WO2020253111A1 - Procédé et appareil d'expansion automatique pour un nœud de chaîne de blocs, et terminal d'exploitation et de maintenance et support d'informations - Google Patents

Procédé et appareil d'expansion automatique pour un nœud de chaîne de blocs, et terminal d'exploitation et de maintenance et support d'informations Download PDF

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Publication number
WO2020253111A1
WO2020253111A1 PCT/CN2019/120904 CN2019120904W WO2020253111A1 WO 2020253111 A1 WO2020253111 A1 WO 2020253111A1 CN 2019120904 W CN2019120904 W CN 2019120904W WO 2020253111 A1 WO2020253111 A1 WO 2020253111A1
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disk
capacity
node
remaining
remaining capacity
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PCT/CN2019/120904
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English (en)
Chinese (zh)
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张青亮
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深圳壹账通智能科技有限公司
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Publication of WO2020253111A1 publication Critical patent/WO2020253111A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
    • G06F3/0608—Saving storage space on storage systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0628—Interfaces specially adapted for storage systems making use of a particular technique
    • G06F3/0638—Organizing or formatting or addressing of data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0668—Interfaces specially adapted for storage systems adopting a particular infrastructure
    • G06F3/067—Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]

Definitions

  • This application relates to the field of blockchain, and in particular to methods, devices, operation and maintenance terminals, and computer-readable storage media for automatic expansion of blockchain nodes.
  • the main purpose of this application is to provide an automatic expansion method, device, operation and maintenance terminal, and computer-readable storage medium for blockchain nodes, aiming to solve the current inability to effectively set the data capacity of each chain node, resulting in disk space utilization Technical problems with low rates.
  • this application provides an automatic expansion method of blockchain nodes, which includes the following steps:
  • the steps to expand the target disk capacity include:
  • the remaining disk capacity value ⁇ after the preset time is less than the preset capacity setting value ⁇
  • the current disk remaining capacity value ⁇ of the node is obtained, and the current disk remaining capacity value ⁇ of the node is set to the preset capacity value.
  • the remaining disk capacity value ⁇ after the preset time is greater than or equal to the preset capacity setting value ⁇
  • the remaining disk capacity value ⁇ after the preset time is used as the target disk capacity expansion ⁇ of the node.
  • this application also provides an automatic expansion device for blockchain nodes, the device including:
  • the acquisition module is used to acquire the remaining disk capacity change data of the nodes in the blockchain
  • the obtaining module is further configured to obtain the disk capacity prediction function corresponding to the node according to the disk remaining capacity change data, and obtain the disk capacity value of the node after a preset time according to the disk capacity prediction function;
  • the expansion module is used to determine the target expansion capacity of the disk according to the remaining disk capacity value of the node after a preset time, so as to expand the node according to the target expansion capacity of the disk; wherein the expansion module includes:
  • the judging unit is used to judge whether the remaining capacity value ⁇ of the disk after the preset time is less than the preset capacity setting value ⁇ ;
  • the setting unit is configured to use the disk remaining capacity value ⁇ after the preset time as the target disk capacity expansion ⁇ of the node when the remaining disk capacity value ⁇ after the preset time is greater than or equal to the preset capacity setting value ⁇ .
  • the present application also provides an operation and maintenance terminal, the operation and maintenance terminal includes: a communication module, a memory, a processor, and a computer that is stored on the memory and can run on the processor. Reading instructions, when the computer-readable instructions are executed by the processor, the steps of the automatic expansion method for blockchain nodes as described above are realized.
  • the present application also provides a computer-readable storage medium having computer-readable instructions stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the above Steps of the automatic expansion method for blockchain nodes.
  • Figure 1 is a schematic structural diagram of a hardware operating environment involved in a solution of an embodiment of the present application
  • FIG. 2 is a schematic flowchart of the first embodiment of the automatic expansion method for blockchain nodes according to the application;
  • step S20 is a schematic flowchart of step S20 in the second embodiment of the automatic expansion method for blockchain nodes of this application;
  • FIG. 4 is a schematic diagram of functional modules of an embodiment of an automatic expansion device for blockchain nodes of this application.
