CN113608901A - NVMe SSD constant temperature reliability test method and device - Google Patents

NVMe SSD constant temperature reliability test method and device Download PDF

Info

Publication number
CN113608901A
CN113608901A CN202110722973.6A CN202110722973A CN113608901A CN 113608901 A CN113608901 A CN 113608901A CN 202110722973 A CN202110722973 A CN 202110722973A CN 113608901 A CN113608901 A CN 113608901A
Authority
CN
China
Prior art keywords
fio
temperature value
nvme ssd
workload
nvme
Prior art date
Legal status (The legal status 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 status listed.)
Granted
Application number
CN202110722973.6A
Other languages
Chinese (zh)
Other versions
CN113608901B (en
Inventor
刘迎
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Suzhou Inspur Intelligent Technology Co Ltd
Original Assignee
Suzhou Inspur Intelligent Technology Co Ltd
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 Suzhou Inspur Intelligent Technology Co Ltd filed Critical Suzhou Inspur Intelligent Technology Co Ltd
Priority to CN202110722973.6A priority Critical patent/CN113608901B/en
Publication of CN113608901A publication Critical patent/CN113608901A/en
Application granted granted Critical
Publication of CN113608901B publication Critical patent/CN113608901B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/008—Reliability or availability analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3037—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a memory, e.g. virtual memory, cache
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3051—Monitoring arrangements for monitoring the configuration of the computing system or of the computing system component, e.g. monitoring the presence of processing resources, peripherals, I/O links, software programs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3058—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Mathematical Physics (AREA)
  • Biomedical Technology (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention provides a method and a device for testing constant temperature reliability of NVMe SSD, wherein the method comprises the following steps: determining a relation model between the temperature value and the FIO working load through an artificial neural network algorithm; acquiring a required constant temperature value, and calculating a required FIO working load through a relation model of the temperature value and the FIO working load; the reliability test is carried out on the FIO workload required by the NVMe SSD operation through the FIO tool, the real-time temperature value is detected through the NVMe CLI tool at set time intervals in the test process, and the real-time FIO workload is adjusted according to the temperature value and the FIO workload relation model when the real-time temperature value is not matched with the required constant temperature value. According to the invention, the corresponding relation model of the temperature value of the NVMe SSD and the FIO work is determined through the artificial neural network algorithm, so that the reliability test of the NVMe SSD at constant temperature is realized, and the test complexity is increased.

