Cloud computer cluster monitoring method and device, electronic equipment, storage medium and program product

Abstract

Embodiments of the present application provide a cloud computer cluster monitoring method and device, electronic equipment, storage medium and program product. The cloud computer cluster monitoring method comprises: obtaining running data of at least one cloud computer in a current period; and establishing a relationship between the running data and a virtual object corresponding to the at least one cloud computer, wherein the virtual object is a simulation model of the corresponding cloud computer that is pre-constructed. Therefore, after determining the running state of the cloud computer based on the running data of the cloud computer and the running standard corresponding to the running data for any cloud computer, the virtual object corresponding to the cloud computer can be displayed in a display mode corresponding to the running state in the current period. In this way, the cloud computer deployed on the cloud server can be monitored according to the virtual object, and the cloud computer can be accurately adjusted in resources.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a cloud computer cluster monitoring method, device, electronic device, storage medium, and program product. Background Technology

[0002] Cloud computing is a cloud computing technology that deploys the computing power of a computing system on a cloud server, allowing users to access the computing system through terminal devices and perform calculations based on the computing system.

[0003] In industrial production and daily life scenarios, multiple cloud computers are typically deployed. Due to the large number of cloud computers and the fact that the computing systems are deployed on cloud servers, it is difficult to monitor the operational status of multiple cloud computers on the terminal device side, making it difficult to make accurate resource adjustments on the terminal device side. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this application provides a cloud computer cluster monitoring method, device, electronic device, storage medium, and program product.

[0005] According to a first aspect of the embodiments of this application, a cloud computer cluster monitoring method is provided, the method comprising: acquiring the operating data of at least one cloud computer in the current period, and establishing a relationship between the operating data and a virtual object corresponding to the at least one cloud computer; wherein the virtual object is a pre-constructed simulation model of the corresponding cloud computer;

[0006] For any cloud computer, the operating status of the cloud computer is determined based on the operating data of the cloud computer and the operating standard corresponding to the operating data. The operating standard is the operating specification corresponding to the normal operating status of the cloud computer.

[0007] Within the current cycle, the virtual object corresponding to the cloud computer is displayed in the display mode corresponding to the running state.

[0008] According to a second aspect of the embodiments of this application, a cloud computer cluster monitoring device is provided, the device comprising:

[0009] The data acquisition module is used to acquire the operating data of at least one cloud computer within the current period, and establish a relationship between the operating data and the virtual object corresponding to the at least one cloud computer; the virtual object is a pre-constructed simulation model of the corresponding cloud computer.

[0010] The status determination module is used to determine the operating status of any cloud computer based on the operating data of the cloud computer and the operating standard corresponding to the operating data. The operating standard is the operating specification corresponding to the normal operating status of the cloud computer.

[0011] The display module is used to display the virtual object corresponding to the cloud computer in the display mode corresponding to the running state during the current cycle.

[0012] According to a third aspect of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being executed by the processor to cause the electronic device to perform the method as described in the first aspect.

[0013] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided having a computer program stored thereon, the program being executed by a processor to implement the method as described in the first aspect.

[0014] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including instructions that, when executed on a computer, cause the computer to perform the method as described in the first aspect.

[0015] The technical solutions provided in this application embodiment may include the following beneficial effects:

[0016] The cloud computer cluster monitoring method in this application embodiment obtains the operating data of at least one cloud computer within the current period, establishes a relationship between the operating data and the virtual object corresponding to the at least one cloud computer, wherein the virtual object is a pre-constructed simulation model of the corresponding cloud computer. The simulation model can display the cloud computer and its operating data. Then, for any cloud computer, after determining its operating status based on the operating data and the corresponding operating standards, the virtual object corresponding to the cloud computer can be displayed in the current period according to the display mode corresponding to the operating status. In this way, the cloud computers deployed on the cloud server can be monitored according to the virtual object, and the cloud computers can be accurately adjusted in terms of resources.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly described below. It should be understood that those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0019] Figure 1 This is a schematic diagram of a scenario corresponding to the cloud computer cluster monitoring method provided in the embodiments of this application;

[0020] Figure 2This is an exemplary method flowchart of the cloud computer cluster monitoring method provided in the embodiments of this application;

[0021] Figure 3 This is a schematic diagram of a virtual object for the cloud computer cluster monitoring method provided in the embodiments of this application;

[0022] Figure 4 A schematic diagram of virtual object partitioning display for the cloud computer cluster monitoring method provided in this application embodiment;

[0023] Figure 5 This is an exemplary interactive diagram illustrating the cloud computer cluster monitoring method provided in the embodiments of this application;

[0024] Figure 6 This is a schematic diagram of an exemplary simulation environment for the cloud computer cluster monitoring method provided in the embodiments of this application;

[0025] Figure 7 This is an exemplary schematic diagram of the cloud computer cluster monitoring device provided in the embodiments of this application;

[0026] Figure 8 This is an exemplary structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings.

[0028] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments and is not intended to limit the technical solutions of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise.

[0029] It should also be understood that although the terms "first," "second," etc., may be used in the following embodiments to describe a certain type of object, the objects should not be limited to these terms. These terms are used to distinguish the specific implementation objects of that type of object.

[0030] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0031] Cloud computing is a virtualization service that provides computing resources over a network. It deploys the computing power of a computing system on cloud servers, allowing users to access the system through terminal devices and perform calculations. Cloud computing can be applied to various industrial and daily life scenarios. For example, in an application scenario where multiple cloud computers run simultaneously for production and daily life, the administrators need to adjust the resource deployment of the cloud computers based on their operational status.

