CN115291797A - Capacity expansion method, capacity expansion device, capacity expansion equipment and readable storage medium - Google Patents

Abstract

The application discloses a capacity expansion method, a capacity expansion device, capacity expansion equipment and a readable storage medium in the technical field of computers. The method comprises the steps that a capacity expansion strategy is preset for a target cluster, and when the capacity expansion strategy is enabled, the capacity expansion times in a preset time length do not exceed a time threshold, and a corresponding target volume of the capacity expansion strategy in the target cluster meets capacity expansion requirements, the capacity expansion strategy is analyzed to obtain a capacity expansion triggering condition and a capacity calculation mode; if the capacity expansion triggering condition is met, determining the expected capacity of the target volume after the current capacity expansion according to a capacity calculation mode; and if the expected capacity is larger than the total capacity of the target volume before the current expansion and is smaller than the capacity limit value of the target volume, expanding the target volume based on the expected capacity. The scheme can master the capacity expansion opportunity and capacity expansion capacity, can perform refined capacity expansion aiming at the cluster, and ensures high availability of cluster services and stability of data storage. The capacity expansion device, the equipment and the readable storage medium have the technical effects.

Description

A capacity expansion method, device, equipment and readable storage medium

technical field

The present application relates to the field of computer technology, in particular to a capacity expansion method, device, equipment and readable storage medium.

Background technique

With the use and operation of the cluster, it is necessary to manually expand the capacity of the cluster in stages. However, since the demand for storage capacity used by cluster operation is unpredictable, manual expansion is not only inefficient, but also unable to grasp the size and timing of each expansion, which may cause abnormal problems such as data storage failure due to untimely expansion.

Therefore, how to finely expand the capacity of the cluster is a problem to be solved by those skilled in the art.

Contents of the invention

In view of this, the purpose of the present application is to provide a capacity expansion method, device, device and readable storage medium, so as to perform refined capacity expansion for clusters. The specific plan is as follows:

In the first aspect, the application provides a capacity expansion method, including:

Obtain the preset expansion strategy in the target cluster;

If the capacity expansion policy is enabled, when the number of capacity expansions within the preset time period does not exceed the number threshold and the target volume corresponding to the capacity expansion policy in the target cluster meets the capacity expansion requirements, analyze the capacity expansion policy to obtain the capacity expansion trigger condition and capacity calculation method;

If the expansion trigger condition is met, the expected capacity of the target volume after this expansion is determined according to the capacity calculation method;

If the expected capacity is greater than the total capacity of the target volume before the current expansion and the expected capacity is smaller than the capacity limit value of the target volume, the target volume is expanded based on the expected capacity.

Optionally, also include:

Acquiring volume usage information of the target volume;

Correspondingly, the expansion trigger condition is: the timing time point set by the expansion trigger condition is reached and the volume usage information exceeds a preset limit value, or the volume usage information exceeds a preset limit value, or based on the The remaining exhaustion duration of the target volume determined by the volume usage information is lower than the exhaustion duration threshold.

Optionally, the determining the expected capacity of the target volume after this expansion according to the capacity calculation method includes:

The sum of the total capacity of the target volume and the preset fixed value before this expansion is taken as the expected capacity;

or

Calculate the product of the total capacity of the target volume before this expansion and a preset fixed percentage, and use the sum of the total capacity of the target volume before this expansion and the product as the expected capacity;

or

A prediction model is used to predict the capacity of this expansion, and the sum of the total capacity of the target volume before this expansion and the capacity of this expansion is used as the expected capacity.

Optionally, the volume usage information of the target volume within a period of time is input into multiple prediction models to obtain multiple prediction results; each prediction result includes: the predicted depletion time and predicted expansion capacity of the target volume;

Selecting the one with the largest numerical value among the plurality of predicted expansion capacities is used as the current expansion capacity, and the predicted exhaustion time in the prediction result corresponding to the current expansion capacity is used as the remaining exhaustion time.

Optionally, also include:

If the number of times of capacity expansion exceeds the number of times threshold, a corresponding alarm event is generated, and when the target volume meets the capacity expansion requirement, the step of analyzing the capacity expansion policy to obtain a capacity expansion trigger condition and a capacity calculation method is executed.

Optionally, the expansion requirement is: a volume exists and volume space can be applied for.

Optionally, the trigger condition for capacity expansion is: reaching a timing time point set by the trigger condition for capacity expansion.