Description

NVMe SSD constant temperature reliability test method and device
Technical Field
The invention belongs to the technical field of hard disk reliability testing, and particularly relates to a constant temperature reliability testing method and device for an NVMe SSD.
Background
NVMe is a Non-Volatile Memory, an abbreviation for Non-Volatile Memory express.
SSD is a Solid State Drive for short.
RDT is a short term for Reliability Tests, Reliability Tests.
FIO, an I/O tool used to stress test and verify hardware, supports 13 different I/O engines.
The existing NVMe SSD RDT test is that the temperature of an NVMe SSD disk is controlled by changing the ambient temperature, so that the reliability test of the NVMe SSD is carried out at a certain required constant temperature under the constant required temperature.
Therefore, it is very necessary to provide a method and an apparatus for testing reliability of NVMe SSD at constant temperature for overcoming the above-mentioned drawbacks in the prior art.
Disclosure of Invention
Aiming at the defects that in the prior art, the reliability test of the NVMe SSD to be tested is difficult to realize real constant temperature by controlling the NVMe SSD disk to be tested in a constant temperature mode through changing the ambient temperature, so that the reliability test result of the NVME SSD is inaccurate, the invention provides a constant-temperature reliability test method and a constant-temperature reliability test device for the NVMe SSD, so as to solve the technical problems.
In a first aspect, the invention provides an NVMe SSD constant temperature reliability test method, comprising the steps of:
s1, determining a relation model of an NVMe SSD temperature value and an FIO working load through an artificial neural network algorithm;
s2, acquiring a required constant temperature value of the NVMe SSD, and calculating a required FIO working load through a relation model of the NVMe SSD temperature value and the FIO working load;
s3, carrying out reliability test on the FIO workload required by the operation of the NVMe SSD through the FIO tool, detecting the real-time temperature value of the NVMe SSD through the NVMe CLI tool at set time intervals in the test process, and adjusting the real-time FIO workload according to a relation model between the NVMe SSD temperature value and the FIO workload when the real-time temperature value is not matched with the required constant temperature value.
Further, the step S1 specifically includes the following steps:
s11, creating a primary relation model of the NVMe SSD temperature value and the FIO workload based on an artificial neural network algorithm, taking the FIO parameter as an input layer, taking the FIO workload as a hidden layer, and taking the NVMe SSD temperature value as an output layer;
s12, acquiring a sample data set, and training a primary relation model of the NVMe SSD temperature value and the FIO workload through the sample data set to obtain a relation model of the NVMe SSD temperature value and the FIO workload. And creating a primary relation model, determining the input and the output of the primary relation model, and acquiring the temperature value and the data corresponding to the FIO working load in the working process of the NVMe SSD as a sample data set.
Further, the step S11 specifically includes the following steps:
s111, based on an artificial neural network algorithm, taking an FIO parameter as an input layer, taking an FIO working load as an implicit layer, taking an NVMe SSD temperature value as an output layer, and creating a primary relation model of the NVMe SSD temperature value and the FIO working load;
s112, setting the weight between the input layer and the hidden layer as a first weight, setting the weight between the hidden layer and the output layer as a second weight, and initializing the first weight and the second weight. And training the primary model through the sample set data, so that the values of the first weight and the second weight are continuously corrected until the threshold requirements of all layers are met, namely the model can reflect the real relation between the NVMe SSD temperature value and the FIO workload, and the model is trained completely.
Further, the FIO parameters include read/write mode, IO block size, number of working threads, and IO queue depth. Each set of FIO parameters corresponds to a FIO workload.
Further, the read-write mode comprises a sequential write mode, a sequential read mode, a random write mode, a random read mode, a sequential mixed read-write mode and a random mixed read-write mode;
the range of the IO block size is larger than 4K and smaller than the NVMe SSD capacity value;
the number of working threads is less than or equal to 100;
the IO queue depth is less than or equal to 4096. Setting a threshold value of the number of working threads based on the memory limit; the number of working threads and the depth of the IO queue are both integers. The IO block size is randomly selected from 4k to the SSD capacity value; considering the memory limitations of the test equipment, the range of the number of working threads: integer random value between 0 and 100, IO queue depth range: and the integer between 0 and 4096 takes random value.
Further, the step S12 specifically includes the following steps:
s121, acquiring a sample data set, wherein the sample data set comprises FIO working parameter data, FIO working load data and NVMe SSD temperature values of each sampling time point of the NVMe SSD;
and S122, sequentially inputting the concentrated data of the sample data into the primary relation model of the NVMe SSD temperature value and the FIO workload, and correcting the first weight and the second weight to obtain the relation model of the NVMe SSD temperature value and the FIO workload. A set of FIO operational parameter data corresponds to a FIO workload data. And continuously correcting the weight until the output meets the threshold requirements of each layer.
Further, the step S2 specifically includes the following steps:
s21, acquiring a required constant temperature value of the NVMe SSD according to the sample data set;
s22, inputting the required constant temperature value of the NVMe SSD into a relational model of the NVMe SSD temperature value and the FIO working load, and calculating the required FIO working load;
s23, adding the required constant temperature value and the required FIO workload of the NVMe SSD to the sample data set. Step S1 determines that the relationship model can determine the corresponding relationship between the NVMe SSD and the FIO workload, and inputs the required constant temperature value into the trained relationship model to obtain the required FIO load.
