispositifs.
PCT/CN2019/128400 2019-12-25 2019-12-25 Traitement de données, acquisition, apprentissage de modèle et procédés de commande de consommation d'énergie, système et dispositif WO2021128084A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2019/128400 WO2021128084A1 (fr) 2019-12-25 2019-12-25 Traitement de données, acquisition, apprentissage de modèle et procédés de commande de consommation d'énergie, système et dispositif

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2019/128400 WO2021128084A1 (fr) 2019-12-25 2019-12-25 Traitement de données, acquisition, apprentissage de modèle et procédés de commande de consommation d'énergie, système et dispositif

Publications (1)

Publication Number Publication Date
WO2021128084A1 true WO2021128084A1 (fr) 2021-07-01

Family

ID=76575081

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/128400 WO2021128084A1 (fr) 2019-12-25 2019-12-25 Traitement de données, acquisition, apprentissage de modèle et procédés de commande de consommation d'énergie, système et dispositif

Country Status (1)

Country Link
WO (1) WO2021128084A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014032250A1 (fr) * 2012-08-30 2014-03-06 华为终端有限公司 Procédé et dispositif de commande d'une unité centrale
CN104204825A (zh) * 2012-03-30 2014-12-10 英特尔公司 动态测量处理器中的功耗
CN107861606A (zh) * 2017-11-21 2018-03-30 北京工业大学 一种通过协调dvfs和任务映射的异构多核功率封顶方法
CN108599966A (zh) * 2018-03-13 2018-09-28 山东超越数控电子股份有限公司 一种网安设备功耗动态调整系统和方法
CN108615071A (zh) * 2018-05-10 2018-10-02 阿里巴巴集团控股有限公司 模型测试的方法及装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104204825A (zh) * 2012-03-30 2014-12-10 英特尔公司 动态测量处理器中的功耗
WO2014032250A1 (fr) * 2012-08-30 2014-03-06 华为终端有限公司 Procédé et dispositif de commande d'une unité centrale
CN107861606A (zh) * 2017-11-21 2018-03-30 北京工业大学 一种通过协调dvfs和任务映射的异构多核功率封顶方法
CN108599966A (zh) * 2018-03-13 2018-09-28 山东超越数控电子股份有限公司 一种网安设备功耗动态调整系统和方法
CN108615071A (zh) * 2018-05-10 2018-10-02 阿里巴巴集团控股有限公司 模型测试的方法及装置

Similar Documents

Publication Publication Date Title
US10587935B2 (en) System and method for automatically determining server rack weight
US11210172B2 (en) System and method for information handling system boot status and error data capture and analysis
EP3002677A1 (fr) Système et procédé pour gérer de multiples configurations par défaut du bios
US10296434B2 (en) Bus hang detection and find out
US20130166243A1 (en) Test device and method for testing stability of electronic devices
US9612641B2 (en) Adjusting the connection idle timeout in connection pools
WO2019052208A1 (fr) Procédé et appareil d'évaluation de mémoire
US10365996B2 (en) Performance-aware and reliability-aware data placement for n-level heterogeneous memory systems
KR101707096B1 (ko) 일반 호스트 기반 제어기 레이턴시 방법 및 장치
US10055366B2 (en) Method for data transmission and server for implementing the method
CN111581043A (zh) 服务器功耗的监控方法、装置和服务器
TWI709039B (zh) 伺服器及錯誤事件紀錄登載功能的控制方法
US9645873B2 (en) Integrated configuration management and monitoring for computer systems
CN106294364B (zh) 实现网络爬虫抓取网页的方法和装置
WO2021128084A1 (fr) Traitement de données, acquisition, apprentissage de modèle et procédés de commande de consommation d'énergie, système et dispositif
TWI611290B (zh) 伺服器機櫃監控方法
CN111901405A (zh) 一种多节点监控方法、装置、电子设备及存储介质
WO2019169727A1 (fr) Procédé et appareil de test de trafic de réseau, dispositif et support de stockage lisible par ordinateur
US10592383B2 (en) Technologies for monitoring health of a process on a compute device
CN107748711A (zh) 自动优化Storm并行度的方法、终端设备及存储介质
US8891515B2 (en) Method for node communication
CN113612624A (zh) 一种节点之间心跳处理方法及装置
US9794120B2 (en) Managing network configurations in a server system
CN104252400A (zh) 数据中心多节点管理系统及方法
CN115756982A (zh) 系统管理中断响应时长的测试方法及装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19957679

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19957679

Country of ref document: EP

Kind code of ref document: A1