In a second aspect, the present application provides a capacity expansion device, including:

The acquisition module is used to acquire the preset expansion strategy in the target cluster;

An execution module, configured to analyze the expansion if the capacity expansion policy is enabled, when the number of capacity expansions within a preset period of time does not exceed the number threshold and the target volume corresponding to the capacity expansion policy in the target cluster meets the capacity expansion requirements The strategy gets the expansion trigger condition and capacity calculation method;

A calculation module, configured to determine the expected capacity of the target volume after this expansion according to the capacity calculation method if the expansion trigger condition is met;

A capacity expansion module, configured to expand the target volume based on the expected capacity if the expected capacity is greater than the total capacity of the target volume before this expansion and the expected capacity is less than the capacity limit value of the target volume .

Optionally, also include:

A volume usage information acquisition module, configured to acquire volume usage information of the target volume;

Correspondingly, the expansion trigger condition is: reaching the timing point set by the expansion trigger condition and the volume usage information exceeds the preset limit value, or the volume usage information exceeds the preset limit value, or the remaining consumption of the target volume determined based on the volume usage information The exhausted duration is below the exhausted duration threshold.

Optionally, the calculation module is specifically used for:

The sum of the total capacity of the target volume before this expansion and the preset fixed value is taken as the expected capacity;

or

Calculate the product of the total capacity of the target volume before this expansion and the preset fixed percentage, and use the sum of the total capacity of the target volume before this expansion and the product as the expected capacity;

or

The prediction model is used to predict the capacity of this expansion, and the sum of the total capacity of the target volume before this expansion and the capacity of this expansion is taken as the expected capacity.

Optionally, also include:

The intelligent prediction module is used to input the volume usage information of the target volume within a period of time into multiple forecasting models to obtain multiple forecasting results; each forecasting result includes: the predicted exhaustion time of the target volume and the predicted expansion capacity; Select the one with the largest numerical value in the predicted expansion capacity as the current expansion capacity, and use the predicted depletion time in the prediction results corresponding to the current expansion capacity as the remaining depletion time.

Optionally, also include:

The alarm module is used to generate a corresponding alarm event if the number of expansion times exceeds the number threshold, and execute the steps of analyzing the expansion strategy to obtain the expansion trigger condition and the capacity calculation method when the target volume meets the expansion requirements.

In a third aspect, the present application provides an electronic device, including:

memory for storing computer programs;

A processor, configured to execute the computer program, so as to implement the capacity expansion method disclosed above.

In a fourth aspect, the present application provides a readable storage medium for storing a computer program, wherein when the computer program is executed by a processor, the capacity expansion method disclosed above is implemented.

From the above solutions, it can be seen that the present application provides a capacity expansion method, including: acquiring the preset capacity expansion policy in the target cluster; When the target volume corresponding to the expansion policy in the target cluster meets the expansion requirements, analyze the expansion policy to obtain the expansion trigger condition and capacity calculation method; if the expansion trigger condition is met, determine the expansion according to the capacity calculation method The expected capacity of the target volume later; if the expected capacity is greater than the total capacity of the target volume before this expansion and the expected capacity is less than the capacity limit value of the target volume, then based on the expected capacity, the The target volume is expanded.

It can be seen that this application presets an expansion policy for the target cluster and can set whether the policy is enabled for application. When the expansion policy is enabled for application, the number of expansion times and the target volume corresponding to the expansion policy in the target cluster are judged. When the number of expansion times within the set time does not exceed the threshold and the target volume corresponding to the expansion policy in the target cluster meets the expansion requirements, analyze the expansion policy to obtain the expansion trigger condition and capacity calculation method; if the expansion trigger condition is met, it is determined according to the capacity calculation method The expected capacity of the target volume after this expansion; if the expected capacity is greater than the total capacity of the target volume before this expansion and the expected capacity is less than the capacity limit value of the target volume, then the target volume will be expanded based on the expected capacity. This solution can grasp the expansion opportunity through the expansion trigger conditions, and can grasp the expansion capacity each time through the capacity calculation method, so it can perform refined expansion for the cluster, ensuring the high availability of the cluster business and the stability of data storage.

Correspondingly, a capacity expansion device, equipment, and readable storage medium provided by the present application also have the above-mentioned technical effects.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only It is an embodiment of the present application, and those skilled in the art can also obtain other drawings according to the provided drawings without creative work.

Fig. 1 is a flow chart of a capacity expansion method disclosed in the present application;

FIG. 2 is a logical schematic diagram of expansion disclosed in the present application;

FIG. 3 is a schematic diagram of a strategy application disclosed in the present application;

FIG. 4 is a flow chart of another expansion method disclosed in the present application;

Fig. 5 is a schematic diagram of a capacity expansion device disclosed in the present application;

FIG. 6 is a schematic diagram of an electronic device disclosed in the present application.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

At present, with the use and operation of the cluster, it is necessary to manually expand the capacity of the cluster in stages. However, since the demand for storage capacity used by cluster operation is unpredictable, manual expansion is not only inefficient, but also unable to grasp the size and timing of each expansion, which may cause abnormal problems such as data storage failure due to untimely expansion. For this reason, the present application provides a capacity expansion solution, which can grasp the expansion opportunity and capacity, so as to perform refined capacity expansion for the cluster.