Further, the step S3 specifically includes the following steps:
s31, performing reliability test on the FIO working load required by the NVMe SSD operation through an FIO tool;
s32, monitoring a real-time temperature value of the NVMe SSD through an NVMe smart-log instruction of the NVMe CLI tool at each set interval time period;
s33, judging whether the difference value between the real-time temperature value and the required constant-temperature value exceeds a temperature threshold value or not;
if yes, go to step S34;
if not, returning to the step S32;
and S34, inputting the required temperature value into a relation model of the NVMe SSD temperature value and the FIO workload, calculating the required FIO workload, and returning to the step S31. In the reliability test process, the FIO load is not constant, so that the NVMe SSD temperature value also changes, a monitoring time interval needs to be set, and the temperature is detected, so that the constant temperature in the true sense is realized, and the reliability test effect is ensured.
In a second aspect, the invention provides a FIO-based NVMe SSD constant temperature reliability testing device, comprising:
the relation model determining module is used for determining a relation model between the NVMe SSD temperature value and the FIO workload through an artificial neural network algorithm;
the required FIO workload calculation module is used for acquiring a required constant temperature value of the NVMe SSD and calculating the required FIO workload according to a relation model of the NVMe SSD temperature value and the FIO workload;
the reliability testing module is used for carrying out reliability testing on the FIO workload required by the operation of the NVMe SSD through the FIO tool, detecting the real-time temperature value of the NVMe SSD through the NVMe CLI tool at set time intervals in the testing process, and adjusting the real-time FIO workload according to a relation model between the NVMe SSD temperature value and the FIO workload when the real-time temperature value is not matched with the required constant temperature value.
Further, the relational model determination module includes:
the primary relation model creating unit is used for creating a primary relation model of the NVMe SSD temperature value and the FIO workload based on an artificial neural network algorithm, taking the FIO parameters as an input layer, taking the FIO workload as a hidden layer and taking the NVMe SSD temperature value as an output layer;
and the model training unit is used for acquiring a sample data set, and training the primary relation model of the NVMe SSD temperature value and the FIO workload through the sample data set to obtain the relation model of the NVMe SSD temperature value and the FIO workload.
Further, the primary relationship model creation unit includes:
the primary model creating subunit is used for creating a primary relation model of the NVMe SSD temperature value and the FIO working load based on an artificial neural network algorithm, by taking the FIO parameters as an input layer, the FIO working load as an implicit layer and the NVMe SSD temperature value as an output layer;
and the model weight setting and initializing subunit is used for setting the weight between the input layer and the hidden layer as a first weight, setting the weight between the hidden layer and the output layer as a second weight, and initializing the first weight and the second weight.
Further, the FIO parameters include read/write mode, IO block size, number of working threads, and IO queue depth.
Further, the read-write mode comprises a sequential write mode, a sequential read mode, a random write mode, a random read mode, a sequential mixed read-write mode and a random mixed read-write mode;
the range of the IO block size is larger than 4K and smaller than the NVMe SSD capacity value;
the number of working threads is less than or equal to 100;
the IO queue depth is less than or equal to 4096. The IO block size is randomly selected from 4k to the SSD capacity value; considering the memory limitations of the test equipment, the range of the number of working threads: integer random value between 0 and 100, IO queue depth range: and the integer between 0 and 4096 takes random value.
Further, the model training unit includes:
a sample data set obtaining subunit, configured to obtain a sample data set, where the sample data set includes FIO working parameter data, FIO working load data, and NVMe SSD temperature values at each sampling time point of the NVMe SSD;
and the model training subunit is used for sequentially inputting the data in the sample data set into the primary relation model of the NVMe SSD temperature value and the FIO workload, and correcting the first weight and the second weight to obtain the relation model of the NVMe SSD temperature value and the FIO workload.
Further, the required FIO workload calculation module includes:
the required temperature calculation unit is used for acquiring a required constant temperature value of the NVMe SSD according to the sample data set;
the required load calculation unit is used for inputting the required constant temperature value of the NVMe SSD into a relation model of the NVMe SSD temperature value and the FIO workload, and calculating the required FIO workload;
a sample data adding unit to add the required thermostat value and the required FIO workload of the NVMe SSD to the sample data set.
Further, the reliability test module includes:
the reliability testing unit is used for carrying out reliability testing on the FIO working load required by the NVMe SSD operation through the FIO tool;
the temperature monitoring unit is used for monitoring the real-time temperature value of the NVMe SSD through the NVMe smart-log instruction of the NVMe CLI tool at each set interval time period;
the temperature judging unit is used for judging whether the difference value between the real-time temperature value and the required constant temperature value exceeds a temperature threshold value or not;
and the required load recalculation unit is used for inputting the required temperature value into the relation model of the NVMe SSD temperature value and the FIO workload when the temperature difference value exceeds the temperature threshold value, and calculating the required FIO workload.
The beneficial effect of the invention is that,
according to the constant-temperature reliability testing method and device for the NVMe SSD, the corresponding relation model of the temperature value of the NVMe SSD and the FIO work is determined by the machine-learned artificial neural network algorithm, and then the reliability test of the NVMe SSD at a certain constant temperature is realized, so that the test complexity of the NVMe SSD is increased, the application scenes of the scheme are rich, and the RDT reliability test is greatly optimized and improved.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a first schematic flow chart of the NVMe SSD constant temperature reliability test method of the present invention.
