Storage node running trend prediction method, device, medium and program product

Abstract

The application discloses a kind of storage node running trend prediction method, equipment, medium and program product, it is related to distributed technical field.The method comprises: by monitoring system, the monitoring data of storage node is obtained from the registration system operated in the registration equipment, and the monitoring data is stored;Obtain running trend prediction request, wherein, running trend prediction request includes to be predicted label and to be predicted monitoring index;By monitoring system, according to the label of storage node in monitoring data and monitoring index, determine the value of the first target monitoring index matched with to be predicted label and to be predicted monitoring index;According to the value of first target monitoring index, determine the running trend of storage node with to be predicted label based on to be predicted monitoring index.The method introduces the self-discovery mechanism of monitoring system, reduces the cost of manpower maintenance, so as to improve the monitoring efficiency and monitoring flexibility, at the same time, realize running trend prediction, improve the comprehensiveness and accuracy of monitoring.

Description

Technical Field

[0001] This invention relates to the field of distributed technology, and in particular to a method, device, medium, and program product for predicting the operating trend of storage nodes. Background Technology

[0002] In distributed systems, storage nodes are used to store configuration data and metadata. Therefore, monitoring storage nodes is crucial.

[0003] In related technologies, monitoring of storage nodes can be achieved through a monitoring system: by manually configuring the Internet Protocol (IP) address and port information of the storage node in the configuration file of the monitoring system, monitoring data can be obtained from the storage node based on the address information.

[0004] However, in the above process, when a new storage node comes online, the address information of the storage node needs to be manually added to the configuration file of the monitoring system, and when a storage node goes offline, the address information of the storage node needs to be manually deleted from the configuration file of the monitoring system, resulting in low monitoring efficiency and flexibility. Summary of the Invention

[0005] This invention provides a method, device, medium, and program product for predicting the operating trend of storage nodes, in order to solve the technical problem that the monitoring efficiency and flexibility of related monitoring methods are low.

[0006] According to one aspect of the present invention, a method for predicting the operating trend of a storage node is provided, applied to a monitoring device, wherein a monitoring system runs on the monitoring device, and the method includes:

[0007] The monitoring system obtains and stores monitoring data of storage nodes from the registration system running in the registration device; wherein the storage node is a node pre-registered in the registration system, and the monitoring data includes the tag of the storage node and the value of the monitoring index of the storage node.

[0008] Obtain an operational trend prediction request; wherein, the operational trend prediction request includes the label to be predicted and the monitoring indicator to be predicted;

[0009] The monitoring system determines the value of a first target monitoring indicator that matches both the label to be predicted and the monitoring indicator to be predicted, based on the labels of the storage nodes in the monitoring data and the monitoring indicators.

[0010] Based on the value of the first target monitoring indicator, determine the operating trend of the storage node with the label to be predicted based on the monitoring indicator to be predicted.

[0011] According to another aspect of the present invention, a storage node operation trend prediction device is provided. The device is installed on a monitoring device, which runs a monitoring system. The device includes:

[0012] The first acquisition module is used to acquire monitoring data of storage nodes from the registration system running in the registration device through the monitoring system, and store the monitoring data; wherein, the storage node is a node pre-registered in the registration system, and the monitoring data includes the tag of the storage node and the value of the monitoring index of the storage node;

[0013] The second acquisition module is used to acquire an operation trend prediction request; wherein, the operation trend prediction request includes a label to be predicted and a monitoring indicator to be predicted.

[0014] The first determining module is used to determine, through the monitoring system, the value of a first target monitoring indicator that matches both the label to be predicted and the monitoring indicator to be predicted, based on the label of the storage node in the monitoring data and the monitoring indicator.

[0015] The second determining module is used to determine the operating trend of the storage node with the label to be predicted based on the value of the first target monitoring indicator.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the storage node operation trend prediction method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, the computer program being configured to cause a processor to execute and implement the storage node operation trend prediction method according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the storage node operation trend prediction method according to any embodiment of the present invention.

[0022] The technical solution of this invention includes: obtaining monitoring data of storage nodes from a registration system running in a registration device through a monitoring system, and storing the monitoring data, wherein the storage nodes are nodes pre-registered in the registration system, and the monitoring data includes the tags of the storage nodes and the values ​​of the monitoring indicators of the storage nodes; obtaining an operation trend prediction request, wherein the operation trend prediction request includes a tag to be predicted and a monitoring indicator to be predicted; determining, through the monitoring system, the value of a first target monitoring indicator that matches both the tag to be predicted and the monitoring indicator to be predicted, based on the tags of the storage nodes and the monitoring indicators in the monitoring data; and determining the operation trend of the storage nodes with the tags to be predicted based on the monitoring indicators to be predicted, based on the value of the first target monitoring indicator. This method has the following technical effects: On the one hand, the monitoring system retrieves monitoring data through the registration system, and storage nodes can dynamically register or unregister from the registration system. Therefore, the monitoring system can perceive changes in storage nodes in the distributed environment, enabling the monitoring process to adapt to constantly changing storage nodes. This is equivalent to introducing a self-discovery mechanism for the monitoring system, reducing human maintenance costs, and thus improving monitoring efficiency, flexibility, and adaptability. On the other hand, it can also predict operational trends based on monitoring data, improving the comprehensiveness of monitoring. Furthermore, it can predict operational trends based on tag granularity and monitoring indicator granularity, improving the accuracy of monitoring.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a storage node operation trend prediction method provided in an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram illustrating an application scenario of the storage node operation trend prediction method provided in this embodiment of the invention;