  • the operation and maintenance terminal may be a server or a device terminal, such as a computer.
  • the operation and maintenance terminal may include a communication module 10, a memory 20, a processor 30 and other components.
  • the processor 30 is respectively connected to the memory 20 and the communication module 10, and computer-readable instructions are stored on the memory 20, and the computer-readable instructions are simultaneously used by the processor 30.
  • the computer-readable instructions implement the steps of the following method embodiments when executed.
  • the communication module 10 can be connected to external communication equipment via a network.
  • the communication module 10 can receive requests sent by external communication devices, and can also send requests, instructions and information to the external communication devices.
  • the external communication device may be other devices or other operation and maintenance terminals, such as other servers.
  • the memory 20 can be used to store software programs and various data.
  • the memory 20 may mainly include a storage program area and a storage data area, where the storage program area may store an operating system, at least one application program required by a function (for example, obtain the disk capacity change data of a blockchain node), etc.; the storage data area may Including the database, the data storage area can store data or information created according to the use of the operation and maintenance terminal.
  • the memory 20 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.
  • the processor 30 is the control center of the operation and maintenance terminal, which uses various interfaces and lines to connect various parts of the entire operation and maintenance terminal, and runs or executes software programs, computer-readable instructions and/or modules stored in the memory 20, and The data stored in the memory 20 is called to perform various functions and processing data of the operation and maintenance terminal, thereby overall monitoring of the operation and maintenance terminal.
  • the processor 30 may include one or more processing units; optionally, the processor 30 may integrate an application processor and a modem processor, where the application processor mainly processes the operating system, user interface, and application programs, etc.
  • the adjustment processor mainly deals with wireless communication. It can be understood that the above modem processor may not be integrated into the processor 30.
  • the above-mentioned operation and maintenance terminal may also include a circuit control module for connecting with a power source to ensure the normal operation of other components.
  • the above-mentioned operation and maintenance terminal may also include a display module for extracting data in the memory 20 and displaying the system interface of the operation and maintenance terminal, the interaction interface with the user, and the disk capacity change of the blockchain.
  • the operation and maintenance terminal structure shown in FIG. 1 does not constitute a limitation on the operation and maintenance terminal, and may include more or less components than shown in the figure, or a combination of certain components, or different components Layout.
  • the method includes:
  • Step S10 Obtain the remaining disk capacity change data of the node in the blockchain
  • the capacity change data may include the occupied disk capacity corresponding to different historical time points and the remaining capacity of the disk that can be occupied by the node.
  • the remaining disk capacity change data can be obtained by obtaining the recorded remaining disk capacity at different historical time points.
  • Step S20 Obtain the disk capacity prediction function corresponding to the node according to the disk remaining capacity change data, and obtain the disk remaining capacity value of the node after a preset time according to the disk capacity prediction function;
  • a preset statistical method may be used.
  • the statistical method may be to use a neural network to regress the remaining capacity change data of the disk, for example, linear regression or adaptive regression.
  • the disk capacity prediction function is obtained based on the historical change trend represented by the remaining disk capacity change data during a certain period of time in the past, which fits the previous capacity changes.
  • This solution uses the change data obtained in the past. To form a prediction function, it is used to estimate the remaining disk capacity value of the node at a certain time or multiple times later.
  • the disk capacity prediction function can be obtained with reference to the remaining disk capacity change data 6 hours before the current time.
  • Step S30 Determine the target expansion capacity of the disk according to the remaining disk capacity value of the node after a preset time, so as to expand the node according to the target expansion capacity of the disk.
  • Disk target expansion capacity refers to the idle disk capacity that the node needs to occupy after a preset time, or the remaining disk capacity value that needs to be reached after expansion.
  • the target disk expansion capacity can be obtained according to the disk capacity prediction function to obtain the node after the preset time
  • the remaining capacity value of the disk can be determined.
  • the preset value can be set to compare with the remaining capacity value of the disk after a preset time, or the remaining capacity value of the disk after the preset time can be brought into the formula for calculation and determination. Expanding the capacity of the node can be directly expanding the running container of the blockchain node through the thermal expansion technology, without affecting normal use.
  • the step of expanding the node according to the target expansion capacity of the disk in the above step S30 may be: before the preset time is reached, the remaining space of the disk occupied by the node is expanded to the target expansion capacity of the disk by means of hot expansion.
  • the remaining disk capacity change data of the node in the blockchain is obtained; the disk capacity prediction function corresponding to the node is obtained according to the remaining disk capacity change data, and the preset time interval is obtained according to the disk capacity prediction function.
  • the disk capacity prediction function obtained predicts the remaining disk capacity value of the node after the preset time, and then determines the target expansion capacity of the disk to be expanded according to the remaining disk capacity value, so as to expand according to the target expansion capacity of the disk.
  • the disk remaining The capacity change data includes the time point and the corresponding remaining capacity value
  • the step S20 includes:
  • Step S21 selecting a starting time point from all time points of the remaining capacity change data of the disk, and using all time points before the starting time point and the corresponding remaining capacity value as training data, and setting the starting time point All time points after the time point and the corresponding remaining capacity value are used as sample data;
  • the process of obtaining a prediction function capable of predicting the value of the remaining capacity of the disk after a preset time according to the remaining capacity change data of the disk is further limited.
  • the accuracy of the disk capacity prediction function is related to the remaining disk capacity value of the node after the preset time and the accuracy of the target expansion capacity result of the disk. Therefore, it is necessary to perform the function before obtaining the final disk capacity prediction function.
  • Acquisition and training which involves the acquisition of training data and sample data. All disk remaining capacity change data can be divided according to time points. The time point before the selected starting time point and the corresponding remaining capacity values are training data, and the time point after the selected starting time point and the corresponding remaining capacity respectively The capacity value is used as sample data.
  • the starting time point of dividing the sample data and the training data is in the middle position in the time axis formed by the time points, or the position of the starting time point is such that the total capacity of the training data is greater than or equal to the total capacity of the sample data.
  • Step S22 Input the training data into a preset calculator for regression to obtain a reference prediction function related to the time point and the remaining capacity value;
  • the regression can be at least one of linear regression and adaptive regression, and finally get The change curve function of can be used as a reference prediction function, which reflects the law of the time point and the remaining capacity value of the disk, and is related to the time point and the remaining capacity value.
  • step S23 the reference prediction function is iterated through the sample data, so that the reference prediction function at the completion of the iteration is used as the disk capacity prediction function corresponding to the node after the iteration is completed.
  • the benchmark prediction function obtained through the training data meets the change of the disk capacity after the preset time can be verified by the sample data, and if the error is large, the benchmark prediction function can be revised and iterated to reduce the prediction error. It is understandable that the latest modified benchmark prediction function when the final iteration is completed is the disk capacity prediction function corresponding to the node.
  • This solution uses the combination of sample data, training data, and regression iterative operations to give a process of how to obtain the disk capacity prediction function, helping to finally obtain the prediction function that meets the actual disk capacity.
  • step S23 may include:
  • Step S231 using the reference prediction function to calculate the predicted remaining capacity value corresponding to the node at any point in the sample data;
  • one or several time points in the sample data can be used to calculate the predicted remaining capacity value through the reference prediction function.
  • the benchmark prediction function is a function related to the time point and the remaining capacity value. Therefore, as long as the time point is known, the remaining capacity value can be calculated by the function, and the function is obtained through the curve, not the actual situation, so The calculated remaining capacity value is the predicted remaining capacity value.
  • Step S232 Obtain the actual remaining capacity value corresponding to the time point when the predicted remaining capacity value is calculated from the sample data, and compare the predicted remaining capacity value with the actual remaining capacity value to obtain the remaining capacity error;
  • the sample data contains the changes in the remaining capacity value of the disk that have been recorded after the selected start time point, after obtaining the predicted remaining capacity value, the actual remaining capacity value at the same time point can be obtained from the sample data, and then the prediction The remaining capacity value is compared with the actual remaining capacity value, and the difference between the actual value and the predicted value is obtained as the remaining capacity error, so as to evaluate the accuracy of the benchmark prediction function based on the obtained remaining capacity error.
  • Step S233 determining whether the remaining capacity error meets a preset iteration termination condition; wherein, when the remaining capacity error meets the preset iteration termination condition, the iteration is terminated;
  • the foregoing preset iteration termination conditions can be set according to actual needs. For example, the number of calculation errors, the number of remaining capacity errors, and/or the number of iterations can be recorded. When the number or number is greater than or equal to a certain set value, it is considered Meet the iteration termination condition; and/or the calculated error is less than a certain extreme value, it is deemed to meet the iteration termination condition. When it is determined that the iteration termination condition is met, the benchmark prediction function of the latest iteration can be output, and the iteration can be stopped.
  • Step S234 When the remaining capacity error does not meet a preset iteration termination condition, update the reference prediction function according to the remaining capacity error.
  • the loss function of the benchmark prediction function can be calculated according to the remaining capacity error, and the parameters of the benchmark prediction function can be adjusted according to the loss function to realize the iteration of the benchmark prediction function until the iteration is completed. Updating the benchmark prediction function by setting the iteration termination condition can help improve the accuracy of the function prediction, which is closer to the disk space changes of actual blockchain nodes, and indirectly improves the accuracy of on-demand expansion.
  • the step of judging whether the remaining capacity error meets the preset iteration termination condition may include:
  • step S30 includes:
  • Step S31 judging whether the remaining capacity value ⁇ of the disk after a preset time is less than the preset capacity setting value ⁇ ;
  • the preset capacity setting value can be fixed.
  • the preset capacity setting value is greater than or equal to 0, and it can also gradually increase or decrease over time. In actual operation, it can be based on the data in different fields actually used by the blockchain. The characteristics of storage are determined. In each different time range, the remaining disk capacity value after the preset time can be compared with the preset capacity setting value belonging to the same time range to determine the target expansion capacity of the disk to which the final node needs to be expanded.
  • the remaining disk capacity value ⁇ after the preset time is greater than or equal to the preset capacity setting value ⁇
  • the remaining disk capacity value ⁇ after the preset time is used as the target disk capacity expansion ⁇ of the node. It is understandable that what is originally predicted based on the disk capacity prediction function is the final target expansion capacity of the disk, then it can be considered that the disk space is still sufficient after the preset time and there is no need to perform disk expansion operations.
  • the remaining disk capacity value is smaller than the preset disk capacity setting value after the preset time
  • the remaining capacity of the disk after expansion is always larger than the current value of the remaining space of the disk, which is more balanced and meets the data storage requirements of the node.
  • the method further includes: determining whether the target expansion capacity of the disk of the node is Is greater than the total remaining capacity of the hard disk where the node is located; when the target disk capacity of the node is greater than the total remaining capacity of the disk where the node is located, a warning message of insufficient hard disk capacity is issued; when the target disk capacity of the node is less than When it is equal to the total remaining capacity of the disk where the node is located, the step of expanding the node according to the target expansion capacity of the disk is executed.
  • the device may be a server, or an operation and maintenance terminal, such as a computer.
  • the device includes:
  • the obtaining module 10 is used to obtain the remaining disk capacity change data of the nodes in the blockchain;
  • the obtaining module 10 is further configured to obtain the disk capacity prediction function corresponding to the node according to the disk remaining capacity change data, and obtain the disk capacity value of the node after a preset time according to the disk capacity prediction function ;
  • the expansion module 20 is configured to determine the target expansion capacity of the disk according to the remaining disk capacity value of the node after a preset time, so as to expand the node according to the target expansion capacity of the disk.
  • the disk remaining capacity change data includes a time point and a corresponding remaining capacity value; the acquiring module includes:
  • the selecting unit is used to select a starting time point from all time points of the remaining capacity change data of the disk, to use all time points before the starting time point and the corresponding remaining capacity value as the training data, and the All time points after the start time point and the corresponding remaining capacity value are taken as sample data;
  • the regression unit is used to input the training data into a preset calculator for regression to obtain a reference prediction function related to the time point and the remaining capacity value;
  • the iterative unit is configured to iterate the reference prediction function through the sample data, so as to use the reference prediction function at the completion of the iteration as the disk capacity prediction function corresponding to the node after the iteration is completed.
  • the iteration unit includes:
  • the calculation subunit is configured to use the reference prediction function to calculate the predicted remaining capacity value corresponding to the node at any point in the sample data;
  • the comparison subunit is used to obtain the actual remaining capacity value corresponding to the time point when the predicted remaining capacity value is calculated from the sample data, and to compare the predicted remaining capacity value with the actual remaining capacity value to obtain the remaining capacity error;
  • the judging subunit is used to judge whether the remaining capacity error meets the preset iteration termination condition; and when the remaining capacity error meets the preset iteration termination condition, the iteration terminates;
  • the update subunit is configured to update the reference prediction function according to the remaining capacity error when the remaining capacity error does not meet a preset iteration termination condition.
  • the judging subunit may be used for:
  • the expansion module includes:
  • the judging unit is used to judge whether the remaining capacity value ⁇ of the disk after the preset time is less than the preset capacity setting value ⁇ ;
  • the expansion module further includes:
  • the setting unit is configured to use the disk remaining capacity value ⁇ after the preset time as the target disk capacity expansion ⁇ of the node when the remaining disk capacity value ⁇ after the preset time is greater than or equal to the preset capacity setting value ⁇ .
  • the device further includes:
  • a judging module for judging whether the target disk capacity of the node is greater than the total remaining capacity of the hard disk where the node is located; and when the target disk capacity of the node is less than or equal to the total remaining capacity of the disk where the node is located, Trigger the expansion module to perform the step of expanding the node according to the target expansion capacity of the disk;
  • the sending module is used to send a prompt message that the hard disk capacity is insufficient when the target expansion capacity of the disk of the node is greater than the total remaining capacity of the disk where the node is located.
  • This application also proposes a computer-readable storage medium, which may be a non-volatile readable storage medium on which computer-readable instructions are stored.
  • the computer-readable storage medium may be the memory 20 in the operation and maintenance terminal of FIG. 1, or may be ROM (Read-Only Memory)/RAM (Random Access Memory), magnetic disk , At least one of the optical discs, the computer-readable storage medium includes a number of instructions to make a terminal device with a processor (which can be a mobile phone, a computer, a server, or a network device, etc.) execute the various embodiments of the present application The method described.
  • a processor which can be a mobile phone, a computer, a server, or a network device, etc.

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Abstract

L'invention concerne un procédé et un appareil d'expansion automatique pour un nœud de chaîne de blocs, et un terminal d'exploitation et de maintenance et un support de stockage, appliqués à une exploitation et à une maintenance de chaîne de blocs. Le procédé consiste à : obtenir les données de changement de capacité de disque restante d'un nœud dans une chaîne de blocs (S10) ; en fonction des données de changement de capacité de disque restante, obtenir une fonction de prédiction de capacité de disque correspondant au nœud, et obtenir la valeur de capacité de disque restante du nœud après un temps prédéfini en fonction de la fonction de prédiction de capacité de disque (S20) ; et déterminer une capacité d'expansion de disque cible en fonction de la valeur de capacité de disque restante du nœud après le temps prédéfini, de manière à étendre le nœud en fonction de la capacité d'expansion de disque cible (S30). La valeur de capacité de disque restante du nœud après le temps prédéfini est estimée au moyen de la fonction de prédiction de capacité de disque, puis la capacité d'expansion de disque cible est obtenue, et en conséquence, l'expansion efficace du nœud de chaîne de blocs selon les exigences est mise en œuvre, ce qui permet d'utiliser de manière rationnelle l'espace de stockage de disque, d'augmenter le taux d'utilisation d'espace de disque, et d'aider à réduire le risque provoqué par une intervention manuelle.
PCT/CN2019/120904 2019-06-19 2019-11-26 Procédé et appareil d'expansion automatique pour un nœud de chaîne de blocs, et terminal d'exploitation et de maintenance et support d'informations WO2020253111A1 (fr)

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