FIG. 2 is a schematic flow chart of a NVMe SSD constant temperature reliability test method of the present invention.
FIG. 3 is a schematic diagram of an NVMe SSD constant temperature reliability testing device of the present invention.
In the figure, 1-relation model determination module; 1.1-a primary relational model creation unit; 1.2-a model training unit; 2-required FIO workload calculation module; 2.1-required temperature calculation unit; 2.2-required load calculation unit; 2.3-sample data increment unit; 3-a reliability test module; 3.1-reliability test unit; 3.2-temperature monitoring unit; 3.3-temperature judging unit; 3.4-required load recalculation unit.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
as shown in fig. 1, the invention provides a FIO-based NVMe SSD constant temperature reliability test method, comprising the following steps:
s1, determining a relation model of an NVMe SSD temperature value and an FIO working load through an artificial neural network algorithm;
s2, acquiring a required constant temperature value of the NVMe SSD, and calculating a required FIO working load through a relation model of the NVMe SSD temperature value and the FIO working load;
s3, carrying out reliability test on the FIO workload required by the operation of the NVMe SSD through the FIO tool, detecting the real-time temperature value of the NVMe SSD through the NVMe CLI tool at set time intervals in the test process, and adjusting the real-time FIO workload according to a relation model between the NVMe SSD temperature value and the FIO workload when the real-time temperature value is not matched with the required constant temperature value.
Example 2:
as shown in fig. 2, the invention provides a FIO-based NVMe SSD constant temperature reliability test method, comprising the following steps:
s1, determining a relation model of an NVMe SSD temperature value and an FIO working load through an artificial neural network algorithm; the method comprises the following specific steps:
s11, creating a primary relation model of the NVMe SSD temperature value and the FIO workload based on an artificial neural network algorithm, taking the FIO parameter as an input layer, taking the FIO workload as a hidden layer, and taking the NVMeSSD temperature value as an output layer; the FIO parameters comprise a read-write mode, IO block size, working thread number and IO queue depth; the read-write mode comprises a sequential write mode, a sequential read mode, a random write mode, a random read mode, a sequential mixed read-write mode and a random mixed read-write mode; the range of the IO block size is larger than 4K and smaller than the NVMe SSD capacity value; the number of working threads is less than or equal to 100; the depth of the IO queue is less than or equal to 4096;
s12, acquiring a sample data set, and training a primary relation model of the NVMe SSD temperature value and the FIO workload through the sample data set to obtain a relation model of the NVMe SSD temperature value and the FIO workload;
s2, acquiring a required constant temperature value of the NVMe SSD, and calculating a required FIO working load through a relation model of the NVMe SSD temperature value and the FIO working load; the method comprises the following specific steps:
s21, acquiring a required constant temperature value of the NVMe SSD according to the sample data set;
s22, inputting the required constant temperature value of the NVMe SSD into a relational model of the NVMe SSD temperature value and the FIO working load, and calculating the required FIO working load;
s23, adding the required constant temperature value and the required FIO working load of the NVMe SSD into a sample data set;
s3, performing reliability test on the FIO workload required by the operation of the NVMe SSD through an FIO tool, detecting the real-time temperature value of the NVMe SSD through an NVMe CLI tool at set time intervals in the test process, and adjusting the real-time FIO workload according to a relation model between the NVMe SSD temperature value and the FIO workload when the real-time temperature value is not matched with the required constant temperature value; the method comprises the following specific steps:
s31, performing reliability test on the FIO working load required by the NVMe SSD operation through an FIO tool;
s32, monitoring a real-time temperature value of the NVMe SSD through an NVMe smart-log instruction of the NVMe CLI tool at each set interval time period;
s33, judging whether the difference value between the real-time temperature value and the required constant-temperature value exceeds a temperature threshold value or not;
if yes, go to step S34;
if not, returning to the step S32;
s34, inputting the required temperature value into a relation model of the NVMe SSD temperature value and the FIO working load,
the required FIO workload is calculated and the process returns to step S31.
Example 3:
as shown in fig. 2, the invention provides a NVMe SSD constant temperature reliability test method, comprising the following steps:
s1, determining a relation model of an NVMe SSD temperature value and an FIO working load through an artificial neural network algorithm; the method comprises the following specific steps:
s11, creating a primary relation model of the NVMe SSD temperature value and the FIO workload based on an artificial neural network algorithm, taking the FIO parameter as an input layer, taking the FIO workload as a hidden layer, and taking the NVMeSSD temperature value as an output layer; the method comprises the following specific steps:
s111, based on an artificial neural network algorithm, taking an FIO parameter as an input layer, taking an FIO working load as an implicit layer, taking an NVMe SSD temperature value as an output layer, and creating a primary relation model of the NVMe SSD temperature value and the FIO working load; the FIO parameters comprise a read-write mode, IO block size, working thread number and IO queue depth; the read-write mode comprises a sequential write mode, a sequential read mode, a random write mode, a random read mode, a sequential mixed read-write mode and a random mixed read-write mode; the range of the IO block size is larger than 4K and smaller than the NVMe SSD capacity value; the number of working threads is less than or equal to 100; the depth of the IO queue is less than or equal to 4096;