Referring to Figure 1, the embodiment of the present application discloses a capacity expansion method, including:

S101. Obtain a preset expansion policy in the target cluster.

Wherein, the target cluster may be a cluster based on Kubernetes. Kubernetes, referred to as K8s for short, is a tool for managing containers in hosts, and has the advantages of high availability and elastic scaling. The expansion strategy can be preset in the cluster, and the switch of the expansion strategy can be set at the same time. When the switch of the expansion policy is on, the expansion policy is enabled and applicable; when the switch of the expansion policy is off, the expansion policy is disabled and cannot be applied. Wherein, the capacity expansion strategy includes information such as: capacity expansion trigger conditions, capacity calculation methods, and IDs of targeted target volumes. That is to say, the expansion policy is set for volumes in the cluster, and the target volume is one or more. The method provided in this embodiment can be executed by each host in the cluster, that is, the method provided in this embodiment can be applied to each host in the cluster.

S102. If the expansion policy is enabled, when the number of expansion times within the preset time period does not exceed the number threshold and the target volume corresponding to the expansion policy in the target cluster meets the expansion requirements, analyze the expansion policy to obtain an expansion trigger condition and a capacity calculation method.

In order to avoid frequent expansion in this embodiment, when the expansion policy can be applied, it is not applied immediately, but first judges whether the number of expansions within the preset duration exceeds the threshold value, if the number of expansions within the preset duration does not exceed the number of times threshold, it indicates that the capacity expansion is not frequent; if the number of expansions within the preset time exceeds the threshold, it indicates that the expansion is frequent, and corresponding alarm events can be generated at this time. Therefore, in a specific implementation, if the number of times of expansion exceeds the number threshold, a corresponding alarm event is generated, and when the target volume meets the expansion requirements, the step of analyzing the expansion strategy to obtain the expansion trigger condition and capacity calculation method is performed, and of course continues Follow the rest of the steps to complete the scaling process. It can be seen that the alarm event that the number of expansion times exceeds the threshold is used to remind the operation and maintenance personnel to pay attention to the expansion, and does not terminate the expansion process.

In a specific implementation manner, the capacity expansion requirement is: a volume exists and volume space can be applied for. That is: when the target volume is in normal use and there is still space available for application, it is considered that the target volume meets the expansion requirements. Generally, a volume in a cluster corresponds to a disk at the back end of the cluster.

S103. If the capacity expansion trigger condition is met, determine the expected capacity of the target volume after the current capacity expansion according to the capacity calculation method.

Specifically, the expansion trigger conditions can be set as: scheduled expansion, threshold expansion, etc. Scheduled expansion such as: once every 24 hours. Threshold expansion, such as: when the remaining space of the target volume is less than a certain threshold, the expansion action is performed. Therefore, in a specific implementation manner, the trigger condition for capacity expansion is: reaching the timing time point set by the trigger condition for capacity expansion.

In a specific implementation, the expected capacity of the target volume after the expansion is determined according to the capacity calculation method, including: a fixed value increment method: the sum of the total capacity of the target volume before the expansion and the preset fixed value is used as the expected capacity ; or percentage increment method: calculate the product of the total capacity of the target volume before the current expansion and the preset fixed percentage, and use the sum of the total capacity of the target volume before the current expansion and the product as the expected capacity; or intelligent incremental method: use the forecast The model predicts the capacity of this expansion, and takes the sum of the total capacity of the target volume before this expansion and the capacity of this expansion as the expected capacity. It can be seen that any of the above-mentioned capacity calculation methods can be used to determine the expected capacity.

S104. If the expected capacity is greater than the total capacity of the target volume before the expansion and the expected capacity is less than the capacity limit value of the target volume, expand the target volume based on the expected capacity.

In this embodiment, in order to avoid invalid expansion actions, in the execution of step S104, the expected capacity that can be achieved after this expansion is judged. If the expected capacity is greater than the total capacity of the target volume before this expansion, it indicates that this expansion If the cluster storage space can be expanded after the action is executed, the expansion action is valid. At the same time, in order to ensure the stable operation of the cluster, this embodiment not only compares the expected capacity with the total capacity of the target volume before the expansion, but also compares the expected capacity with the capacity limit value of the target volume. If the expected capacity is less than the capacity limit value of the target volume, it means that after the completion of the expansion operation, no storage exception will occur, that is, after the completion of the expansion operation, the application space of the target volume will not be exhausted and malfunction. It can be seen that this embodiment can not only achieve effective capacity expansion, but also ensure stable operation of the cluster before and after capacity expansion.

It can be seen that this embodiment presets the expansion policy for the target cluster and can set whether the policy enables the application. When the expansion policy enables the application, the number of expansion times and the target volume corresponding to the expansion policy in the target cluster are judged. When the number of expansion times within the preset time period does not exceed the number threshold and the target volume corresponding to the expansion policy in the target cluster meets the expansion requirements, analyze the expansion policy to obtain the expansion trigger condition and capacity calculation method; if the expansion trigger condition is met, then follow the capacity calculation method Determine the expected capacity of the target volume after this expansion; if the expected capacity is greater than the total capacity of the target volume before this expansion and the expected capacity is less than the capacity limit value of the target volume, then expand the target volume based on the expected capacity. This solution can grasp the expansion opportunity through the expansion trigger conditions, and can grasp the expansion capacity each time through the capacity calculation method, so it can perform refined expansion for the cluster, ensuring the high availability of the cluster business and the stability of data storage.

Based on the above-mentioned embodiments, it should be noted that the opportunity for capacity expansion may also be determined based on the actual usage of the volume. Therefore, in a specific implementation manner, it also includes: acquiring the volume usage information of the target volume; correspondingly, the trigger condition for capacity expansion is: reaching the timing time point set by the trigger condition for capacity expansion and the volume usage information exceeds the preset limit value, or the volume The usage information exceeds a preset limit value, or the remaining exhaustion duration of the target volume determined based on the volume usage information is lower than the exhaustion duration threshold.

Correspondingly, when the trigger condition for capacity expansion is reaching the timing point set by the trigger condition for capacity expansion and the volume usage information exceeds the preset limit value, the capacity calculation method can be a percentage increment method or a fixed value increment method. When the trigger condition for capacity expansion is that the volume usage information exceeds the preset limit value, the capacity calculation method can be selected: percentage increment method or fixed value increment method. When the trigger condition for capacity expansion is that the remaining depletion time of the target volume is lower than the depletion time threshold, the capacity calculation method can be selected: intelligent incremental method. When the expansion trigger condition is reaching the timing time point set by the expansion trigger condition, the capacity calculation method can be selected: percentage increment method or fixed value increment method. Of course, the above four expansion trigger conditions and three capacity calculation methods can be matched and used at will.

In a specific implementation manner, the remaining depletion time of the target volume and the current expansion capacity obtained by using the prediction model can be determined by referring to the following steps. Input the volume usage information of the target volume within a period of time into multiple prediction models to obtain multiple prediction results; each prediction result includes: the predicted depletion time of the target volume and the predicted expansion capacity; select the value among multiple predicted expansion capacities The largest one is used as the capacity of this expansion, and the predicted exhaustion time in the prediction result corresponding to the capacity of this expansion is used as the remaining exhaustion time. Among them, the prediction model can be obtained through training such as triple exponential smoothing algorithm, ARIMA algorithm, and STL algorithm. The volume usage information may be a volume usage rate or the like.

The following embodiments can closely monitor volume capacity, describe volume capacity change curves, predict resource exhaustion time, and support multiple strategies in combination with user-set expansion timing, expansion capacity, and upper limit of single volume capacity. elastic expansion.

Please refer to Figure 2, the implementation logic shown in Figure 2 is deployed on any host in the cluster. The host includes many containers, and Figure 2 monitors and expands the volumes used by each container.

As shown in Figure 2, the Node-Exporter interface collects the indicator information of the storage volume on the host based on the set indicators, and the monitoring system calls the Node-Exporter interface to obtain the corresponding indicator information, mainly including the volume usage rate and volume history curve. The monitoring system can realize data monitoring by using the prometheus component. The prediction system predicts the exhaustion time of the volume and the expansion size based on the obtained indicator information. The adapter can be adapted to a variety of monitoring systems, and can obtain volume usage, volume usage history curves, and volume trend forecast information. The recommender regularly obtains real-time indicator data and trend prediction data. And the expected capacity is updated according to the acquired monitoring data. The update component updates the capacity of the target volume according to the desired volume capacity. The elastic expansion business model is used to set information such as the volume to be expanded, the expected expansion capacity, and the expansion strategy. When the update component monitors the updated expected expansion capacity, it triggers the expansion action. Among them, the controller and forecasting system shown in Figure 2 communicate using the unix domain socket protocol, which has high communication efficiency and is easy to implement.

The elastic expansion business model supports multiple expansion trigger strategies (such as: threshold trigger, intelligent trigger, and timing trigger) and capacity setting strategies (fixed capacity, fixed percentage, and intelligent capacity adjustment). As shown in Figure 3, the matching application relationship between each capacity expansion trigger policy and each capacity setting policy can be referred to in Figure 3 . Of course, more expansion trigger strategies and capacity setting strategies can be abstracted based on actual business needs. Take threshold triggering as an example: when the volume usage exceeds a certain threshold, the elastic capacity expansion business model expands capacity based on a fixed capacity method. Take intelligent triggering as an example again: when the volume exhaustion time output by the prediction system is lower than a certain threshold, the elastic expansion business model expands based on the expansion capacity required to support N days of business output by the prediction system at the same time.

Furthermore, a management terminal can also be set, and the management terminal is used to configure specific acquisition indicators, expansion trigger policies, and capacity setting policies.

Among them, the input data of the prediction system is: volume historical usage rate, and the output result is: the time when the remaining capacity of the volume is exhausted and the expansion capacity required to support N days of business. Wherein, the forecasting model in the forecasting system may be constructed by using a cubic exponential smoothing algorithm, an ARIMA algorithm and/or an STL algorithm, and the like. When multiple prediction models are constructed by using multiple algorithms, the output result of the model with the best evaluation is selected.

Specifically, the expansion process may refer to FIG. 4 . As shown in Figure 4, after obtaining the expansion trigger policy and capacity setting policy set in the elastic expansion business model, it is judged whether the elastic expansion business model is available; if it is available, it is judged whether it is enabled; The process ends when possible. If the elastic expansion business model is available and enabled, it is judged whether the number of expansions exceeds the threshold, and the next judgment is executed regardless of whether the number of expansions exceeds the threshold, but an alarm is added when the number of expansions exceeds the threshold. When the number of expansion times does not exceed the threshold, determine whether the target volume to be expanded exists and whether it can be expanded. If both are satisfied, analyze the expansion trigger policy and capacity setting policy so that the expansion can be triggered, and the expansion capacity is calculated after the expansion is triggered. . After the expected capacity is calculated, when the expected capacity is less than the actual total available capacity of the target volume, less than the maximum capacity of the volume, and greater than the capacity before volume expansion, update the expected capacity to the elastic expansion business model so that the update component can monitor the expected capacity and perform expansion actions .

It can be seen that this embodiment can automatically complete expansion according to the configuration policy when the volume capacity is insufficient, ensuring high availability of application services and solving the problem of service interruption caused by insufficient volume capacity; The resulting expansion capacity is not clear; expansion does not require manual intervention, which reduces the maintenance cost of the cloud platform, enhances the stability of container storage, improves the utilization of storage resources, and improves the exception handling mechanism.

A capacity expansion device provided in the embodiment of the present application is introduced below. The capacity expansion device described below and the capacity expansion method described above may refer to each other.

Referring to Figure 5, the embodiment of the present application discloses a capacity expansion device, including:

An acquisition module 501, configured to acquire a preset expansion strategy in the target cluster;

The execution module 502 is configured to analyze the expansion policy to obtain the expansion trigger condition and capacity when the number of expansion times within the preset time period does not exceed the number threshold and the target volume corresponding to the expansion policy in the target cluster meets the expansion requirements if the expansion policy is enabled Calculation;

Calculation module 503, configured to determine the expected capacity of the target volume after the current expansion according to the capacity calculation method if the capacity expansion trigger condition is met;

The expansion module 504 is configured to expand the target volume based on the expected capacity if the expected capacity is greater than the total capacity of the target volume before the current expansion and the expected capacity is smaller than the capacity limit value of the target volume.

In a specific embodiment, it also includes:

A volume usage information acquisition module, configured to acquire volume usage information of the target volume;

Correspondingly, the expansion trigger condition is: reaching the timing point set by the expansion trigger condition and the volume usage information exceeds the preset limit value, or the volume usage information exceeds the preset limit value, or the remaining consumption of the target volume determined based on the volume usage information The exhausted duration is below the exhausted duration threshold.

In a specific implementation manner, the calculation module is specifically used for:

The sum of the total capacity of the target volume before this expansion and the preset fixed value is taken as the expected capacity;

or

Calculate the product of the total capacity of the target volume before this expansion and the preset fixed percentage, and use the sum of the total capacity of the target volume before this expansion and the product as the expected capacity;

or

The prediction model is used to predict the capacity of this expansion, and the sum of the total capacity of the target volume before this expansion and the capacity of this expansion is taken as the expected capacity.

In a specific embodiment, it also includes:

The intelligent prediction module is used to input the volume usage information of the target volume within a period of time into multiple forecasting models to obtain multiple forecasting results; each forecasting result includes: the predicted exhaustion time of the target volume and the predicted expansion capacity; Select the one with the largest numerical value in the predicted expansion capacity as the current expansion capacity, and use the predicted depletion time in the prediction results corresponding to the current expansion capacity as the remaining depletion time.

In a specific embodiment, it also includes:

The alarm module is used to generate a corresponding alarm event if the number of expansion times exceeds the number threshold, and execute the steps of analyzing the expansion strategy to obtain the expansion trigger condition and the capacity calculation method when the target volume meets the expansion requirements.

In a specific implementation manner, the capacity expansion requirement is: a volume exists and volume space can be applied for. That is: when the target volume is in normal use and there is still space available for application, it is considered that the target volume meets the expansion requirements. Generally, a volume in a cluster corresponds to a disk at the back end of the cluster.

In a specific implementation manner, the trigger condition for capacity expansion is: reaching a timing time point set by the trigger condition for capacity expansion. The expansion trigger conditions can be set as: scheduled expansion, threshold expansion, etc. Scheduled expansion such as: once every 24 hours. Threshold expansion, such as: when the remaining space of the target volume is less than a certain threshold, the expansion action is performed. Therefore, in a specific implementation manner, the trigger condition for capacity expansion is: reaching the timing time point set by the trigger condition for capacity expansion.

For the more specific working process of each module and unit in this embodiment, reference may be made to the corresponding content disclosed in the foregoing embodiments, and details are not repeated here.

It can be seen that this embodiment provides a capacity expansion device capable of grasping the expansion opportunity and capacity, so as to perform refined capacity expansion for the cluster.

An electronic device provided by an embodiment of the present application is introduced below, and the electronic device described below and the capacity expansion method and device described above may refer to each other.

Referring to Figure 6, the embodiment of the present application discloses an electronic device, including:

Memory 601, used to store computer programs;

The processor 602 is configured to execute the computer program, so as to implement the method disclosed in any of the foregoing embodiments.

In this embodiment, the computer program executed by the processor can implement the following steps: obtain the preset expansion strategy in the target cluster; When the corresponding target volume in the target cluster meets the expansion requirements, analyze the expansion strategy to obtain the expansion trigger condition and capacity calculation method; if the expansion trigger condition is met, determine the expected capacity of the target volume after this expansion according to the capacity calculation method; if the expected capacity is greater than If the total capacity of the target volume before this expansion and the expected capacity is less than the capacity limit value of the target volume, then the target volume is expanded based on the expected capacity. Optionally, the expansion requirement is: the volume exists and the volume space can be applied for. Optionally, the trigger condition for capacity expansion is: reaching a timing time point set by the trigger condition for capacity expansion.

In this embodiment, the computer program executed by the processor can implement the following steps: acquire the volume usage information of the target volume; The limit value, or the volume usage information exceeds a preset limit value, or the remaining exhaustion duration of the target volume determined based on the volume usage information is lower than the exhaustion duration threshold.

In this embodiment, the computer program executed by the processor can implement the following steps: use the sum of the total capacity of the target volume before the current expansion and the preset fixed value as the expected capacity; or calculate the total capacity of the target volume before the current expansion and Preset the product of a fixed percentage, and use the sum of the total capacity of the target volume before this expansion and the product as the expected capacity; or use the forecasting model to predict the capacity of this expansion, and compare the total capacity of the target volume before this expansion with this The sum of the expansion capacity is used as the expected capacity.

In this embodiment, the computer program executed by the processor can implement the following steps: input the volume usage information of the target volume within a period of time into multiple prediction models to obtain multiple prediction results; each prediction result includes: the prediction of the target volume Depletion time and predicted expansion capacity; select the one with the largest value among multiple predicted expansion capacities as the current expansion capacity, and use the predicted depletion time in the prediction results corresponding to this expansion capacity as the remaining depletion time.

In this embodiment, the computer program executed by the processor can implement the following steps: if the number of times of capacity expansion exceeds the threshold value, a corresponding alarm event is generated, and when the target volume meets the capacity expansion requirements, the parsing expansion policy is executed to obtain the capacity expansion trigger condition and capacity Steps in how to calculate.

Further, the embodiment of the present application also provides a server as the above-mentioned electronic device. The server may specifically include: at least one processor, at least one memory, a power supply, a communication interface, an input and output interface, and a communication bus. Wherein, the memory is used to store a computer program, and the computer program is loaded and executed by the processor, so as to implement relevant steps in the capacity expansion method disclosed in any of the foregoing embodiments.

In this embodiment, the power supply is used to provide working voltage for each hardware device on the server; the communication interface can create a data transmission channel between the server and external devices, and the communication protocol it follows is applicable to the technical solution of this application Any communication protocol is not specifically limited here; the input/output interface is used to obtain external input data or output data to the external, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

In addition, memory, as a resource storage carrier, can be read-only memory, random access memory, magnetic disk or optical disk, etc. The resources stored on it include operating system, computer program and data, etc., and the storage method can be temporary storage or permanent storage.

Wherein, the operating system is used to manage and control various hardware devices and computer programs on the server, so as to realize the calculation and processing of the data in the memory by the processor, which may be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the capacity expansion method disclosed in any of the foregoing embodiments, the computer program can further include a computer program that can be used to complete other specific tasks. In addition to data such as the virtual machine, the data may also include data such as developer information of the virtual machine.

Further, the embodiment of the present application also provides a terminal as the above-mentioned electronic device. The terminal specifically may include, but is not limited to, a smart phone, a tablet computer, a notebook computer or a desktop computer, and the like.

Generally, the terminal in this embodiment includes: a processor and a memory.

Wherein, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor can be realized by at least one hardware form among DSP (Digital Signal Processing, digital signal processing), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array, programmable logic array) . The processor can also include a main processor and a coprocessor, the main processor is a processor for processing data in the wake-up state, also called a CPU (Central Processing Unit, central processing unit); Low-power processor for processing data in standby state. In some embodiments, the processor may be integrated with a GPU (Graphics Processing Unit, image processor), and the GPU is used for rendering and drawing the content that needs to be displayed on the display screen. In some embodiments, the processor may further include an AI (Artificial Intelligence, artificial intelligence) processor, where the AI processor is configured to process computing operations related to machine learning.

The memory may include one or more computer-readable storage media, which may be non-transitory. Memory may also include high-speed random access memory, and non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory is at least used to store the following computer program, wherein, after the computer program is loaded and executed by the processor, it can implement the relevant steps in the capacity expansion method performed by the terminal side disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory may also include an operating system and data, etc., and the storage method may be temporary storage or permanent storage. Wherein, the operating system may include Windows, Unix, Linux, and the like. Data may include, but is not limited to, update information for applications.

In some embodiments, the terminal may further include a display screen, an input/output interface, a communication interface, a sensor, a power supply, and a communication bus.

A readable storage medium provided by an embodiment of the present application is introduced below, and a readable storage medium described below and a capacity expansion method, device, and device described above may refer to each other.

A readable storage medium is used to store a computer program, wherein when the computer program is executed by a processor, the capacity expansion method disclosed in the foregoing embodiments is implemented.

In this embodiment, the computer program stored in the readable storage medium can implement the following steps: obtain the preset expansion strategy in the target cluster; if the expansion strategy is enabled, the number of times of expansion within the preset duration does not exceed the number threshold When the target volume corresponding to the policy in the target cluster meets the expansion requirements, analyze the expansion policy to obtain the expansion trigger condition and capacity calculation method; if the expansion trigger condition is met, determine the expected capacity of the target volume after this expansion according to the capacity calculation method; If the capacity is greater than the total capacity of the target volume before this expansion and the expected capacity is less than the capacity limit value of the target volume, then the target volume is expanded based on the expected capacity. Optionally, the expansion requirement is: the volume exists and the volume space can be applied for. Optionally, the trigger condition for capacity expansion is: reaching a timing time point set by the trigger condition for capacity expansion.

In this embodiment, the computer program stored in the readable storage medium can implement the following steps: obtain the volume usage information of the target volume; The preset limit value, or the volume usage information exceeds the preset limit value, or the remaining exhaustion duration of the target volume determined based on the volume usage information is lower than the exhaustion duration threshold.

In this embodiment, the computer program stored in the readable storage medium can implement the following steps: use the sum of the total capacity of the target volume before this expansion and the preset fixed value as the expected capacity; or calculate the total capacity of the target volume before this expansion. The product of the capacity and the preset fixed percentage, and the sum of the total capacity of the target volume before the expansion and the product is the expected capacity; or use the forecasting model to predict the capacity of the expansion, and the total capacity of the target volume before the expansion The sum of the expansion capacity is taken as the expected capacity.

In this embodiment, the computer program stored in the readable storage medium can implement the following steps: input the volume usage information of the target volume within a period of time into multiple prediction models to obtain multiple prediction results; each prediction result includes: the target volume The predicted depletion time and predicted expansion capacity; among the multiple predicted expansion capacities, select the one with the largest value as the current expansion capacity, and use the predicted depletion time in the prediction results corresponding to this expansion capacity as the remaining depletion time.

In this embodiment, the computer program stored in the readable storage medium can implement the following steps: if the number of times of expansion exceeds the number threshold, a corresponding alarm event is generated, and when the target volume meets the expansion requirements, the analysis of the expansion strategy is executed to obtain the expansion trigger condition and the steps of the capacity calculation method.

"First", "second", "third", "fourth" and the like referred to in the present application, if any, are used to distinguish similar objects and not necessarily to describe a specific order or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, e.g. a process, method or apparatus comprising a series of steps or elements is not necessarily limited to those steps or elements explicitly listed , but may include other steps or elements not explicitly listed or inherent to the process, method or apparatus.

It should be noted that the descriptions in this application involving "first", "second" and so on are for descriptive purposes only, and should not be understood as indicating or implying their relative importance or implicitly indicating the number of indicated technical features . Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In addition, the technical solutions of the various embodiments can be combined with each other, but it must be based on the realization of those skilled in the art. When the combination of technical solutions is contradictory or cannot be realized, it should be considered that the combination of technical solutions does not exist , nor within the scope of protection required by the present application.

Each embodiment in this specification is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same or similar parts of each embodiment can be referred to each other.

The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be directly implemented by hardware, software modules executed by a processor, or a combination of both. Software modules can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other Any other known readable storage medium.

In this paper, specific examples are used to illustrate the principles and implementation methods of the application. The descriptions of the above embodiments are only used to help understand the method and core idea of the application; meanwhile, for those of ordinary skill in the art, according to the application There will be changes in the specific implementation and scope of application. In summary, the content of this specification should not be construed as limiting the application.

Claims (10)

1. A capacity expansion method is characterized by comprising the following steps:

acquiring a preset capacity expansion strategy in a target cluster;

if the capacity expansion strategy is enabled, analyzing the capacity expansion strategy to obtain a capacity expansion triggering condition and a capacity calculation mode when the capacity expansion times in a preset time length do not exceed a time threshold and a target volume corresponding to the capacity expansion strategy in the target cluster meets the capacity expansion requirement;

if the capacity expansion triggering condition is met, determining the expected capacity of the target volume after the current capacity expansion according to the capacity calculation mode;

and if the expected capacity is larger than the total capacity of the target volume before the current capacity expansion and the expected capacity is smaller than the capacity limit value of the target volume, expanding the capacity of the target volume based on the expected capacity.

2. The method of claim 1, further comprising:

acquiring volume use information of the target volume;

correspondingly, the capacity expansion triggering condition is as follows: and when the set timing time point of the capacity expansion triggering condition is reached, the volume use information exceeds a preset limit value, or the remaining exhaustion duration of the target volume determined based on the volume use information is lower than an exhaustion duration threshold value.

3. The method according to claim 2, wherein the determining the expected capacity of the target volume after the current capacity expansion according to the capacity calculation manner includes:

taking the sum of the total capacity of the target volume before the current capacity expansion and a preset fixed value as the expected capacity;

or

Calculating the product of the total capacity of the target volume before the current capacity expansion and a preset fixed percentage, and taking the sum of the total capacity of the target volume before the current capacity expansion and the product as the expected capacity;

or

And predicting the current capacity expansion capacity by using a prediction model, and taking the sum of the total capacity of the target volume before the current capacity expansion and the current capacity expansion capacity as the expected capacity.

4. The method of claim 3,

inputting the volume use information of the target volume in a period of time into a plurality of prediction models to obtain a plurality of prediction results; each prediction result includes: the predicted exhaustion duration and the predicted capacity expansion capacity of the target volume;

selecting one of the plurality of predicted capacity expansion capacities with the largest value as the current capacity expansion capacity, and using the predicted exhaustion duration in the prediction result corresponding to the current capacity expansion capacity as the remaining exhaustion duration.

5. The method of claim 4, further comprising:

and if the capacity expansion times exceed the time threshold, generating a corresponding alarm event, and when the target volume meets the capacity expansion requirement, executing the step of analyzing the capacity expansion strategy to obtain a capacity expansion triggering condition and a capacity calculation mode.

6. The method of any of claims 1 to 5, wherein the capacity expansion requirement is: volumes exist and volume space is available.

7. The method of claim 1, wherein the capacity expansion triggering condition is: and reaching the timing time point set by the expansion triggering condition.

8. A flash device, comprising:

the acquisition module is used for acquiring a preset expansion strategy in the target cluster;

the execution module is used for analyzing the capacity expansion strategy to obtain a capacity expansion triggering condition and a capacity calculation mode when the capacity expansion times in a preset time length do not exceed a time threshold and a target volume corresponding to the capacity expansion strategy in the target cluster meets the capacity expansion requirement if the capacity expansion strategy is enabled;

the calculation module is used for determining the expected capacity of the target volume after the current capacity expansion according to the capacity calculation mode if the capacity expansion triggering condition is reached;

and the capacity expansion module is used for expanding the capacity of the target volume based on the expected capacity if the expected capacity is larger than the total capacity of the target volume before the current capacity expansion and the expected capacity is smaller than the capacity limit value of the target volume.

9. An electronic device, comprising:

a memory for storing a computer program;

a processor for executing the computer program to implement the method of any one of claims 1 to 7.

10. A readable storage medium for storing a computer program, wherein the computer program when executed by a processor implements the method of any one of claims 1 to 7.

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