[0027] Figure 3 This is a flowchart of another storage node operation trend prediction method provided in an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of various thresholds in an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of the structure of a storage node operation trend prediction device provided in an embodiment of the present invention;

[0030] Figure 6 This is a schematic diagram of the structure of an electronic device that implements the storage node operation trend prediction method of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices. The acquisition, storage, use, and processing of data in the embodiments of this invention comply with the relevant provisions of national laws and regulations.

[0033] Figure 1 This is a flowchart illustrating a method for predicting the operational trends of storage nodes according to an embodiment of the present invention. This embodiment is applicable to scenarios involving monitoring storage nodes in a distributed system. The method can be executed by a storage node operational trend prediction device, which can be implemented in hardware and / or software. This storage node operational trend prediction device can be configured in an electronic device, such as a monitoring device. In this embodiment, the monitoring device runs a monitoring system. Figure 1 As shown, the method includes the following steps 101 to 104.

[0034] Step 101: Obtain the monitoring data of the storage node from the registration system running on the registration device through the monitoring system, and store the monitoring data.

[0035] Storage nodes are nodes that are pre-registered in the registration system, and the monitoring data includes the tags of the storage nodes and the values ​​of the monitoring metrics of the storage nodes.

[0036] In this embodiment, the storage node refers to a device in a distributed system used to store configuration data and metadata. The normal operation of the storage node is crucial to the stability and reliability of the entire distributed system. Therefore, monitoring the storage node is extremely important.

[0037] In this embodiment, in order to achieve automated monitoring access of storage nodes, avoid frequent operation of monitoring device configuration files due to changes in storage nodes, and reduce the pressure on operation and maintenance personnel, monitoring can be achieved by registering devices.

[0038] The registration system in this embodiment provides registration for storage nodes and enables service discovery functionality for the monitoring system, allowing the monitoring system to dynamically discover and monitor storage nodes. In this embodiment, the storage nodes expose monitoring indicator interfaces. Storage nodes register with the registration system when they come online and unregister from the registration system when they go offline.

[0039] Optionally, the registration system in this embodiment can provide health checks. The registration system checks whether the storage nodes are operating normally at a preset frequency; when an abnormal operation of a storage node is detected, it can actively unregister the storage node.

[0040] During registration, a storage node can register by sending a registration request to the registration system. Optionally, the registration request may include: the port and IP address of the registration system, the list of tags for the storage node, and health check configuration (including the address and interval for health checks).

[0041] For example, a possible registration request could be: `curl -X PUT -d '{"id":"'${IP}'","name":"'XXX'","address":"'${IP}}'","port":2379,"tags":["slb-***"],"checks":[{"http":"http: / / '${IP}':2379 / metrics","interval":"5s"}]}'http: / / ${SSS}:8500 / v1 / agent / service / register`. Here, 8500 represents the port of the registration system, and ` / v1 / agent / service / register` represents the Uniform Resource Locator (URL) of the registration system. `http: / / '${IP}':2379 / metrics` represents the URL for health checks, with a check interval of 5 seconds.

[0042] During deregistration, the storage node can deregister by sending a deregistration request to the registration system. Optionally, the deregistration request may include the registration system's port and IP address.

[0043] For example, a possible unregistration request could be: curl --request PUT http: / / ${SSS}:8500 / v1 / agent / service / deregister / ${ip}. Here, 8500 represents the port of the registration system, / v1 / agent / service / deregister / represents the URL of the registration system, and ${ip} represents the IP address of the registration system.

[0044] The monitoring system in this embodiment refers to a system capable of acquiring and analyzing monitoring data from storage nodes. In this embodiment, the monitoring system operates within a monitoring device.

[0045] Optionally, to enable monitoring of storage nodes via device registration, before step 101, the method further includes the following step: modifying the monitoring system's configuration file to register the registration system with the monitoring system. The modified configuration file includes the address information of the registration system. This implementation registers the registration system with the monitoring system, facilitating subsequent retrieval of monitoring data through the registration system, thus improving monitoring efficiency, flexibility, and adaptability.

[0046] Alternatively, one possible implementation of the modified configuration file is as follows:

[0047] scrape_configs:

[0048] -job_name:***

[0049] ***_sd_configs:

[0050] -server:$#{ENV_ELC_DOMAIN}:8500

[0051] relabel_configs:

[0052] -source_labels:[__meta_tags]

[0053] regex:.*ccc.*

[0054] action: keep

[0055] Here, $#{ENV_ELC_DOMAIN}:8500 specifies the IP address and port of the registration system.

[0056] The monitoring system can retrieve monitoring data from the registration system at a preset frequency and store this monitoring data. This embodiment achieves dynamic updates to the monitoring configuration through the registration and deregistration of storage nodes on the registration system, ensuring that the monitoring data obtained by the monitoring system changes synchronously with the storage node structure. One possible monitoring data acquisition process is as follows: the monitoring system sends a data acquisition request to the registration system; upon receiving the data acquisition request, the registration system retrieves monitoring data from each registered storage node that has passed a health check; the registration system then sends the retrieved monitoring data back to the monitoring system.

[0057] Optionally, the storage system can also selectively store monitoring data. In the modified configuration file described above, regex:.*ccc.* represents a regular expression; only monitoring data that satisfies .*ccc.* will be stored in the monitoring device.

[0058] In this embodiment, to improve the accuracy of monitoring, each monitoring data includes a tag for the storage node. The tag in this embodiment is used to characterize a certain attribute of the storage node. For example, the tag can be information indicating the location of the storage node, such as "Campus 1" or "Campus 2," and the tag can also be information indicating the purpose of the storage node, such as "Production Environment" or "Test Environment."

[0059] The monitoring metrics in this embodiment are used to characterize the dimensions of monitoring. There can be at least one monitoring metric in this embodiment. For example, the monitoring metrics in this embodiment may include at least one of the following: storage space utilization, central processing unit (CPU) runtime, disk space, network traffic, disk response, disk latency, server response, etc. In this embodiment, the value of the monitoring metric refers to the specific value of that monitoring metric in the storage node. For example, the storage space utilization rate is 30%.

[0060] In one implementation, to facilitate subsequent steps based on tags, step 101 is implemented as follows: The original monitoring data of the storage node is obtained from the registration system running on the registration device via the monitoring system. The original monitoring data includes the original tag of the storage node and the values ​​of the monitoring indicators of the storage node. The original tag of the storage node is converted into a tag of the storage node via the monitoring system, and the converted monitoring data is used as the monitoring data. The monitoring data is then stored.

[0061] Continuing with the example based on the modified configuration file above, relabel_configs: represents the renamed labels (i.e., the labels in this example), and source_labels: represents the original labels.

[0062] In this implementation, when converting the original labels of storage nodes into standardized labels, the converted labels are standardized. Optionally, the monitoring system can convert the original labels of storage nodes into standardized labels based on a pre-stored label conversion mapping relationship. This label conversion mapping relationship is used to characterize the mapping relationship between the original labels and the standardized labels. This implementation can achieve label standardization, facilitating subsequent data querying or trend prediction based on label granularity.

[0063] It should be noted that in step 101, monitoring data can be acquired multiple times at a preset frequency, and the acquired monitoring data can be stored. For example, the frequency in this embodiment can be 0.1Hz.

[0064] Step 102: Obtain the running trend prediction request.

[0065] The operational trend prediction request includes the tags to be predicted and the monitoring indicators to be predicted.

[0066] In this embodiment, the operation trend prediction request can be sent upon triggering by maintenance personnel, or it can be generated by other devices and sent to the monitoring device. This embodiment can achieve operation trend prediction based on tag granularity and monitoring indicator granularity.

[0067] For example, the label to be predicted could be, for instance, Park 1, and the monitoring metrics to be predicted could be, for instance, CPU runtime and storage space utilization.

[0068] Step 103: Using the monitoring system, based on the tags and monitoring indicators of the storage nodes in the monitoring data, determine the value of the first target monitoring indicator that matches both the tag to be predicted and the monitoring indicator to be predicted.

[0069] After receiving the operation trend prediction request, we can first determine the value of the first target monitoring indicator that matches both the label to be predicted and the monitoring indicator to be predicted in the operation trend prediction request.

[0070] One possible implementation of step 103 is as follows: Based on the labels of the storage nodes in the monitoring data, determine the monitoring data that matches the label to be predicted; from the monitoring data that matches the label to be predicted, determine the value of the first target monitoring indicator that matches the monitoring indicator to be predicted.

[0071] In one implementation, the monitoring system can determine the value of a first target monitoring indicator that matches both the label to be predicted and the monitoring indicator to be predicted, based on the tags and monitoring indicators of the stored nodes in the monitoring data acquired within a preset time period. For example, the preset time period can be monitoring data acquired in the past 12 hours or monitoring data acquired in the past 48 hours.

[0072] In another implementation, to improve the accuracy of operational trend prediction, the monitoring data also includes the timestamps of the values ​​of each monitoring indicator, and the operational trend prediction request also includes the prediction duration. Correspondingly, the implementation process of step 103 includes the following steps: using the monitoring system, determining candidate monitoring data that matches the prediction duration from the timestamps of the monitoring indicator values, wherein the longer the prediction duration, the earlier the timestamps of the candidate monitoring data; and determining the value of a first target monitoring indicator that matches both the label to be predicted and the monitoring indicator to be predicted, based on the labels of the storage nodes and the monitoring indicators in the candidate monitoring data.

[0073] This implementation method corresponds to the following scenario: Suppose we want to predict the trend 3 days from now, we need monitoring data from the past 10 days; suppose we want to predict the trend 5 days from now, we need monitoring data from the past 20 days. Here, 3 days and 5 days refer to the prediction duration. The longer the prediction duration, the more monitoring data is needed, that is, the earlier the timestamps of the required monitoring data. This implementation method achieves prediction based on monitoring data matching the prediction duration, thus improving prediction accuracy.

[0074] Step 104: Based on the value of the first target monitoring indicator, determine the operating trend of the storage node with the label to be predicted based on the monitoring indicator to be predicted.

[0075] In this embodiment, the operating trend of the storage node refers to the change of the storage node's predicted monitoring indicators over time after the current moment.

[0076] Optionally, step 104 can also be implemented through a monitoring system, that is, by using the monitoring system to determine the operating trend of the storage node with the label to be predicted based on the value of the first target monitoring indicator.

[0077] In step 104, in scenarios where there are multiple monitoring indicators to be predicted, the operating trend of each monitoring indicator to be predicted for a storage node with a label to be predicted can be determined.

[0078] For example, assuming the label to be predicted is "Campus 1", and the monitoring metrics to be predicted include CPU runtime and storage space utilization, then the values ​​of the first target monitoring metrics include: the CPU runtime of each storage node in Campus 1, and the storage space utilization of each storage node in Campus 1. In step 104, the operating trend of the CPU runtime of each storage node in Campus 1 can be determined based on the CPU runtime of each storage node in Campus 1, and the operating trend of the storage space utilization of each storage node in Campus 1 can be determined based on the storage space utilization of each storage node in Campus 1.

[0079] In one implementation, a big data prediction model can be used to determine the operating trend of storage nodes with unpredictable labels based on unpredictable monitoring indicators.

[0080] In another implementation, the operating trend of storage nodes with tags to be predicted can be determined based on the monitoring indicators to be predicted through a trend prediction function in the monitoring system.

[0081] Furthermore, after determining the operational trend of a storage node, the system can also identify the moment when the value of the predicted monitoring indicator exceeds a pre-defined threshold, and then relay this moment to the operations and maintenance personnel or control equipment as a notification. For example, if the threshold is 90%, the predicted monitoring indicator is storage space occupancy, and the predicted label is "Campus 1," and the operational trend of a storage node's storage space occupancy indicates that the node's occupancy will exceed 90% in 10 days, then the system will notify the operations and maintenance personnel or control equipment that the occupancy will exceed 90% in 10 days. This allows the operations and maintenance personnel to determine subsequent handling strategies and improve the operational reliability of the storage node.

[0082] Optionally, the method further includes the following step: sending monitoring data to the display device through the monitoring system. The data visualization platform in the display device is used to visualize the monitoring data at the tag level according to predefined display rules, and to send warning information when the monitoring data meets the warning rules according to predefined warning rules.

[0083] The data visualization platform in this embodiment can visualize monitoring data at the tag level. For example, monitoring data with the same tag can be displayed in the same graph for visualization. Furthermore, the data visualization platform can also visualize monitoring data at the monitoring metric level, in addition to tag-based granularity.

[0084] Optionally, the data visualization platform in this embodiment can also send early warning information when the monitored data meets the predefined early warning rules. In this embodiment, the early warning information can be sent via email, instant messaging software, etc.

[0085] Optionally, the data visualization platform in this embodiment can also aggregate monitoring data from each storage node in a storage cluster at the storage cluster level. For example, it can aggregate the values ​​of monitoring metrics such as throughput and response latency of each storage node in a storage cluster.

[0086] Figure 2 This is a schematic diagram illustrating an application scenario of the storage node operation trend prediction method provided in this embodiment of the invention. For example... Figure 2 As shown, each storage node registers or unregisters in the registration system of the registration device. The monitoring device obtains monitoring data from the registration system through the monitoring system. The monitoring device can also send monitoring data to the data visualization platform of the display device through the monitoring system to realize the visualization and alarm of the monitoring data. The storage node operation trend prediction method provided in this embodiment can be applied to the monitoring process in data development, analysis, and industry chain fields.

[0087] The storage node operation trend prediction method provided in this embodiment includes: obtaining and storing monitoring data of storage nodes from a registration system running on a registration device through a monitoring system, wherein the storage nodes are nodes pre-registered in the registration system, and the monitoring data includes the tags of the storage nodes and the values ​​of the monitoring indicators of the storage nodes; obtaining an operation trend prediction request, wherein the operation trend prediction request includes a tag to be predicted and a monitoring indicator to be predicted; determining, through the monitoring system, the value of a first target monitoring indicator that matches both the tag to be predicted and the monitoring indicator to be predicted, based on the tags of the storage nodes and the monitoring indicators in the monitoring data; and determining the operation trend of the storage nodes with the tags to be predicted based on the monitoring indicators to be predicted, based on the value of the first target monitoring indicator. This method has the following technical effects: On the one hand, the monitoring system retrieves monitoring data through the registration system, and storage nodes can dynamically register or unregister from the registration system. Therefore, the monitoring system can perceive changes in storage nodes in the distributed environment, enabling the monitoring process to adapt to constantly changing storage nodes. This is equivalent to introducing a self-discovery mechanism for the monitoring system, reducing human maintenance costs, and thus improving monitoring efficiency, flexibility, and adaptability. On the other hand, it can also predict operational trends based on monitoring data, improving the comprehensiveness of monitoring. Furthermore, it can predict operational trends based on tag granularity and monitoring indicator granularity, improving the accuracy of monitoring.

[0088] Figure 3 This is a flowchart of another storage node operation trend prediction method provided in this embodiment of the invention. The storage node operation trend prediction method provided in this embodiment... Figure 1 Based on the illustrated embodiments and various optional implementations, the implementation method for the subsequent query process is described in detail. For example... Figure 3 As shown, the storage node operation trend prediction method provided in this embodiment includes the following steps 301 to 308.

[0089] Step 301: Obtain the monitoring data of the storage node from the registration system running on the registration device through the monitoring system, and store the monitoring data.

[0090] Storage nodes are nodes that are pre-registered in the registration system, and the monitoring data includes the tags of the storage nodes and the values ​​of the monitoring metrics of the storage nodes.

[0091] Step 302: Obtain the running trend prediction request.

[0092] The operational trend prediction request includes the tags to be predicted and the monitoring indicators to be predicted.

[0093] Step 303: Using the monitoring system, based on the tags and monitoring indicators of the storage nodes in the monitoring data, determine the value of the first target monitoring indicator that matches both the tag to be predicted and the monitoring indicator to be predicted.

[0094] Step 304: Based on the value of the first target monitoring indicator, determine the operating trend of the storage node with the label to be predicted based on the monitoring indicator to be predicted.

[0095] The implementation process and technical principles of steps 301 and 101, 302 and 102, 303 and 103, and 304 and 104 are similar, and will not be repeated here.

[0096] Step 305: Obtain the query request sent by the query device.

[0097] The query request includes the tag to be queried and the calculation method of each monitoring indicator.

[0098] This embodiment enables flexible querying based on monitoring data. The query request in this embodiment can be sent by the querying device at the trigger of maintenance personnel, or it can be generated by the querying device and sent to the monitoring device. The query request in this embodiment can include the tag to be queried and the calculation method for each monitoring indicator.

[0099] Optionally, the calculation method in this embodiment can be the calculation of the average value, the determination of the maximum value, the determination of the minimum value, the determination of the median, etc.

[0100] Step 306: Using the monitoring system, determine the value of the second target monitoring indicator that matches the tag to be queried based on the tags of the storage nodes in the monitoring data.

[0101] This step is similar to step 103. After receiving the query request, the monitoring system determines the value of the second target monitoring indicator that matches the tag to be queried, based on the tags of the stored nodes in the monitoring data.

[0102] In one implementation, the monitoring system can determine the value of a second target monitoring indicator that matches the tag to be queried based on the tags of the stored nodes in the monitoring data obtained within a preset time period.

[0103] In another implementation, the monitoring data also includes the timestamps of the values ​​of each monitoring indicator, and the query request also includes the query duration. Correspondingly, the implementation process of step 306 includes the following steps: using the monitoring system, determining the monitoring data that matches the query duration from the timestamps of the monitoring indicator values; and determining the value of the second target monitoring indicator that matches the tag to be queried based on the tags of the storage nodes in the monitoring data that matches the query duration.

[0104] Step 307: For each monitoring indicator, according to the calculation method corresponding to the monitoring indicator, perform calculation on the value of the second target monitoring indicator to obtain the calculated value.

[0105] Optionally, step 307 can also be implemented through a monitoring system.

[0106] In this embodiment, the calculation methods corresponding to different monitoring indicators can be the same or different, and this embodiment is not limited to this.

[0107] In step 307, the corresponding calculation method can be used for each monitoring indicator to obtain the calculated value.

[0108] Optionally, the monitoring metrics in this embodiment include: storage space utilization rate and CPU runtime. The storage space utilization rate is calculated by determining a minimum value, and the CPU runtime is calculated by determining an average value. In step 307, for the storage space utilization rate, a minimum value can be determined. For the CPU runtime, an average value can be determined.

[0109] Step 308: Feed back the calculated value to the query device.

[0110] Furthermore, in this embodiment, when the calculated value includes the minimum storage space occupancy rate and the average CPU runtime, in the first scenario, after step 308, the method provided in this embodiment further includes the following steps: if the minimum storage space occupancy rate of each storage node with the tag to be queried belonging to the same storage cluster is greater than a preset first storage space occupancy rate threshold, and the average CPU runtime is greater than a preset first CPU runtime threshold, then it is determined that the storage cluster needs to add storage nodes with the tag to be queried.

[0111] The monitoring data uploaded by each storage node may also include the storage node's identifier. The storage cluster to which the storage node belongs is determined based on the storage node's identifier. In the first scenario, a preset first storage space occupancy threshold is a pre-determined value representing the upper limit of storage space occupancy. A preset first CPU runtime threshold is a pre-determined value representing the upper limit of CPU runtime. If the minimum storage space occupancy of all storage nodes with the queried tag in the same storage cluster is greater than the preset first storage space occupancy threshold, and the average CPU runtime is greater than the preset first CPU runtime threshold, it indicates that the number of storage nodes in the storage cluster cannot meet the actual business needs. To improve the reliability of business operations, the storage cluster needs to be expanded.

[0112] Optionally, in the second scenario, after step 308, the method provided in this embodiment further includes the following step: if the minimum storage space occupancy rate of each storage node with the tag to be queried belonging to the same storage cluster is less than a preset second storage space occupancy rate threshold, and the average CPU runtime is less than a preset second CPU runtime threshold, then it is determined that the storage cluster needs to reduce the number of storage nodes with the tag to be queried. Wherein, the preset first storage space occupancy rate threshold is greater than the preset second storage space occupancy rate threshold, and the preset first CPU runtime threshold is greater than the preset second CPU runtime threshold.

[0113] In the second scenario, the preset second storage space occupancy threshold is a pre-determined value representing the lower limit of storage space occupancy. The preset second CPU runtime threshold is a pre-determined value representing the lower limit of CPU runtime. If the minimum storage space occupancy of all storage nodes with the queried tag in the same storage cluster is less than the preset second storage space occupancy threshold, and the average CPU runtime is less than the preset second CPU runtime threshold, it indicates that there is redundancy in the number of storage nodes in the storage cluster, and the performance of some storage nodes is not being fully utilized. To save storage resources, it is necessary to downsize the storage cluster.

[0114] For example, the preset first storage space utilization threshold can be 60%, and the preset second storage space utilization threshold can be 20%. The preset first CPU runtime threshold can be 18 hours, and the preset second CPU runtime threshold can be 2 hours.

[0115] Alternatively, the implementation process of the above three scenarios can also be achieved through a monitoring system.

[0116] Figure 4 This is a schematic diagram of various thresholds in an embodiment of the present invention. For example... Figure 4 As shown, when the minimum storage space occupancy rate of each storage node with the tag to be queried in the same storage cluster is greater than the preset first storage space occupancy rate threshold, and the average CPU runtime is greater than the preset first CPU runtime threshold, it is determined that the storage cluster needs to be expanded.

[0117] If the minimum storage space utilization rate of each storage node with the tag to be queried belonging to the same storage cluster is less than the preset second storage space utilization rate threshold, and the average CPU runtime is less than the preset second CPU runtime threshold, then it is determined that the storage cluster needs to be scaled down.

[0118] In the third scenario, if the minimum storage space occupancy rate of each storage node with the tag to be queried belonging to the same storage cluster is greater than or equal to the preset second storage space occupancy rate threshold and less than or equal to the preset first storage space occupancy rate threshold, and / or the average CPU runtime of each storage node with the tag to be queried belonging to the same storage cluster is greater than or equal to the preset second CPU runtime threshold and less than or equal to the preset first CPU runtime threshold, then the number of storage nodes included in the storage cluster is determined to be appropriate.

[0119] In the three scenarios mentioned above, based on tag-based queries, monitoring metrics can be used to determine whether the storage cluster capacity is appropriate, thereby optimizing resource utilization and improving data development and analysis efficiency.

[0120] The storage node operation trend prediction method provided in this embodiment calculates the value of the second target monitoring indicator based on the corresponding calculation method for each monitoring indicator. This allows for querying monitoring data at the tag level and enables calculation of monitoring indicator values ​​according to specified methods, meeting diverse monitoring needs. Furthermore, based on tag-level queries, the method can also determine the suitability of the storage cluster capacity at the monitoring indicator level, optimizing storage resource utilization, improving business operation reliability, and saving storage resources.

[0121] Figure 5 This is a schematic diagram of a storage node operation trend prediction device provided in an embodiment of the present invention. The device is installed on a monitoring device, which runs a monitoring system. Figure 5 As shown, the storage node operation trend prediction device provided in this embodiment includes the following modules: a first acquisition module 51, a second acquisition module 52, a first determination module 53, and a second determination module 54.

[0122] The first acquisition module 51 is used to acquire monitoring data of the storage node from the registration system running in the registration device through the monitoring system, and to store the monitoring data.

[0123] The storage node is a node pre-registered in the registration system. The monitoring data includes the tag of the storage node and the values ​​of the monitoring metrics of the storage node.

[0124] The second acquisition module 52 is used to acquire running trend prediction requests.

[0125] The operational trend prediction request includes the tags to be predicted and the monitoring indicators to be predicted.

[0126] The first determining module 53 is used to determine, through the monitoring system, the value of a first target monitoring indicator that matches both the label to be predicted and the monitoring indicator to be predicted, based on the label of the storage node in the monitoring data and the monitoring indicator.

[0127] The second determining module 54 is used to determine the operating trend of the storage node with the label to be predicted based on the value of the first target monitoring indicator.

[0128] In one embodiment, the device further includes a configuration file modification module for modifying the configuration file of the monitoring system to register the registration system with the monitoring system. The modified configuration file includes the address information of the registration system.

[0129] In one embodiment, the monitoring data further includes timestamps of the values ​​of each monitoring indicator, and the operational trend prediction request further includes a prediction duration. The first determining module 53 is specifically configured to: determine, through the monitoring system, candidate monitoring data matching the prediction duration from the timestamps of the monitoring indicator values, wherein the longer the prediction duration, the earlier the timestamps of the candidate monitoring data; and determine the value of a first target monitoring indicator that matches both the label to be predicted and the monitoring indicator to be predicted, based on the labels of the storage nodes in the candidate monitoring data and the monitoring indicators.

[0130] In one embodiment, the device further includes: a third acquisition module, a third determination module, a calculation module, and a feedback module.

[0131] The third acquisition module is used to acquire query requests sent by the query device.

[0132] The query request includes the tag to be queried and the calculation method of each monitoring indicator.

[0133] The third determining module is used to determine the value of a second target monitoring indicator that matches the tag to be queried, based on the tags of the storage nodes in the monitoring data, through the monitoring system.

[0134] The calculation module is used to perform calculations on the value of the second target monitoring indicator for each monitoring indicator, according to the calculation method corresponding to the monitoring indicator, to obtain the calculated value.

[0135] The feedback module is used to send the calculated value back to the query device.

[0136] In one embodiment, the monitoring metrics include: storage space utilization rate and CPU runtime. The storage space utilization rate is calculated by determining a minimum value, and the CPU runtime is calculated by determining an average value. The calculated values ​​include the minimum storage space utilization rate and the average CPU runtime. The device further includes a fourth determining module and a fifth determining module.

[0137] The fourth determining module is used to determine that if the minimum storage space occupancy rate of each storage node with the tag to be queried belonging to the same storage cluster is greater than a preset first storage space occupancy rate threshold, and the average CPU runtime is greater than a preset first CPU runtime threshold, then the storage cluster needs to add storage nodes with the tag to be queried.

[0138] The fifth determining module is used to determine that if the minimum storage space occupancy rate of each storage node with the tag to be queried belonging to the same storage cluster is less than a preset second storage space occupancy rate threshold, and the average CPU runtime is less than a preset second CPU runtime threshold, then the storage cluster needs to reduce the number of storage nodes with the tag to be queried.

[0139] Among them, the preset first storage space occupancy rate threshold is greater than the preset second storage space occupancy rate threshold, and the preset first CPU runtime threshold is greater than the preset second CPU runtime threshold.

[0140] In one embodiment, the first acquisition module 51 is specifically used to: acquire the original monitoring data of the storage node from the registration system running in the registration device through the monitoring system, wherein the original monitoring data includes the original tag of the storage node and the value of the monitoring index of the storage node; convert the original tag of the storage node into the tag of the storage node through the monitoring system, and use the converted monitoring data as the monitoring data; and store the monitoring data.

[0141] In one embodiment, the device further includes a sending module, configured to send the monitoring data to a display device via the monitoring system. The data visualization platform in the display device is configured to visualize the monitoring data at a tag-based granularity according to predefined display rules, and to send warning information when the monitoring data conforms to predefined warning rules.

[0142] The storage node operation trend prediction device provided in the embodiments of the present invention can execute the storage node operation trend prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0143] Figure 6This is a schematic diagram of the structure of an electronic device implementing the storage node operation trend prediction method of this invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0144] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0145] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0146] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a storage node running a trend prediction method.

[0147] In some embodiments, the storage node operation trend prediction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the storage node operation trend prediction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the storage node operation trend prediction method by any other suitable means (e.g., by means of firmware).

[0148] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0149] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0150] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0151] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0152] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0153] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0154] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the storage node operation trend prediction method provided in any embodiment of this invention.

[0155] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0156] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0157] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for predicting the operating trend of storage nodes, characterized in that, Applied to monitoring equipment, wherein a monitoring system runs on the monitoring equipment, the method includes: The monitoring system obtains and stores monitoring data of storage nodes from the registration system running in the registration device; wherein the storage node is a node pre-registered in the registration system, and the monitoring data includes the tag of the storage node and the value of the monitoring index of the storage node. Obtain an operational trend prediction request; wherein, the operational trend prediction request includes the label to be predicted and the monitoring indicator to be predicted; The monitoring system determines the value of a first target monitoring indicator that matches both the label to be predicted and the monitoring indicator to be predicted, based on the labels of the storage nodes in the monitoring data and the monitoring indicators. Based on the value of the first target monitoring indicator, determine the operating trend of the storage node with the predicted label based on the predicted monitoring indicator; The method further includes: Obtain the query request sent by the query device; wherein, the query request includes the tag to be queried and the calculation method of each monitoring indicator; The monitoring system determines the value of a second target monitoring indicator that matches the tag to be queried based on the tags of the storage nodes in the monitoring data. For each monitoring indicator, the value corresponding to the monitoring indicator in the value of the second target monitoring indicator is calculated according to the calculation method corresponding to the monitoring indicator to obtain the calculated value; The calculated value is returned to the query device; The monitoring metrics include: storage space utilization rate and CPU runtime. The storage space utilization rate is calculated by determining a minimum value, and the CPU runtime is calculated by determining an average value. The calculated values ​​include the minimum storage space utilization rate and the average CPU runtime. The method further includes: If the minimum storage space occupancy rate of each storage node with the tag to be queried belonging to the same storage cluster is greater than the preset first storage space occupancy rate threshold, and the average CPU runtime is greater than the preset first CPU runtime threshold, then it is determined that the storage cluster needs to add storage nodes with the tag to be queried. If the minimum storage space utilization rate of each storage node with the tag to be queried belonging to the same storage cluster is less than the preset second storage space utilization rate threshold, and the average CPU runtime is less than the preset second CPU runtime threshold, then it is determined that the storage cluster needs to reduce the number of storage nodes with the tag to be queried. Among them, the preset first storage space occupancy rate threshold is greater than the preset second storage space occupancy rate threshold, and the preset first CPU runtime threshold is greater than the preset second CPU runtime threshold.

2. The method according to claim 1, characterized in that, Before obtaining monitoring data of storage nodes from the registration system running in the registration device through the monitoring system, the method further includes: Modify the configuration file of the monitoring system to register the registration system to the monitoring system; wherein the modified configuration file includes the address information of the registration system.

3. The method according to claim 1, characterized in that, The monitoring data also includes timestamps of the values ​​of each monitoring indicator, and the operation trend prediction request also includes prediction duration; The step of determining the value of a first target monitoring indicator that matches both the label to be predicted and the monitoring indicator to be predicted, based on the tags of the storage nodes in the monitoring data and the monitoring indicators, through the monitoring system, includes: The monitoring system determines candidate monitoring data that matches the predicted duration from the timestamps of the monitoring indicator values; wherein, the longer the predicted duration, the earlier the timestamps of the candidate monitoring data. Based on the labels of the storage nodes in the candidate monitoring data and the monitoring indicators, determine the value of a first target monitoring indicator that matches both the label to be predicted and the monitoring indicator to be predicted.

4. The method according to any one of claims 1 to 3, characterized in that, The step of obtaining and storing monitoring data of storage nodes from the registration system running on the registration device through the monitoring system includes: The monitoring system obtains the original monitoring data of the storage nodes from the registration system running in the registration device; wherein, the original monitoring data includes the original tag of the storage node and the value of the monitoring index of the storage node; The monitoring system converts the original tags of the storage nodes into tags for the storage nodes, and uses the converted monitoring data as the monitoring data. Store the monitoring data.

5. The method according to any one of claims 1 to 3, characterized in that, The method further includes: The monitoring system sends the monitoring data to the display device; wherein, the data visualization platform in the display device is used to visualize the monitoring data at the granularity of tags according to predefined display rules, and to send warning information when the monitoring data meets the warning rules according to predefined warning rules.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the storage node operation trend prediction method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that enables a processor to execute the storage node operation trend prediction method according to any one of claims 1 to 5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the storage node operation trend prediction method as described in any one of claims 1 to 5.

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