s112, setting the weight between the input layer and the hidden layer as a first weight, setting the weight between the hidden layer and the output layer as a second weight, and initializing the first weight and the second weight;
s12, acquiring a sample data set, and training a primary relation model of the NVMe SSD temperature value and the FIO workload through the sample data set to obtain a relation model of the NVMe SSD temperature value and the FIO workload; the method comprises the following specific steps:
s121, acquiring a sample data set, wherein the sample data set comprises FIO working parameter data, FIO working load data and NVMe SSD temperature values of each sampling time point of the NVMe SSD;
s122, sequentially inputting the concentrated data of the sample data into a primary relation model of the NVMe SSD temperature value and the FIO workload, and correcting the first weight and the second weight to obtain a relation model of the NVMe SSD temperature value and the FIO workload;
s2, acquiring a required constant temperature value of the NVMe SSD, and calculating a required FIO working load through a relation model of the NVMe SSD temperature value and the FIO working load; the method comprises the following specific steps:
s21, acquiring a required constant temperature value of the NVMe SSD according to the sample data set;
s22, inputting the required constant temperature value of the NVMe SSD into a relational model of the NVMe SSD temperature value and the FIO working load, and calculating the required FIO working load;
s23, adding the required constant temperature value and the required FIO working load of the NVMe SSD into a sample data set;
s3, performing reliability test on the FIO workload required by the operation of the NVMe SSD through an FIO tool, detecting the real-time temperature value of the NVMe SSD through an NVMe CLI tool at set time intervals in the test process, and adjusting the real-time FIO workload according to a relation model between the NVMe SSD temperature value and the FIO workload when the real-time temperature value is not matched with the required constant temperature value; the method comprises the following specific steps:
s31, performing reliability test on the FIO working load required by the NVMe SSD operation through an FIO tool;
s32, monitoring a real-time temperature value of the NVMe SSD through an NVMe smart-log instruction of the NVMe CLI tool at each set interval time period;
s33, judging whether the difference value between the real-time temperature value and the required constant-temperature value exceeds a temperature threshold value or not;
if yes, go to step S34;
if not, returning to the step S32;
and S34, inputting the required temperature value into a relation model of the NVMe SSD temperature value and the FIO workload, calculating the required FIO workload, and returning to the step S31.
In the above embodiment 3, in step S11, a primary relationship model of NVMe SSD temperature values and FIO workload is created based on an artificial neural network algorithm, with FIO parameters as an input layer, FIO workload as an implicit layer, and NVMe SSD temperature values as an output layer, assuming that the numbers of units of the input layer, the implicit layer, and the output layer are I, J and K, respectively, and the input is (x)1,x2，。。。，xI-1) The hidden layer output is (z)1,z2，。。。，zJ-1) The net actual output is (y)1,y2，。。。，yK-1)，(d1,d2，。。。，dK-1) Representing an expected output of the training sample;
suppose the weight from input layer unit i to hidden layer unit j is vijThe weight from the hidden layer unit j to the output layer unit k is wjkBy PjAnd PkTo represent the thresholds of the hidden layer unit and the output layer unit, respectively;
in the information forward propagation stage, the output value of the model hidden layer unit is:
Figure BDA0003137100040000121
the output value of each unit of the output layer is as follows:
Figure BDA0003137100040000122
and (3) an error back propagation stage:
if the activation function of each layer of the artificial neural network is a unipolar sigmoid function, namely:
Figure BDA0003137100040000123
the weight value correction quantity between each layer of the model can be conveniently calculated;
for weight value adjustment between the output layer and the hidden layer:
Δwjk＝ηOj(dk-Ok)f'(netk)＝ηOj(dk-Ok)Ok(1-Ok)
for weight value adjustment between the hidden layer and the input layer:
Figure BDA0003137100040000131
according to Δ wjkThe first weight is adjusted according to DeltavijAnd adjusting the second weight to obtain a final relation model of the NVMe SSD temperature value and the FIO working load.
Example 4:
as shown in fig. 3, the present invention provides an NVMe SSD constant temperature reliability test apparatus, including:
the relation model determining module 1 is used for determining a relation model between an NVMe SSD temperature value and an FIO working load through an artificial neural network algorithm;
the required FIO workload calculation module 2 is used for acquiring a required constant temperature value of the NVMe SSD and calculating the required FIO workload according to a relation model of the NVMe SSD temperature value and the FIO workload;
and the reliability test module 3 is used for performing reliability test on the FIO workload required by the operation of the NVMe SSD through the FIO tool, detecting the real-time temperature value of the NVMe SSD through the NVMe CLI tool at set time intervals in the test process, and adjusting the real-time FIO workload according to a relation model between the NVMe SSD temperature value and the FIO workload when the real-time temperature value is not matched with the required constant temperature value.
Example 5:
as shown in fig. 3, the present invention provides an NVMe SSD constant temperature reliability test apparatus, including:
the relation model determining module 1 is used for determining a relation model between an NVMe SSD temperature value and an FIO working load through an artificial neural network algorithm; the relational model determination module 1 includes:
a primary relationship model creating unit 1.1, configured to create a primary relationship model of the NVMe SSD temperature value and the FIO workload based on an artificial neural network algorithm, with the FIO parameter as an input layer, the FIO workload as a hidden layer, and the NVMe SSD temperature value as an output layer;
the model training unit 1.2 is used for acquiring a sample data set, and training a primary relation model of the NVMe SSD temperature value and the FIO workload through the sample data set to obtain a relation model of the NVMe SSD temperature value and the FIO workload;