[0032] However, in related technologies, statistics on the operation of cloud computers are typically only available on the server side providing the computing power resources. Furthermore, these server-side statistics usually represent the overall operation of all cloud computers, not the individual operation of each cloud computer. Moreover, because cloud computers differ from traditional physical computers, understanding the operational status of a particular cloud computer requires a certain level of expertise.

[0033] In view of this, this application proposes a cloud computer cluster monitoring method, which can be applied to a cloud computer cluster monitoring system. For example... Figure 1 The image shows a cloud computer cluster monitoring system that implements a cloud computer cluster monitoring method. Figure 1 The cloud computer cluster monitoring system shown includes a monitoring terminal 10 and at least one cloud computer 20. The monitoring terminal 10 includes a processor 101 and a display device 102. The at least one cloud computer 20 is connected to the monitoring terminal 10 via a network.

[0034] The monitoring terminal 10 is used to acquire the operating data of at least one cloud computer 20, the processor 101 is used to calculate relevant monitoring algorithms based on the acquired operating data of the at least one cloud computer, and the display device 102 is used to display the calculation results. Specifically, the monitoring terminal 10 can be any device with processing power and display function, such as a desktop computer or laptop computer and a cloud computer (the processor 101 is deployed on a cloud server). The display device 102 is used to visualize the output of the monitoring terminal. Specifically, the display device 102 can be a monitor, projector, television, etc. In some embodiments, depending on actual needs, the monitoring terminal may also include a storage device (not shown in the figure) for storing relevant running programs and calculation results, etc.

[0035] At least one cloud computer (20) is a virtual computer running on a remote server, accessible to users via the network. In actual use, users can access the cloud computer from any location and any device via the network. The computing power of the cloud computer is deployed on the cloud server, reducing dependence on physical hardware.

[0036] In some embodiments, if the number of at least one cloud computer is one, and the cloud computer's operating status is monitored by a device running the cloud computer, then at least one cloud computer 20 and the monitoring terminal 10 can also be one terminal.

[0037] It should be understood that Figure 1 The schematic diagram of the application scenario is merely an exemplary representation of the cloud computer cluster monitoring system involved in the embodiments of this application, and does not constitute a limitation on the technical solutions of the embodiments of this application.

[0038] In the above operating environment, the embodiments of this application provide as follows: Figure 2 The cloud PC cluster monitoring method is shown below. Please refer to it. Figure 2 This cloud computer cluster monitoring method is applied to, for example, Figure 1 Monitoring terminal 10 in the cloud computer cluster. This cloud computer cluster monitoring method includes the following steps:

[0039] In step S201, the running data of at least one cloud computer in the current period is obtained, and the running data is associated with the virtual object corresponding to at least one cloud computer.

[0040] Among them, the virtual object is a pre-built simulation model of the corresponding cloud computer;

[0041] For example, when monitoring at least one cloud computer, it is necessary to periodically acquire the operating data of each cloud computer to achieve real-time monitoring of each cloud computer. Specifically, the period for acquiring the operating data of each cloud computer can be determined according to the actual situation, and this application does not limit the time setting of the period.

[0042] Operational data of at least one cloud PC can be obtained through the cloud PC provider's corresponding Application Programming Interface (API), allowing users to access this data. These APIs can provide detailed resource usage, performance metrics, and status information. Third-party monitoring tools can also be used to monitor and collect cloud PC operational data. This operational data can include system performance metrics, application performance, security-related data, user behavior data, network data, and database performance, etc. This application does not limit the specific data type or quantity of operational data; those skilled in the art can determine this based on the actual situation.

[0043] After obtaining the operating data of at least one cloud computer, in order to more intuitively monitor and view the operating status of each cloud computer, a relationship is established between the operating data and the virtual objects corresponding to each cloud computer.

[0044] The virtual objects can be pre-built simulation models for each cloud computer, such as... Figure 3 The image shows a schematic diagram of one of the cloud computer virtual objects. When establishing a relationship with a virtual object, the identifier in the runtime data can be matched with the identifier of the virtual object. The successfully matched virtual object is then associated with the corresponding runtime data, allowing the corresponding runtime data to be displayed through the virtual object.

[0045] After establishing the relationship between runtime data and virtual objects, relevant operations and maintenance personnel can directly view the runtime data of the corresponding cloud computer by viewing the virtual object corresponding to the cloud computer.

[0046] In step S202, for any cloud computer, the operating status of the cloud computer is determined based on the operating data of the cloud computer and the corresponding operating standards.

[0047] For example, after obtaining the operating data of each cloud computer and establishing a relationship with the corresponding virtual object, the operating data can be further analyzed to determine whether the operating status of each cloud computer is normal, and resource scheduling can be performed based on the operating status.

[0048] The acquired operational data includes various types. Therefore, when analyzing the operational data, the operational status can be determined based on the type of operational data and the corresponding operational standards. In some embodiments, if the operational data is a system performance indicator, it can be compared with a system performance indicator threshold. If it exceeds the threshold, it indicates that the operation is overloaded. If the operational data is security-related data, the operational status is determined based on the corresponding security level. If the security level is high, it indicates that there is a security problem. If the operational data is network data, the operational status is determined based on the corresponding network data indicators such as latency and packet loss rate. The system performance indicator threshold, security level, and indicators such as latency and packet loss rate are the operational standards.

[0049] After determining the various types of operating states, some embodiments can also determine an overall operating state based on these states to represent the overall operating state of the cloud computer. Specifically, weights can be assigned to the operating states of each type of operating data, and a weighted sum can be performed based on the level of each operating state and its corresponding weight to obtain the overall operating state.

[0050] The operating standards corresponding to the operating data can be set by administrators based on actual conditions or based on the system performance of the cloud computer. The standards can be adjusted according to actual conditions without affecting the operation of the cloud computer. This application does not limit the specific form of the operating standards.

[0051] In step S203, within the current cycle, the virtual object corresponding to the cloud computer is displayed in the display mode corresponding to the running state.

[0052] For example, after determining the operating status of the cloud computer based on operating data and corresponding operating standards, each operating status of the cloud computer corresponds to a display mode. Specifically, if the operating status includes three types: abnormal operation, normal operation, and warning, then when displaying the operating status of the cloud computer in a virtual object, to ensure that the virtual object accurately maps to the real cloud computer, the display mode corresponding to the abnormal operation is set to red, the display mode for the normal operation is set to blue, and the display mode for the warning is set to yellow, to distinguish cloud computers in different operating states. When displaying the virtual object corresponding to the cloud computer in the display mode corresponding to the operating status, the corresponding virtual object can be rendered with the corresponding "red display, blue display, or yellow display".

[0053] As can be seen, by adopting the embodiments of this application, the operating data of at least one cloud computer in the current period is obtained, and a relationship is established between the operating data and the virtual objects corresponding to at least one cloud computer. The virtual objects are pre-built simulation models of the corresponding cloud computers. Therefore, for any cloud computer, after determining the operating status of the cloud computer based on the operating data and the operating standards corresponding to the operating data, the virtual objects corresponding to the cloud computer can be displayed in the current period in the display mode corresponding to the operating status. In this way, the cloud computers deployed on the cloud server can be monitored according to the virtual objects, and the resources of the cloud computers can be accurately adjusted.

[0054] In the above embodiments, it is necessary to establish a relationship between the cloud computer's operating data and the corresponding virtual object. Therefore, the corresponding virtual object needs to be established before acquiring the operating data. Based on the above embodiments, the method further includes: acquiring attribute information of at least one cloud computer; the attribute information includes the cloud computer's type information and configuration information; for any cloud computer, constructing a virtual object corresponding to the cloud computer based on the cloud computer's configuration information and type information; determining the display area of ​​the virtual object based on the type information, and displaying the virtual object in the display area.

[0055] For example, before constructing virtual objects, it is necessary to determine the attribute information of each cloud computer so that the virtual objects constructed based on this attribute information correspond to the cloud computers. The attribute information is used to characterize the identity and performance of the corresponding cloud computer, and may specifically include processor type, memory size, disk size, and network transmission protocol.

[0056] In this embodiment, the attribute information includes configuration information and type information. The configuration information may include the processor type, memory size, disk size, and network transmission protocol, etc., used to configure the virtual object. The type information can characterize the functional category of the cloud computer, for example, the usage area, departmental division, and functional division of the cloud computer. Setting type information for virtual objects can establish a topology between virtual objects, enabling subsequent classified monitoring and corresponding data viewing and display based on this topology. This embodiment does not limit the specific type and quantity of attribute information; those skilled in the art can determine it according to the actual situation.

[0057] When constructing a virtual object, you can first create a blank virtual object for the cloud computer, where the blank virtual object is as follows: Figure 3 As shown, a blank virtual object is then configured based on the configuration information to obtain the initial virtual object; the type of the initial virtual object is set based on the type information to obtain the virtual object.

[0058] Specifically, virtual objects can be constructed in the same number as at least one cloud computer, and then configured separately according to configuration and type information. For example... Figure 4 The figure shows a virtual object constructed according to an embodiment of this application. The figure includes virtual objects corresponding to two cloud computers, region A and region B. Region A and region B represent the corresponding type information. When displaying each virtual object, based on the different type information, it can be displayed according to... Figure 4 The layout is shown in different areas. Figure 4 The configuration information of the virtual object is not shown.

[0059] In some embodiments, virtual objects can also be constructed by: determining the configuration information that the virtual object needs to include, such as CPU type, memory size, storage type and capacity, network configuration, etc. This information can be obtained from user input, configuration files, or a database. Parsing the configuration information to extract the attribute values ​​required by the virtual object model. Determining the specific implementation or configuration template of the virtual object based on the cloud computer's type information. Type information may include operating system type, pre-installed software, security settings, etc. Selecting a suitable virtualization technology and adjusting the virtual object's configuration according to the requirements of the virtualization technology. Creating the virtual object using command-line tools of the virtualization platform based on the parsed configuration and type information. Initializing the virtual object, including installing the operating system, configuring the network, and allocating storage. Implementing a configuration management module for monitoring and adjusting the virtual object's configuration. Providing a user interface that allows users to view the virtual object's status, configuration, and performance metrics. Supporting user management and operation of virtual objects through the interface.

[0060] During the operation of a cloud PC, determining its operational status involves two aspects. First, it involves assessing whether the processor and memory usage percentages are excessively high, potentially impacting normal operation, or whether network latency is within normal thresholds. Second, it involves analyzing data such as whether the cloud PC is under network attack, whether there are any abnormal operations performed on it, and trends in memory usage. Based on this data, analysis is conducted to determine whether warning messages are necessary.

[0061] Specifically, if the running data is numerical data, the running status of the cloud computer is determined based on the relationship between the numerical data and a preset threshold; the numerical data includes at least one of data round-trip time, processor usage, memory capacity, disk capacity, and network bandwidth.

[0062] For example, preset thresholds can be set for each type of numerical data. These thresholds can be determined based on practice, historical data, or service level agreements. For instance: the Round Trip Time (RTT) threshold can be set to below 100ms for normal, 100-200ms for warning, and above 200ms for abnormal; the processor utilization threshold can be set to below 60% for normal, 60%-80% for warning, and above 80% for abnormal; the memory capacity threshold can be set to below 70% for normal, 70%-85% for warning, and above 85% for abnormal; the disk capacity threshold can be set to below 80% for normal, 80%-90% for warning, and above 90% for abnormal; and the network bandwidth threshold can be set to define the boundaries between normal, warning, and abnormal based on the actual bandwidth capacity.

[0063] The running data is compared with corresponding preset thresholds to determine the status of each running data point. This analysis can be performed using simple conditional statements or other algorithms.

[0064] Based on the data analysis results, the overall operating status of the cloud PC is determined. For example: all indicators are within the normal range: the operating status is "Normal". One or more indicators are in a warning state: the operating status is "Warning". One or more indicators are in an abnormal state: the operating status is "Abnormal". When the operating status is in a warning or abnormal state, the operating status can be visualized on a virtual object to help relevant managers quickly understand the operating status of the cloud PC.

[0065] If the operational data is time-series data, then the operational status of the cloud computer is obtained by analyzing the time-series data of the current period and the time-series data within the preset historical period. The time-series data includes first-series data composed of the cloud computer's event logs and second-series data composed of numerical data.

[0066] For example, the first time-series data represents the event logs corresponding to the cloud computer, which may include system events, application events, security events, etc. These events are usually timestamped and record the key activities that occurred on the cloud computer at a specific point in time. The second time-series data is the numerical data corresponding to a certain period of time (multiple periods), which may include numerical indicators that change over time, such as processor utilization, memory usage, disk I / O, and network traffic.

[0067] Typically, time-series data, once acquired, needs to be stored in a database. When analyzing operational status, the corresponding data is retrieved to determine the cloud computer's operational status. Time-series data, such as time logs, often contains significant noise and suffers from non-standard data formats. Therefore, before analyzing time-series data, preprocessing can be performed to remove erroneous or incomplete data points, convert the data to a standardized format for easier analysis, and perform data aggregation operations to allow for analysis of data of the same type.

[0068] Specifically, the first time-series data can be analyzed in the following way: Cluster the first time-series data based on the event log types, and calculate the frequency of each type of event log occurrence after clustering; determine the cloud computer's operating status based on the relationship between the frequency and a first preset threshold, where the first preset threshold corresponds to the event log type.

[0069] For example, event logs within a preset time period are clustered according to event type. Specifically, each event log is traversed, and each traversed event log is classified according to event type until all event logs have been traversed, thus achieving clustering and classification of each type of event log. The following clustering methods can also be used: Clustering algorithms include: K-means center-based clustering algorithm, where the number of clusters is pre-specified based on the number of event log types; hierarchical clustering, which creates a hierarchical structure of clusters; and density-based clustering algorithms, which can identify clusters of arbitrary shapes and can identify noise points.

[0070] After clustering is completed, the frequency of each event is calculated based on the number of event logs in each cluster. If the same event occurs frequently, it indicates that the cloud computer is malfunctioning. Specifically, cloud computer malfunctions can include the following situations.

[0071] For example, a severe impact on disk performance at the start or end may indicate an abnormal disk performance issue in the storage system. A network connection loss or device offline may indicate network connectivity problems or device malfunctions. A canceled or failed scheduled instance restart due to system maintenance may indicate an anomaly during system maintenance that led to the maintenance or restart. Device attack incidents indicate security issues requiring immediate attention and handling. In analytical database events, an insert failure rate exceeding a certain percentage (e.g., 10%) may indicate insert failure problems in database operations. A query failure rate exceeding a certain percentage (e.g., 10%) may indicate query failure problems in database operations. Software anomalies in cloud servers may include program crashes, memory leaks, data inconsistencies, and other software-related issues.

[0072] In some embodiments, the first time-series data can also be analyzed in the following ways: Preprocessing the acquired first time-series data, including data cleaning, format conversion, and outlier handling, to ensure data quality and consistency. Extracting features from the preprocessed first time-series data. Analyzing the extracted features using machine learning or deep learning algorithms to identify the cloud computer's operating status. This may involve classification models, such as support vector machines, random forests, or neural networks, to distinguish different operating states, such as normal operation, performance bottlenecks, and faults. Continuously collecting and analyzing the first time-series data to monitor the cloud computer's operating status in real time and predict potential problems. The analysis results can be visualized using charts, dashboards, etc., and reports can be generated to facilitate user understanding and appropriate management measures.

[0073] The second time-series data can be analyzed in the following way: Extract the statistical features of the second time-series data, and based on the relationship between the statistical features and a second preset threshold, obtain the operating status of the cloud computer. The statistical features are used to characterize the changing trend of the numerical data.

[0074] For example, the second time-series data is typically numerical data within a preset time period. Therefore, when performing statistical analysis on the numerical data, the purpose is to analyze the trend of the numerical data within the preset time period. For instance, if the percentage of memory usage gradually increases over time within the preset time period, then even if the percentage of memory usage has not yet exceeded the corresponding threshold in the current period, a warning should be issued so that relevant personnel can adjust the memory size of the corresponding cloud computer to ensure its normal operation.

[0075] Within a preset time period, processor utilization shows a gradual increasing trend. Even if the current processor utilization has not yet exceeded the threshold, a warning should be issued. This may indicate that a process is consuming increasingly more computing resources, which could lead to processor overload if left unchecked. Relevant personnel can check the processes with high processor utilization, optimize the code, or increase the processor resources of the cloud computer.

[0076] Specifically, statistical characteristics of the second time series data are extracted, which can characterize the data's changing trend. For example, the mean can be extracted to reflect the central trend; the variance and standard deviation can be extracted to reflect the degree of dispersion and the magnitude of fluctuation; the standard deviation reflects the degree of dispersion. The maximum and minimum observed values ​​reflect the range of the data. The long-term direction of the data over time is calculated using linear regression or other trend analysis methods.

[0077] In some embodiments, the second time-series data can also be analyzed in the following way: Multiple prediction models can be pre-trained, and the best prediction result can be selected based on the analysis and prediction results of each prediction model on the second time-series data to determine the operating status of the cloud computer.

[0078] Based on the analysis of operational data to obtain the operational status in the above embodiments, in order to more intuitively monitor the operational status of each cloud computer, the following method can be adopted when displaying the corresponding operational status and display mode on the virtual object. Specifically, if the operational status is abnormal, the virtual object is rendered according to the first preset display mode so that the virtual object is displayed in the first preset display mode; if the operational status is normal, the virtual object is rendered according to the second preset display mode so that the virtual object is displayed in the second preset display mode.

[0079] For example, a "running normally" status indicates that the cloud computer is functioning correctly. However, cloud computer malfunctions can include issues related to performance, stability, security, resource management, configuration, and user experience. Different display modes can be set for each type of malfunction, displayed via virtual objects. The corresponding running status for each display mode can also be explained on the relevant display page for more intuitive monitoring.

[0080] Specifically, performance issues may include: persistently high processor usage may cause system slowdowns, affecting user experience; memory usage approaching or reaching 100% may cause application crashes or system performance degradation; slow disk read / write speeds may cause file access delays and increased system response time; and unstable network connections may cause data transmission delays or losses, affecting remote access and application performance.

[0081] Stability issues include: unexpected crashes of the operating system or critical services, which may lead to data loss and service interruption; frequent automatic restarts of cloud computers, which may be a sign of hardware failure or software conflict; and inaccessibility of critical services (such as databases and servers), affecting task continuity.

[0082] Security issues: Unauthorized users or programs are detected attempting to access sensitive data or system resources; the system is infected with viruses or malware, which may lead to data leaks or system damage; known security vulnerabilities exist that could be exploited by attackers.

[0083] Resource management issues: running out of disk space may prevent new data from being written or the system from starting normally; exceeding network bandwidth quotas may result in additional costs or service degradation; expired software licenses may result in limited software functionality or software malfunction.

[0084] Configuration issues: Incorrect configuration may lead to performance degradation, service interruption, or security vulnerabilities; compatibility issues between different software may cause system instability or malfunction; system or application update failures may result in new features being unavailable or introduce new problems.

[0085] User experience issues: The response time for user operations is significantly longer, affecting work efficiency and satisfaction; incorrect graphical interface rendering may lead to visual confusion or missing functions; abnormal audio or video playback affects the user experience of multimedia applications.

[0086] Building upon the above embodiments that display the operating data of each cloud computer on corresponding virtual objects, this application embodiment can also display and statistically analyze the corresponding data based on the constructed virtual objects. Specifically, this application embodiment further includes: in response to obtaining an operation instruction input by a user, reading the target operating data identifier in the operation instruction; displaying the target operating data of at least one matching virtual object; and performing processing on the target operating data corresponding to the operation instruction, wherein the processing corresponding to the operation instruction includes at least one of adding, deleting, and modifying.

[0087] For example, such as Figure 5 As shown, in the display interface of the virtual object corresponding to each cloud computer, during the actual monitoring of the cloud computer, the required data can also be displayed as needed. Specifically, such as... Figure 5As shown, an input box is set in the display interface. In this input box, the user can enter relevant information about the cloud computers they want to query, add, or delete corresponding data. For example, the user can enter the memory usage percentage of all cloud computers in region A. Then, the data on the memory usage percentage of cloud computers categorized as region A will be displayed in the display area. Specifically, it can be displayed in any of the three locations: "Display Area 1", "Display Area 2", and "Display Area 3" according to actual needs. In some embodiments, the memory usage percentage can also be displayed in real time using dynamic charts (such as line charts or bar charts). Alternatively, at least one of the following methods can be used to display the memory usage percentage, such as table display, line chart display, bar chart display, and pie chart display, depending on actual needs.

[0088] When memory usage exceeds a preset threshold, an alert and notification will be displayed. A red warning sign and a pop-up notification will be used to alert the administrator to memory usage. For example, if the memory usage of a cloud computer in Zone A exceeds the preset threshold of 80%, Zone 3 will display a red warning sign and a pop-up notification prompting the administrator to check and potentially increase memory resources.

[0089] In some embodiments, when determining the data to be displayed based on user input information, the data can be determined in the following manner: the target running data identifier includes a virtual object identifier and a running data identifier; the process of determining the target running data may include: matching a target virtual object set in at least one virtual object; any target virtual object in the target virtual object set includes target running data corresponding to the target running data identifier; and filtering the target running data corresponding to the target running data identifier from the running data corresponding to each target virtual object in the target virtual object set.

[0090] For example, there are two ways to determine the target runtime data. One is to query the stored runtime data to match a set of virtual objects containing the target runtime data identifier. This can be achieved through database queries, such as using database statements to filter data records containing a specific identifier. Within the matched target virtual object set, the specific runtime data corresponding to the target runtime data identifier is further filtered out. The other approach is to directly determine the target virtual object set, i.e., the virtual objects corresponding to the target computer set, based on the virtual object identifier. On this basis, the target runtime data corresponding to the runtime data identifier is obtained from each target virtual object in the target virtual object set.

[0091] After the target running data is determined, processing corresponding to the operation instructions can be performed on the target running data. Specifically, this may include displaying the target running data, deleting the already displayed target running data, and adjusting it.

[0092] For example, if the target runtime data is a memory usage percentage, displayed as a line chart, then the corresponding target display template can be found from the pre-defined display templates. Configure the target runtime data with the corresponding target display template and display it as shown in the example. Figure 5 The target data can be viewed in the displayed area shown.

[0093] The ways to interact with users are not limited to Figure 5 The input box format can also be customized with buttons, sliders, full-screen display, reset, patrol, and other interactive methods depending on the actual situation. Figure 5 The function displays, cloud computer area displays, and interaction methods (input boxes) shown are for illustrative purposes only and are not intended to be limiting.

[0094] In some embodiments, based on the above-described display method of cloud computer cluster operation data and operation status, a 3D simulation environment can be established to provide a virtual object operation environment when constructing each virtual object and displaying and adjusting the target operation data. For example... Figure 6 As shown, when setting up the simulation environment, the interaction layer, state layer, model layer and environment layer are defined.

[0095] The interaction layer includes user interface elements such as input boxes, search buttons, menus, and sliders to interact with runtime data in the 3D environment. It provides an intuitive user interface and smooth interaction logic, allowing users to easily view relevant data. Search and grouping functions enable users to quickly find target runtime data, improving work efficiency. The state layer displays runtime data related to virtual objects, including at least one of the following: round-trip time, processor usage, memory capacity, disk capacity, network bandwidth, and log events. The runtime status of the cloud computer is also displayed in the state layer, using different colors or icons to indicate the current state of resources, such as powered on, powered off, or online. Virtual objects in abnormal states are highlighted to help users quickly identify potential problems. The model layer constructs virtual objects and the topological structure formed by type information between them. After constructing the virtual objects, it is updated in real-time based on periodically acquired runtime data and changing attribute data (if any) to ensure consistency with the actual cloud computer. Finally, the environment layer can provide a simulation environment for the interaction layer, state layer, and model layer. In the event of abnormal running data of the cloud computer, the environment of the corresponding virtual object can be highlighted or highlighted to provide a prompt, and animation effects can be added, such as dynamic data loading and animation of state changes, to improve the user experience.

[0096] After constructing the 3D simulation environment based on the above layers, the layout of the virtual objects to be displayed, the statistical results of the running data corresponding to each virtual object, the alarm information display area, etc., can be arranged as follows: Figure 6 The display is shown in the figure. Specifically, the task indicators can be divided into: data statistics, group statistics, input box search area, alarm information display area, running data viewing area (first time series data, etc.), and virtual object display area.

[0097] The task metrics can include online count (online rate and number of online users), 30-minute connection statistics (connection success rate, number of connections, number of failures), and usage statistics (connection, disconnection, power-on, power-off, hibernation, wake-up, overall network). The actual content displayed for these metrics can be customized to meet specific needs. Data statistics can be provided by region, system, terminal, or specification, showing cloud computer usage as a percentage of processor usage, memory usage, and disk capacity. Abnormally used virtual objects can be highlighted during the actual display. Task grouping allows for further grouping of cloud computers based on task concepts, such as by department, project, or task type. Specifically, users can dynamically adjust groups based on real-time data to adapt to different monitoring needs, providing corresponding group views that display key information such as the total number of cloud computers, online count, and abnormal counts for each group. The search area in the input box can search and display relevant data based on actual needs, allowing for fuzzy search to quickly locate target resources. Buttons can include full-screen display functionality for convenient use on different devices, a reset function to restore the 3D environment to its initial state, and a patrol function that automatically traverses all resources to help users quickly understand the overall status. The runtime data viewing area displays detailed metrics for the cloud PC and allows users to query historical metric data for resources, helping them understand the long-term operational status of the resources. The alarm information display area shows current alarm information, including alarm time, alarm count, alarm category, and anomaly analysis. It can also include an alarm list displaying all historical alarm information for easy retrospective analysis. A diagnostic button provides one-click access to a detailed analysis and handling page. Alarm filtering allows users to filter alarms based on alarm level, alarm type, and other criteria to quickly locate critical alarms.

[0098] It is important to note that Figure 6 The construction of the simulation environment and the arrangement of the displayed content in each area are merely illustrative examples, and the embodiments of this application do not limit this.

[0099] The above implementation steps can also be implemented through software modules to achieve the corresponding functions. Corresponding to the above cloud computer cluster monitoring method, this application embodiment can also provide a cloud computer cluster monitoring device.

[0100] like Figure 7 As shown, a cloud computer cluster monitoring device is provided, which may include: a data acquisition module 71, a status determination module 72, and a display module 73.

[0101] For example: the data acquisition module 71 is used to acquire the running data of at least one cloud computer in the current period, and establish a relationship between the running data and the virtual object corresponding to the at least one cloud computer; the virtual object is a pre-constructed simulation model of the corresponding cloud computer;

[0102] The status determination module 72 is used to determine the operating status of any cloud computer based on the operating data of the cloud computer and the operating standard corresponding to the operating data. The operating standard is the operating specification corresponding to the normal operating status of the cloud computer.

[0103] Display module 73 is used to display the virtual object corresponding to the cloud computer in the display mode corresponding to the running state during the current cycle.

[0104] Optionally, before the data acquisition module, the device is further configured to:

[0105] Obtain attribute information of the at least one cloud computer; the attribute information includes at least one of the cloud computer's type information and configuration information;

[0106] For any cloud computer, construct a virtual object corresponding to the cloud computer based on the cloud computer's configuration information and type information;

[0107] The display area of ​​the virtual object is determined based on the type information, and the virtual object is displayed in the display area.

[0108] Optionally, the device is further used for:

[0109] Create a blank virtual object of the cloud computer;

[0110] Configure the blank virtual object based on the configuration information to obtain the initial virtual object;

[0111] The type of the initial virtual object is set based on the type information to obtain the virtual object. The type information is used to characterize the usage type of the cloud computer.

[0112] Optionally, the state determination module is further configured to:

[0113] If the operating data is numerical data, the operating status of the cloud computer is determined based on the relationship between the numerical data and the corresponding preset threshold; the numerical data includes at least one of data round-trip time, processor usage, memory capacity, disk capacity, and network bandwidth; the operating standard is the preset threshold corresponding to the operating data;

[0114] If the running data is time-series data, then the time-series data of the current period and the time-series data within the preset historical period are analyzed to obtain the running status of the cloud computer; the time-series data includes first time-series data composed of the event logs of the cloud computer and second time-series data composed of the numerical data; the running standard is the change trend corresponding to the time-series data.

[0115] Optionally, the operating state includes a first operating state and a second operating state, and the state determination module is further configured to:

[0116] The first time-series data is clustered based on the event log type of the first time-series data, and the frequency of occurrence of each type of event log after clustering is calculated; based on the relationship between the frequency and a first preset threshold, the first operating state of the cloud computer is determined, and the first preset threshold corresponds to the event log type;

[0117] Statistical features of the second time series data are extracted, and the second operating state of the cloud computer is obtained based on the relationship between the statistical features and the second preset threshold. The statistical features are used to characterize the changing trend of the numerical data.

[0118] The operating state is determined based on the anomaly levels corresponding to the first operating state and the second operating state, respectively.

[0119] Optionally, the display module is further configured to:

[0120] If the running status is abnormal, the virtual object is rendered according to the first preset display mode so that the virtual object is displayed in the first preset display mode;

[0121] If the running status is normal, the virtual object is rendered according to the second preset display mode so that the virtual object is displayed in the second preset display mode, and the first preset display mode is different from the second preset display mode.

[0122] Optionally, the device is further configured to: in response to receiving an operation instruction input by a user, read the target running data identifier in the operation instruction;

[0123] Display the target running data of at least one virtual object whose running data identifier matches the target running data identifier;

[0124] The target running data is processed in accordance with the operation instruction, and the processing corresponding to the operation instruction includes at least one of adding, deleting, and modifying.

[0125] Optionally, the apparatus is further configured to: match a target virtual object set among the at least one virtual object based on the virtual object identifier; wherein any target virtual object in the target virtual object set includes target running data corresponding to the target running data identifier;

[0126] Among the running data corresponding to each target virtual object in the target virtual object set, the target running data corresponding to the target running data identifier is filtered out.

[0127] Optionally, the device is further configured to: filter pre-stored target display templates corresponding to the display mode indicated by the operation instruction; the display mode includes at least one of table display, line chart display, bar chart display, and pie chart display;

[0128] Configure the target display template based on the target running data, and display it at a preset position.

[0129] As can be seen, the cloud computer cluster monitoring method in this embodiment of the application obtains the running data of at least one cloud computer in the current period, and establishes a relationship between the running data and the virtual object corresponding to the at least one cloud computer. The virtual object is a pre-constructed simulation model of the corresponding cloud computer. Therefore, for any cloud computer, after determining the running status of the cloud computer based on the running data and the running standards corresponding to the running data, the virtual object corresponding to the cloud computer can be displayed in the current period in the display mode corresponding to the running status. In this way, the cloud computers deployed on the cloud server can be monitored according to the virtual object, and the cloud computers can be accurately adjusted in terms of resources.

[0130] Understandable, Figure 7 The division of the various modules is merely a logical functional division. In actual implementation, the functions of these modules can be integrated into the hardware entity of the electronic device.

[0131] Please refer to Figure 8 , Figure 8 This disclosure provides an electronic device, and further provides an electronic device that can be implemented as follows: Figure 1 The processor 101 of the monitoring terminal 10 is used to execute some or all of the steps of the cloud computer cluster monitoring method described above. Please refer to... Figure 8The electronic device includes a processor 800, a memory 801, a bus 802, and a communication interface 803. The processor 800, the communication interface 803, and the memory 801 are connected via the bus 802. The memory 801 can be implemented as the memory and storage in the processor 101. The memory 801 stores computer programs that can run on the processor 800. The processor 800 can be equivalent to... Figure 1 The CPU in the processor 101 executes the aforementioned provisions of this disclosure when running the computer program. Figure 2 The cloud computer cluster monitoring method provided in any of the embodiments includes, during the execution of the aforementioned method, data and command interaction with the corresponding client through the communication interface 803.

[0132] The memory 801 may include high-speed random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 803 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0133] Bus 802 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Memory 801 is used to store programs, and the processor 800 executes the programs after receiving execution instructions. Figure 2 The illustrated implementation shows that the cloud computer cluster monitoring method can be applied to or implemented by the processor 800.

[0134] The processor 800 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 800 or by instructions in software form. The processor 800 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 801. Processor 800 reads the information in memory 801 and, in conjunction with its hardware, completes the steps of the above method.

[0135] The electronic device provided in this disclosure and the cloud computer cluster monitoring method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0136] This application also provides a computer-readable storage medium storing instructions for monitoring a cloud computer cluster, which, when run on a computer, cause the computer to perform some or all of the steps in the method described in the foregoing embodiments.

[0137] This application also provides a computer program product that includes instructions for monitoring a cloud computer cluster. When run on a computer, it causes the computer to perform some or all of the steps in the method described in the foregoing embodiments.

[0138] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0140] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0141] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0142] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, smartphone, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0143] Although alternative embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make further changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0144] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above description is only a specific embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of this application should be included within the scope of protection of this invention.

Claims

1. A method for monitoring a cloud computer cluster, characterized in that, The method includes: Obtain the operating data of at least one cloud computer within the current period, and establish a relationship between the operating data and the virtual object corresponding to the at least one cloud computer; the virtual object is a pre-constructed simulation model of the corresponding cloud computer. For any cloud computer, the operating status of the cloud computer is determined based on the operating data of the cloud computer and the operating standard corresponding to the operating data. The operating standard is the operating specification corresponding to the normal operating status of the cloud computer. Within the current cycle, the virtual object corresponding to the cloud computer is displayed in the display mode corresponding to the running state.

2. The method according to claim 1, characterized in that, Before acquiring the operational data of at least one cloud computer within the current period, the method further includes: Obtain the attribute information of the at least one cloud computer; the attribute information includes the type information and configuration information of the cloud computer; For any cloud computer, construct a virtual object corresponding to the cloud computer based on the cloud computer's configuration information and type information; The display area of ​​the virtual object is determined based on the type information, and the virtual object is displayed in the display area.

3. The method according to claim 2, characterized in that, The process of constructing the virtual object corresponding to the cloud computer based on its configuration and type information includes: Create a blank virtual object of the cloud computer; Configure the blank virtual object based on the configuration information to obtain the initial virtual object; The type of the initial virtual object is set based on the type information to obtain the virtual object. The type information is used to characterize the usage type of the cloud computer.

4. The method according to claim 1, characterized in that, The process of determining the operating status of the cloud computer based on its operating data and the corresponding operating standards includes: If the operating data is numerical data, the operating status of the cloud computer is determined based on the relationship between the numerical data and the corresponding preset threshold; the numerical data includes at least one of data round-trip time, processor usage, memory capacity, disk capacity, and network bandwidth; the operating standard is the preset threshold corresponding to the operating data; If the running data is time-series data, then the time-series data of the current period and the time-series data within the preset historical period are analyzed to obtain the running status of the cloud computer; the time-series data includes first time-series data composed of the event logs of the cloud computer and second time-series data composed of the numerical data; the running standard is the change trend corresponding to the time-series data.

5. The method according to claim 4, characterized in that, The operating state includes a first operating state and a second operating state. The step of analyzing the time-series data of the current period and the time-series data within a preset historical period to obtain the operating state of the cloud computer includes: The first time-series data is clustered based on the event log type of the first time-series data, and the frequency of occurrence of each type of event log after clustering is calculated; based on the relationship between the frequency and a first preset threshold, the first operating state of the cloud computer is determined, and the first preset threshold corresponds to the event log type; Statistical features of the second time series data are extracted, and the second operating state of the cloud computer is obtained based on the relationship between the statistical features and the second preset threshold. The statistical features are used to characterize the changing trend of the numerical data. The operating state is determined based on the anomaly levels corresponding to the first operating state and the second operating state, respectively.

6. The method according to claim 1, characterized in that, Displaying the virtual object corresponding to the cloud computer in the display mode corresponding to the running state includes: If the running status is abnormal, the virtual object is rendered according to the first preset display mode so that the virtual object is displayed in the first preset display mode; If the running status is normal, the virtual object is rendered according to the second preset display mode so that the virtual object is displayed in the second preset display mode, and the first preset display mode is different from the second preset display mode.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: In response to receiving an operation command input by the user, the target running data identifier in the operation command is read; Display the target running data of at least one virtual object whose running data identifier matches the target running data identifier; The target running data is processed in accordance with the operation instruction, and the processing corresponding to the operation instruction includes at least one of adding, deleting, and modifying.

8. The method according to claim 7, characterized in that, The target runtime data identifier includes a virtual object identifier and a runtime data identifier. Displaying the target runtime data of at least one virtual object whose runtime data identifier matches the target runtime data identifier includes: Based on the virtual object identifier, a target virtual object set is matched among the at least one virtual object; any target virtual object in the target virtual object set includes target running data corresponding to the target running data identifier; Among the running data corresponding to each target virtual object in the target virtual object set, the target running data corresponding to the target running data identifier is filtered out.

9. The method according to claim 7, characterized in that, The process of performing the operation corresponding to the operation instruction on the target running data includes: Based on the display mode indicated by the operation command, a pre-stored target display template corresponding to the display mode is selected; the display mode includes at least one of table display, line chart display, bar chart display, and pie chart display; Configure the target display template based on the target running data, and display it at a preset position.

10. A cloud computer cluster monitoring device, characterized in that, The device includes: The data acquisition module is used to acquire the operating data of at least one cloud computer within the current period, and establish a relationship between the operating data and the virtual object corresponding to the at least one cloud computer; the virtual object is a pre-constructed simulation model of the corresponding cloud computer. The status determination module is used to determine the operating status of any cloud computer based on the operating data of the cloud computer and the operating standard corresponding to the operating data. The operating standard is the operating specification corresponding to the normal operating status of the cloud computer. The display module is used to display the virtual object corresponding to the cloud computer in the display mode corresponding to the running state during the current cycle.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The computer program is executed by the processor to cause the electronic device to perform the method as described in any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by a processor to implement the method as described in any one of claims 1-9.

13. A computer program product, characterized in that, Includes instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-9.

Images