the required FIO workload calculation module 2 is used for acquiring a required constant temperature value of the NVMe SSD and calculating the required FIO workload according to a relation model of the NVMe SSD temperature value and the FIO workload; the required FIO workload calculation module 2 includes:
a required temperature calculation unit 2.1, configured to obtain a required constant temperature value of the NVMe SSD according to the sample data set;
a required load calculation unit 2.2, configured to input the required constant temperature value of the NVMe SSD into a relationship model between the NVMe SSD temperature value and the FIO workload, and calculate the required FIO workload;
a sample data adding unit 2.3, configured to add the required constant temperature value and the required FIO workload of the NVMe SSD to the sample data set;
the reliability testing module 3 is used for performing reliability testing on the FIO workload required by the operation of the NVMe SSD through the FIO tool, detecting the real-time temperature value of the NVMe SSD through the NVMe CLI tool at set time intervals in the testing process, and adjusting the real-time FIO workload according to a relation model between the NVMe SSD temperature value and the FIO workload when the real-time temperature value is not matched with the required constant temperature value; the reliability test module 3 includes:
the reliability testing unit 3.1 is used for performing reliability testing on the FIO working load required by the NVMe SSD operation through an FIO tool;
the temperature monitoring unit 3.2 is used for monitoring the real-time temperature value of the NVMe SSD through the NVMe smart-log instruction of the NVMe CLI tool at each set interval time period;
the temperature judging unit 3.3 is used for judging whether the difference value between the real-time temperature value and the required constant temperature value exceeds a temperature threshold value or not;
and the required load recalculation unit 3.4 is used for inputting the required temperature value into the relation model of the NVMe SSD temperature value and the FIO workload when the temperature difference value exceeds the temperature threshold value, and calculating the required FIO workload.
Example 6:
as shown in fig. 3, the present invention provides an NVMe SSD constant temperature reliability test apparatus, including:
the relation model determining module 1 is used for determining a relation model between an NVMe SSD temperature value and an FIO working load through an artificial neural network algorithm; the relational model determination module 1 includes:
a primary relationship model creating unit 1.1, configured to create a primary relationship model of the NVMe SSD temperature value and the FIO workload based on an artificial neural network algorithm, with the FIO parameter as an input layer, the FIO workload as a hidden layer, and the NVMe SSD temperature value as an output layer; the primary relational model creation unit 1.1 includes:
the primary model creating subunit is used for creating a primary relation model of the NVMe SSD temperature value and the FIO working load based on an artificial neural network algorithm, by taking the FIO parameters as an input layer, the FIO working load as an implicit layer and the NVMe SSD temperature value as an output layer;
the model weight setting and initializing subunit is used for setting the weight between the input layer and the hidden layer as a first weight, setting the weight between the hidden layer and the output layer as a second weight and initializing the first weight and the second weight;
the model training unit 1.2 is used for acquiring a sample data set, and training a primary relation model of the NVMe SSD temperature value and the FIO workload through the sample data set to obtain a relation model of the NVMe SSD temperature value and the FIO workload; the model training unit 1.2 comprises:
a sample data set obtaining subunit, configured to obtain a sample data set, where the sample data set includes FIO working parameter data, FIO working load data, and NVMe SSD temperature values at each sampling time point of the NVMe SSD;
the model training subunit is used for sequentially inputting the data in the sample data set into a primary relation model of the NVMe SSD temperature value and the FIO workload, and correcting the first weight and the second weight to obtain a relation model of the NVMe SSD temperature value and the FIO workload;
the required FIO workload calculation module 2 is used for acquiring a required constant temperature value of the NVMe SSD and calculating the required FIO workload according to a relation model of the NVMe SSD temperature value and the FIO workload; the required FIO workload calculation module 2 includes:
a required temperature calculation unit 2.1, configured to obtain a required constant temperature value of the NVMe SSD according to the sample data set;
a required load calculation unit 2.2, configured to input the required constant temperature value of the NVMe SSD into a relationship model between the NVMe SSD temperature value and the FIO workload, and calculate the required FIO workload;
a sample data adding unit 2.3, configured to add the required constant temperature value and the required FIO workload of the NVMe SSD to the sample data set;
the reliability testing module 3 is used for performing reliability testing on the FIO workload required by the operation of the NVMe SSD through the FIO tool, detecting the real-time temperature value of the NVMe SSD through the NVMe CLI tool at set time intervals in the testing process, and adjusting the real-time FIO workload according to a relation model between the NVMe SSD temperature value and the FIO workload when the real-time temperature value is not matched with the required constant temperature value; the reliability test module 3 includes:
the reliability testing unit 3.1 is used for performing reliability testing on the FIO working load required by the NVMe SSD operation through an FIO tool;
the temperature monitoring unit 3.2 is used for monitoring the real-time temperature value of the NVMe SSD through the NVMe smart-log instruction of the NVMe CLI tool at each set interval time period;
the temperature judging unit 3.3 is used for judging whether the difference value between the real-time temperature value and the required constant temperature value exceeds a temperature threshold value or not;
and the required load recalculation unit 3.4 is used for inputting the required temperature value into the relation model of the NVMe SSD temperature value and the FIO workload when the temperature difference value exceeds the temperature threshold value, and calculating the required FIO workload.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (10)

1. A NVMe SSD constant temperature reliability test method is characterized by comprising the following steps:
s1, determining a relation model of an NVMe SSD temperature value and an FIO working load through an artificial neural network algorithm;
s2, acquiring a required constant temperature value of the NVMe SSD, and calculating a required FIO working load through a relation model of the NVMe SSD temperature value and the FIO working load;
s3, carrying out reliability test on the FIO workload required by the operation of the NVMe SSD through the FIO tool, detecting the real-time temperature value of the NVMe SSD through the NVMe CLI tool at set time intervals in the test process, and adjusting the real-time FIO workload according to a relation model between the NVMe SSD temperature value and the FIO workload when the real-time temperature value is not matched with the required constant temperature value.
2. The NVMe SSD constant temperature reliability test method of claim 1, wherein the step S1 comprises the following steps:
s11, creating a primary relation model of the NVMe SSD temperature value and the FIO workload based on an artificial neural network algorithm, taking the FIO parameter as an input layer, taking the FIO workload as a hidden layer, and taking the NVMe SSD temperature value as an output layer;
s12, acquiring a sample data set, and training a primary relation model of the NVMe SSD temperature value and the FIO workload through the sample data set to obtain a relation model of the NVMe SSD temperature value and the FIO workload.
3. The NVMe SSD constant temperature reliability test method of claim 2, wherein the step S11 comprises the following steps:
s111, based on an artificial neural network algorithm, taking an FIO parameter as an input layer, taking an FIO working load as an implicit layer, taking an NVMe SSD temperature value as an output layer, and creating a primary relation model of the NVMe SSD temperature value and the FIO working load;
s112, setting the weight between the input layer and the hidden layer as a first weight, setting the weight between the hidden layer and the output layer as a second weight, and initializing the first weight and the second weight.
4. The NVMe SSD constant temperature reliability test method of claim 3, wherein the step S12 comprises the following steps:
s121, acquiring a sample data set, wherein the sample data set comprises FIO working parameter data, FIO working load data and NVMe SSD temperature values of each sampling time point of the NVMe SSD;
and S122, sequentially inputting the concentrated data of the sample data into the primary relation model of the NVMe SSD temperature value and the FIO workload, and correcting the first weight and the second weight to obtain the relation model of the NVMe SSD temperature value and the FIO workload.
5. The NVMe SSD constant temperature reliability test method of claim 4, wherein the step S2 comprises the following steps:
s21, acquiring a required constant temperature value of the NVMe SSD according to the sample data set;
s22, inputting the required constant temperature value of the NVMe SSD into a relational model of the NVMe SSD temperature value and the FIO working load, and calculating the required FIO working load;
s23, adding the required constant temperature value and the required FIO workload of the NVMe SSD to the sample data set.
6. The NVMe SSD constant temperature reliability test method of claim 1, wherein the step S3 comprises the following steps:
s31, performing reliability test on the FIO working load required by the NVMe SSD operation through an FIO tool;
s32, monitoring a real-time temperature value of the NVMe SSD through an NVMe smart-log instruction of the NVMe CLI tool at each set interval time period;
s33, judging whether the difference value between the real-time temperature value and the required constant-temperature value exceeds a temperature threshold value or not;
if yes, go to step S34;
if not, returning to the step S32;
and S34, inputting the required temperature value into a relation model of the NVMe SSD temperature value and the FIO workload, calculating the required FIO workload, and returning to the step S31.
7. The utility model provides a NVMe SSD constant temperature reliability testing arrangement which characterized in that includes:
the relation model determining module (1) is used for determining a relation model between the NVMe SSD temperature value and the FIO workload through an artificial neural network algorithm;
the required FIO workload calculation module (2) is used for acquiring a required constant temperature value of the NVMe SSD and calculating a required FIO workload according to a relation model of a NVMe SSD temperature value and the FIO workload;
the reliability testing module (3) is used for performing reliability testing on the FIO workload required by the operation of the NVMe SSD through the FIO tool, detecting the real-time temperature value of the NVMe SSD through the NVMe CLI tool at set time intervals in the testing process, and adjusting the real-time FIO workload according to a relation model between the NVMe SSD temperature value and the FIO workload when the real-time temperature value is not matched with the required constant temperature value.
8. The NVMe SSD constant temperature reliability test device of claim 7, wherein the relational model determination module (1) comprises:
a primary relation model creating unit (1.1) for creating a primary relation model of the NVMe SSD temperature value and the FIO workload based on an artificial neural network algorithm, wherein the FIO parameter is used as an input layer, the FIO workload is used as an implied layer, and the NVMe SSD temperature value is used as an output layer;
and the model training unit (1.2) is used for acquiring a sample data set, and training a primary relation model of the NVMe SSD temperature value and the FIO workload through the sample data set to obtain a relation model of the NVMe SSD temperature value and the FIO workload.
9. The NVMe SSD constant temperature reliability test device of claim 7, wherein the required FIO workload calculation module (2) comprises:
a required temperature calculation unit (2.1) for acquiring a required constant temperature value of the NVMe SSD according to the sample data set;
a required load calculation unit (2.2) for inputting the required constant temperature value of the NVMe SSD into a relational model of the NVMe SSD temperature value and the FIO workload, and calculating the required FIO workload;
a sample data add unit (2.3) for adding the required thermostat value and the required FIO workload of the NVMe SSD to the sample data set.
10. The NVMe SSD constant temperature reliability test device of claim 7, wherein the reliability test module (3) comprises:
the reliability testing unit (3.1) is used for carrying out reliability testing on the FIO workload required by the NVMe SSD operation through the FIO tool;
the temperature monitoring unit (3.2) is used for monitoring the real-time temperature value of the NVMe SSD through the NVMe smart-log instruction of the NVMe CLI tool at set time intervals;
the temperature judging unit (3.3) is used for judging whether the difference value between the real-time temperature value and the required constant temperature value exceeds a temperature threshold value or not;
and the required load recalculation unit (3.4) is used for inputting the required temperature value into the relation model of the NVMe SSD temperature value and the FIO workload when the temperature difference value exceeds the temperature threshold value, and calculating the required FIO workload.
CN202110722973.6A 2021-06-28 2021-06-28 NVMe SSD constant temperature reliability test method and device Active CN113608901B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110722973.6A CN113608901B (en) 2021-06-28 2021-06-28 NVMe SSD constant temperature reliability test method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110722973.6A CN113608901B (en) 2021-06-28 2021-06-28 NVMe SSD constant temperature reliability test method and device

Publications (2)

Publication Number Publication Date
CN113608901A true CN113608901A (en) 2021-11-05
CN113608901B CN113608901B (en) 2023-07-18

Family

ID=78303830

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110722973.6A Active CN113608901B (en) 2021-06-28 2021-06-28 NVMe SSD constant temperature reliability test method and device

Country Status (1)

Country Link
CN (1) CN113608901B (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7873885B1 (en) * 2004-01-20 2011-01-18 Super Talent Electronics, Inc. SSD test systems and methods
CN111696618A (en) * 2020-05-15 2020-09-22 苏州浪潮智能科技有限公司 Method and system for testing power-on and power-off stability of SSD (solid State disk)

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7873885B1 (en) * 2004-01-20 2011-01-18 Super Talent Electronics, Inc. SSD test systems and methods
CN111696618A (en) * 2020-05-15 2020-09-22 苏州浪潮智能科技有限公司 Method and system for testing power-on and power-off stability of SSD (solid State disk)

Also Published As

Publication number Publication date
CN113608901B (en) 2023-07-18

Similar Documents

Publication Publication Date Title
US20210383041A1 (en) In-situ thermodynamic model training
CN112686464A (en) Short-term wind power prediction method and device
CN111860982A (en) Wind power plant short-term wind power prediction method based on VMD-FCM-GRU
JP6404909B2 (en) How to calculate the output model of a technical system
CN107977710A (en) Multiplexing electric abnormality data detection method and device
CN111814342B (en) Complex equipment reliability hybrid model and construction method thereof
CN112214880B (en) Multi-stage maintainability assessment method based on virtual-real fusion
CN106568647B (en) A kind of Strength Forecast of Concrete method neural network based
CN112163335A (en) Training method, prediction method and device of NOx concentration prediction model
CN113983646A (en) Air conditioner interaction end energy consumption prediction method based on generation countermeasure network and air conditioner
CN116089870A (en) Industrial equipment fault prediction method and device based on meta-learning under small sample condition
CN113486591A (en) Gravity multi-parameter data density weighted inversion method for convolutional neural network result
CN113608901A (en) NVMe SSD constant temperature reliability test method and device
CN109240927B (en) GDE 3-based multi-stage dynamic multi-target test resource allocation method
CN116303786A (en) Block chain financial big data management system based on multidimensional data fusion algorithm
CN116292057A (en) Control method, device, equipment and medium for variable-speed pumped storage unit
EP3955075A1 (en) Engine model construction method, program, and apparatus
CN115057006A (en) Distillation strategy evaluation method, device and medium based on reinforcement learning
CN112685954B (en) Method and device for predicting wind speed and fan rotating speed of automobile environment wind tunnel
CN104200269A (en) Real-time fault diagnosis method based on online learning minimum embedding dimension network
Kuznetsova Some Features of Quality Improvement of the Neural Network Identifying an Aeroengine Low-emission Combustion Chamber
CN113255887A (en) Radar error compensation method and system based on genetic algorithm optimization BP neural network
Wang et al. A bent line Tobit regression model with application to household financial assets
CN113159395A (en) Deep learning-based sewage treatment plant water inflow prediction method and system
CN113705932B (en) Short-term load prediction method and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant