Method and device for monitoring server cluster and storage medium

Abstract

The invention discloses a method and device for monitoring a server cluster and a storage medium, and the method comprises the steps: obtaining first state data of a target server in the server cluster within a first time range; predicting target state data of the target server in a second time range based on the first state data under the condition of determining that the target server is in a normal operation state in the first time range based on the first state data; under the condition that the target server is determined to be in an abnormal operation state within a second time range based on the target state data, determining a target early warning mode of the target equipment; and sending the target early warning mode to a target display device of the target server to indicate the target display device to execute an early warning operation based on the target early warning mode. Through the method and the device, the problem that the abnormal server cannot be efficiently positioned in related technologies is solved, so that the effect of efficiently positioning the abnormal server is achieved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a method, apparatus, and storage medium for monitoring a server cluster. Background Technology

[0002] Current server cluster operation and maintenance management primarily relies on software monitoring platforms such as Zabbix and Prometheus. These platforms provide alerts to operations and maintenance personnel through real-time monitoring of server hardware status. However, software monitoring platforms can only display the server status corresponding to logical identifiers, such as Internet Protocol Addresses (IP addresses) or hostnames, and cannot directly and quickly locate physical devices in the data center environment. For large-scale clusters, this logical-to-physical location conversion process is time-consuming and inefficient, especially during emergency fault response, with an average location time exceeding 5 minutes, severely impacting troubleshooting speed. Moreover, traditional software monitoring and alerting mechanisms depend on the active review of operations and maintenance personnel, especially at night or during unattended periods, which may result in a status awareness vacuum of up to several hours. This leads to critical hardware failures not being responded to and handled in a timely manner, potentially escalating into serious service interruptions. In other words, related technologies suffer from the problem of inefficiently locating abnormal servers. Summary of the Invention

[0003] This application provides a method, apparatus, and storage medium for monitoring server clusters, to at least solve the problem of inefficiently locating abnormal servers in related technologies.

[0004] According to one aspect of the embodiments of this application, a method for monitoring a server cluster is provided, comprising: acquiring first state data of a target server in the server cluster within a first time range, wherein the target server is any server in the server cluster; predicting target state data of the target server within a second time range based on the first state data when it is determined based on the first state data that the target server is in a normal operating state within the first time range, wherein the second time range is a time range after the first time range; determining a target warning mode for a target device when it is determined based on the target state data that the target server is in an abnormal operating state within the second time range, wherein the target device is a device included in the target server that is in the abnormal operating state within the second time range; and sending the target warning mode to a target display device of the target server to instruct the target display device to perform a warning operation based on the target warning mode.

[0005] In one exemplary embodiment, acquiring first status data of a target server in a server cluster within a first time range includes: sending a first instruction, wherein the first instruction is used to request the acquisition of status data of the server cluster; receiving the status data of the server cluster, wherein the status data of the server cluster is data fed back by a detection device after responding to the first instruction; obtaining status data including the target identification information from the status data of the server cluster based on the target identification information of the target server, and determining the status data including the target identification information as the first status data, wherein the target identification information is used to indicate the target server in the server cluster, and different servers have different identification information.

[0006] In an exemplary embodiment, before predicting target state data of the target server in a second time range based on the first state data after determining that the target server is in a normal operating state within the first time range based on the first state data, the method further includes: determining the operating state of the target server within the first time range based on the first state data; and determining a first alarm mode of a first device based on the first state data when the operating state indicates that the target server is in the abnormal operating state within the first time range, wherein the first device is a device included in the target server that is in the abnormal operating state within the first time range.

[0007] In an exemplary embodiment, determining the operating state of the target server within the first time range based on the first state data includes: determining first detectable state data corresponding to each first detection indicator included in the first state data based on multiple detection indicators included in the anomaly detection library, wherein the multiple detection indicators include the first detection indicator; if one or more of the first detectable state data includes first abnormal state data that satisfies the first abnormal state condition of the first detection indicator in the anomaly detection library, determining the operating state of the target server within the first time range as the abnormal operating state, determining the first detection indicator corresponding to the first abnormal state data as an abnormal indicator, and the first device corresponding to the first abnormal state data as a device included in the target server that is in the abnormal operating state within the first time range; if one or more of the first detectable state data does not include data that satisfies the first abnormal state, determining the operating state of the target server within the first time range as the normal operating state.

[0008] In an exemplary embodiment, when it is determined based on the first state data that the target server is in a normal operating state within the first time range, predicting the target state data of the target server within a second time range based on the first state data includes: determining the state data to be predicted for each device to be detected from the first state data, wherein the device to be detected is one or more devices included in the target server; determining the prediction method for each of the predicted state data based on the attribute information of each of the predicted state data, wherein the attribute information is used to indicate the attributes of the predicted state data that change over time; performing a prediction operation on each of the predicted state data based on the prediction method of each of the predicted state data to obtain the target prediction data corresponding to each device to be detected; and determining one or more of the target prediction data as the target state data.

[0009] In an exemplary embodiment, before determining the target warning method for the target device when it is determined that the target server is in an abnormal operating state within the second time range based on the target state data, the method further includes: determining the operating state of the target server within the second time range based on the target state data.

[0010] In an exemplary embodiment, determining the operating state of the target server within the second time range based on the target state data includes: determining target detection state data corresponding to each target detection indicator included in the target state data based on multiple detection indicators included in the anomaly detection library, wherein the multiple detection indicators include the target detection indicator; if one or more of the target detection state data includes target abnormal state data that satisfies the target abnormal state conditions of the target detection indicator in the anomaly detection library, then determining the operating state of the target server within the second time range as the abnormal operating state, determining the target detection indicator corresponding to the target abnormal state data as an abnormal indicator, and determining the device corresponding to the target abnormal state data as the target device.

[0011] In one exemplary embodiment, determining the target warning mode for the target device includes: determining a target flashing mode and a target sound mode for the target display device based on the target abnormal state data, wherein the target flashing mode and the target sound mode are used to indicate the warning level of the target device; determining a target display mode for the target display device based on the target detection index, wherein the target display mode is used to indicate that the target device is a device in the abnormal operating state; and determining the target flashing mode, the target sound mode, and the target display mode as the target warning mode.

[0012] According to another aspect of the embodiments of this application, an apparatus for monitoring a server cluster is also provided, comprising: a first acquisition module, configured to acquire first state data of a target server in the server cluster within a first time range, wherein the target server is any one server in the server cluster; a first prediction module, configured to predict target state data of the target server within a second time range based on the first state data, wherein the second time range is a time range after the first time range, when it is determined based on the first state data that the target server is in a normal operating state within the first time range; a first determination module, configured to determine a target warning method for a target device, wherein the target device is a device included in the target server that is in the abnormal operating state within the second time range, when it is determined based on the target state data that the target server is in an abnormal operating state within the second time range; and a first sending module, configured to send the target warning method to a target display device of the target server to instruct the target display device to perform a warning operation based on the target warning method.

[0013] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed by a processor.

[0014] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the steps in any of the method embodiments described above.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to perform the steps of any of the above method embodiments through the computer program.

[0016] This application, by periodically collecting initial state data of the target server, ensures real-time monitoring of cluster health, compared to related technologies that only detect and notify operations personnel when an anomaly occurs. Furthermore, by analyzing whether the target server is operating normally within the first timeframe, its state within the subsequent second timeframe is predicted, allowing for early warnings before potential anomalies occur, providing operations personnel with preparation and response time. Finally, once an anomaly is predicted in the second timeframe, a targeted warning method is established to identify the specific potentially abnormal device (i.e., the target device), enabling operations personnel to more quickly and accurately identify the specific abnormal device within the abnormal server. Therefore, this solves the problem of inefficiently locating abnormal servers in related technologies, thus achieving efficient abnormal server location. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating an application scenario of a method for monitoring a server cluster according to an embodiment of this application;

[0018] Figure 2 This is a flowchart illustrating an optional method for monitoring a server cluster according to an embodiment of this application;

[0019] Figure 3 This is an architecture diagram of an optional monitoring server cluster method according to an embodiment of this application;

[0020] Figure 4 This is a timing diagram of an optional method for monitoring a server cluster according to an embodiment of this application;

[0021] Figure 5 This is a flowchart of an optional method for monitoring a server cluster according to an embodiment of this application;

[0022] Figure 6 This is a structural block diagram of an optional monitoring server cluster apparatus according to an embodiment of this application. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus 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 such processes, methods, products, or apparatus.

[0025] According to one aspect of the embodiments of this application, a method for monitoring a server cluster is provided. Optionally, in this embodiment, the above-described method for monitoring a server cluster may be applied, but is not limited to, to applications such as... Figure 1 The hardware environment shown includes terminal device 102 and server 104. Server 104 can be connected to terminal device 102 via a network and can be used to provide services (e.g., application services, etc.) to terminal device 102 or clients installed on terminal device 102. A database can be set up on server 104 or independently of server 104 to provide data storage services for server 104.

[0026] The aforementioned network may include, but is not limited to, at least one of the following: wired network and wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network (WAN), metropolitan area network (MAN), and local area network (LAN). The aforementioned wireless network may include, but is not limited to, at least one of the following: Wireless Fidelity (WIFI) and Bluetooth. Terminal device 102 may be, but is not limited to, a personal computer (PC), mobile phone, tablet computer, etc. Server 104 may be, but is not limited to, a cloud server, server cluster, or other server types.

[0027] The monitoring server cluster method of this application embodiment can be executed by server 104, by terminal device 102, or by both server 104 and terminal device 102. Alternatively, the monitoring server cluster method of this application embodiment can be executed by a client installed on the terminal device 102.

[0028] Taking the method of monitoring server clusters in this embodiment executed by terminal device 102 as an example, Figure 2 This is a flowchart illustrating an optional method for monitoring a server cluster according to an embodiment of this application, as shown below. Figure 2 As shown, the process of this method may include the following steps:

[0029] Step S202: Obtain the first state data of the target server in the server cluster within a first time range, wherein the target server is any one of the servers in the server cluster.

[0030] Optionally, a server cluster is a collection of multiple independent servers working collaboratively through network connections. In a server cluster, the target server refers to the server whose status data the system is monitoring or analyzing; it can be a server randomly selected within the cluster or selected based on specific conditions.

[0031] Optionally, the first time range is a duration set when collecting target server status data, used to analyze the current state and predict future states. For example, if the first time range is the past 5 minutes, the system can continuously collect status data from the target server every second within the past 5 minutes.

[0032] Optionally, the first status data refers to data reflecting the operational status of the target server obtained within the first time frame, including but not limited to the operational status of the hardware devices and software devices in the target server. Examples include Central Processing Unit (CPU) status data, memory status data, hard disk status data, and network status data, specifically such as CPU utilization, CPU temperature, CPU fan speed, hard disk input / output (hard disk I / O), and power module status.

[0033] Step S204: If it is determined that the target server is in normal operation within the first time range based on the first state data, the target state data of the target server within a second time range is predicted based on the first state data, wherein the second time range is a time range after the first time range.

[0034] Optionally, normal operating status is used to indicate that the server's status data meets normal operating conditions within a specific time range (such as a first time range).

[0035] Optionally, the second time range is a period of time following the first time range, typically used to predict future server status data. The setting of the second time range depends on the accuracy of the predictive model or the needs of the operations team, and can be a future time window ranging from minutes to hours. For example, if the first time range is set to 5 minutes before the current time, then the second time range could be the next 5 minutes, 30 minutes, or 1 hour from now, depending on the accuracy of the predictive model or the operations team's need for early warnings.

[0036] Optionally, the target status data includes, but is not limited to, the predicted operating status of the hardware devices and the predicted operating status of the software devices in the target server. For example, predicted CPU status data, predicted memory status data, predicted hard disk status data, and predicted network status data, specifically, predicted CPU utilization, predicted CPU temperature, predicted CPU fan speed, and predicted hard disk I / O.

[0037] Step S206: If it is determined based on the target status data that the target server is in an abnormal operating state within the second time range, a target warning method for the target device is determined, wherein the target device is a device included in the target server that is in the abnormal operating state within the second time range.

[0038] Optionally, abnormal operating status is used to indicate that the server's status data does not meet normal operating conditions or meets abnormal operating conditions within a specific time range (such as a second time range).

[0039] Optionally, the target warning method refers to the prompting or notification mechanism taken when an abnormal operating state of the target device is detected. Warning methods include, but are not limited to, light color, light flashing frequency, sound warning, vibration prompt, or sending electronic alarms through communication networks.

[0040] Optionally, determining the target warning method for the target device includes: determining the target warning method based on the target abnormal state data in the target state data, wherein the target abnormal state data is data in the target state data that includes data that meets the target abnormal state conditions.

[0041] Step S208: The above-mentioned target warning method is sent to the target display device of the target server to instruct the target display device to perform a warning operation based on the above-mentioned target warning method.

[0042] Optionally, the target display device is a hardware device located near or directly integrated on the target server, used to receive warning signals carrying the target warning method and perform corresponding warning operations. The target display device includes, but is not limited to, light-emitting diode (LED) light groups, liquid crystal display (LCD) screens, or buzzers. For example, an integrated LED alarm light group is installed on the side of the server S123 rack as a target display device. When a hard drive failure warning is detected in S123, a command for the target warning method (such as a rapidly flashing yellow light) will be sent to the display device via a wireless communication module or a wired interface, such as a Controller Area Network (CAN) bus. The indicator light group will flash according to a predetermined pattern to clearly indicate the problem with the S123 hard drive.

[0043] This embodiment collects the target server's initial state data periodically, ensuring real-time monitoring of cluster health compared to related technologies that only detect and notify operations personnel when an anomaly occurs. Furthermore, by analyzing whether the target server is operating normally within the first timeframe, its state within the subsequent second timeframe is predicted, allowing for early warnings before potential anomalies occur, providing operations personnel with preparation and response time. Finally, once an anomaly is predicted for the target server within the second timeframe, a targeted warning method is established to identify the specific potentially abnormal device (i.e., the target device), enabling operations personnel to more quickly and accurately identify the specific abnormal device within the abnormal server. Therefore, this solves the problem of inefficiently locating abnormal servers in related technologies, achieving efficient abnormal server location.

[0044] In one exemplary embodiment, acquiring first status data of a target server in a server cluster within a first time range includes: sending a first instruction, wherein the first instruction is used to request the acquisition of status data of the server cluster; receiving the status data of the server cluster, wherein the status data of the server cluster is data fed back by a detection device after responding to the first instruction; obtaining status data including the target identification information from the status data of the server cluster based on the target identification information of the target server, and determining the status data including the target identification information as the first status data, wherein the target identification information is used to indicate the target server in the server cluster, and different servers have different identification information.

[0045] Optionally, the first instruction is a request command sent by the system to the detection device to trigger or request the acquisition of server cluster status data. The first instruction can be automatically triggered at a set time or manually triggered by manual operation. For example, the system sends an HTTP GET request to the Zabbix server via the communication module as the first instruction (e.g., GET / api / v1 / status?server_id=S123), requesting the acquisition of hardware status data for the server with the specified ID S123 within a first time frame, including CPU utilization, temperature, and other information.

[0046] Optionally, the testing equipment is a hardware or software system used to periodically monitor and record the server hardware status and performance indicators. The testing equipment can be a standalone hardware monitor, a sensor on the server, or a rack-level environmental monitoring system.

[0047] Optionally, the target identification information is information that uniquely identifies and locates a specific server in the server cluster. This target identification information can be a server's unique ID, IP address, hostname, or Media Access Control Address (MAC) address, etc. The target identification information ensures that the system can accurately filter out the target server's data from the server cluster's status data.

[0048] This embodiment accurately filters out the first-state data related to the target server from complex cluster status data based on the target server's identification information (such as IP address, MAC address, server ID, etc.), greatly improving the targeting and efficiency of fault detection. In data centers, the number of servers is enormous, and traditional manual troubleshooting is not only time-consuming but also prone to errors. This improvement ensures that maintenance personnel can quickly locate servers that may have problems, reducing troubleshooting costs and improving anomaly response speed.

[0049] In an exemplary embodiment, before predicting target state data of the target server in a second time range based on the first state data after determining that the target server is in a normal operating state within the first time range based on the first state data, the method further includes: determining the operating state of the target server within the first time range based on the first state data; and determining a first alarm mode of a first device based on the first state data when the operating state indicates that the target server is in the abnormal operating state within the first time range, wherein the first device is a device included in the target server that is in the abnormal operating state within the first time range.

[0050] Optionally, the first device is a device detected in the target server that is in an abnormal operating state, such as the CPU, memory, hard drive, etc.

[0051] Optionally, the first alarm method refers to the prompting or notification mechanism taken when the first device is detected to be in an abnormal operating state. Alarm methods include, but are not limited to, light color, light flashing frequency, sound warning, vibration alert, or sending electronic alarms via a communication network.

[0052] This embodiment, upon detecting that the target server is in an abnormal operating state within a first time frame, can immediately determine the first alarm mode for the first device (such as critical components like the CPU, memory, and hard drive) based on the first state data. This immediate response mechanism can reduce the possibility of a fault escalating into a major incident.

[0053] In an exemplary embodiment, determining the operating state of the target server within the first time range based on the first state data includes: determining first detectable state data corresponding to each first detection indicator included in the first state data based on multiple detection indicators included in the anomaly detection library, wherein the multiple detection indicators include the first detection indicator; if one or more of the first detectable state data includes first abnormal state data that satisfies the first abnormal state condition of the first detection indicator in the anomaly detection library, determining the operating state of the target server within the first time range as the abnormal operating state, determining the first detection indicator corresponding to the first abnormal state data as an abnormal indicator, and the first device corresponding to the first abnormal state data as a device included in the target server that is in the abnormal operating state within the first time range; if one or more of the first detectable state data does not include data that satisfies the first abnormal state, determining the operating state of the target server within the first time range as the normal operating state.

[0054] Optionally, an anomaly detection library is a set of predefined rules or algorithms used to evaluate whether server status data deviates from the normal operating range or pattern, including anomaly judgment conditions for multiple detection indicators. For example, a typical data center anomaly detection library might include the following rules: if CPU utilization exceeds 90% and lasts for more than one minute, the CPU is determined to be in an abnormal operating state; when memory utilization exceeds 85% and system response time increases by more than 20% from the baseline value, the memory module is determined to be abnormal; if hard drive read / write speed decreases by more than 30% and the Self-Monitoring, Analysis and Reporting Technology (SMART) error count increases, the hard drive is considered to show signs of pre-failure.

[0055] Optionally, the monitoring metrics are specific parameters used to measure and evaluate the server's operating status, including but not limited to CPU utilization, memory usage percentage, hard disk read / write speed, and network throughput.

[0056] Optionally, the first state data to be detected is the actual state data of the target server for each first detection indicator within a first time range, that is, a series of state data acquired at the current moment. For example, within the current 5-minute time window (first time range), the first detection indicators of server S123 include: CPU utilization, memory usage percentage, and hard disk read / write speed. The first state data to be detected for each first detection indicator are: CPU utilization of 75%, memory usage of 80%, and hard disk read / write speed of 120MB / s.

[0057] Optionally, the abnormal state condition is a threshold or condition set for each detection indicator in the anomaly detection library. It is used to determine whether the first data to be detected exceeds the normal range, thereby determining whether there is an abnormal operating state. For example, in the anomaly detection library, the abnormal state condition for memory usage is defined as exceeding 85%. If the detected memory usage reaches 87%, the abnormal state condition will be triggered, indicating that the memory module may be operating abnormally.

[0058] Optionally, the first abnormal state data is the actual state data of the first detection indicator that meets the abnormal state conditions in the first state data. When one or more first detection state data meet the abnormal state conditions, the target server is determined to be in an abnormal operating state. The abnormal indicator is the first detection indicator corresponding to the first abnormal state data. For example, in server S12, its abnormal indicator is CPU utilization, because this indicator meets the abnormal state conditions. Accordingly, the CPU module in S12 is identified as the first device, that is, the hardware part that is malfunctioning.

[0059] Optionally, normal operation refers to the operating state of the target server when none of the first-stage data to be detected meets the abnormal state conditions. For example, if server S123 has a CPU utilization of 70%, a memory utilization of 75%, and a hard disk read / write speed of 100MB / s during the current monitoring period, and these data are all below the defined abnormal state conditions, the system will determine that S123 is in normal operation, indicating that there are no obvious problems with the server's hardware and performance at this time.

[0060] In an exemplary embodiment, when it is determined based on the first state data that the target server is in a normal operating state within the first time range, predicting the target state data of the target server within a second time range based on the first state data includes: determining the state data to be predicted for each device to be detected from the first state data, wherein the device to be detected is one or more devices included in the target server; determining the prediction method for each of the predicted state data based on the attribute information of each of the predicted state data, wherein the attribute information is used to indicate the attributes of the predicted state data that change over time; performing a prediction operation on each of the predicted state data based on the prediction method of each of the predicted state data to obtain the target prediction data corresponding to each device to be detected; and determining one or more of the target prediction data as the target state data.

[0061] Optionally, the first state data refers to the real-time operating status information of each device on the target server collected within the first time frame, such as CPU utilization, memory usage, hard disk read / write speed, and network latency. The devices to be monitored are the devices on the target server that need to be monitored and analyzed, such as CPU, memory modules, disk drives, and network adapters.

[0062] Optionally, the state data to be predicted is extracted from the first state data and is used to predict the possible state data of the target server within a future time range (a second time range). Attribute information includes, but is not limited to, trend, periodicity, and randomness.

[0063] Optionally, the prediction method involves selecting or designing a prediction model or algorithm based on the attribute information of the state data to be predicted. The target prediction data is the predicted state data of each device to be detected within the next second time interval, predicted through the prediction method. For example, for linear trend-type state data, such as memory load rate, a Holt-Winters exponential smoothing model can be used to capture linear growth trends; for non-linear fluctuation-type state data, such as hard disk input / output operations per second (IOPS) and graphics processing unit (GPU) temperature, a Long Short-Term Memory network (LSTM) can be used to model temporal dependencies; for sudden anomaly-type state data, such as power supply voltage, an isolated forest can be introduced to identify outliers, and a Markov chain can be combined to predict the probability of anomaly propagation. For example, based on the CPU utilization trend of the target server over the past 5 minutes (average utilization slowly increasing from 60% to 65%), it is predicted that the CPU utilization will rise to 70% in the next second time interval (i.e., the next 5 minutes). This indicates that the server load may gradually increase. If it exceeds the preset threshold (such as 80%), it may trigger a performance warning. Operations personnel need to prepare response strategies in advance, such as increasing resource allocation, optimizing application configuration, or preparing a failover plan.

[0064] Optionally, the target status data is a set of prediction results formed by integrating the target prediction data of each device to be tested within a second time range, used to guide operation and maintenance personnel in resource allocation and potential risk prevention.

[0065] This embodiment can predict impending hardware failures or performance degradation by analyzing first-state data. Simultaneously, it selects the prediction method for each state data point based on the attribute information of the predicted state data (such as linear trends, periodic fluctuations, sudden anomalies, etc.), thus matching the most suitable prediction method and greatly improving prediction accuracy.

[0066] In an exemplary embodiment, before determining the target warning method for the target device when it is determined that the target server is in an abnormal operating state within the second time range based on the target state data, the method further includes: determining the operating state of the target server within the second time range based on the target state data.

[0067] In an exemplary embodiment, determining the operating state of the target server within the second time range based on the target state data includes: determining target detection state data corresponding to each target detection indicator included in the target state data based on multiple detection indicators included in the anomaly detection library, wherein the multiple detection indicators include the target detection indicator; if one or more of the target detection state data includes target abnormal state data that satisfies the target abnormal state conditions of the target detection indicator in the anomaly detection library, then determining the operating state of the target server within the second time range as the abnormal operating state, determining the target detection indicator corresponding to the target abnormal state data as an abnormal indicator, and determining the device corresponding to the target abnormal state data as the target device.

[0068] Optionally, the detection metrics are specific parameters used to measure and evaluate the server's operating status, including but not limited to CPU utilization, memory usage percentage, hard disk read / write speed, and network throughput. Target detection status data refers to key parameters defined in the anomaly detection library used to assess hardware status. Target detection status data is predicted status data directly related to the detection metrics, selected from the target status data. For example, if the target detection metric is CPU utilization, the corresponding target detection status data could be a predicted CPU utilization of 95% for target server S123 in the next hour. If the target detection metric is hard disk I / O operations, the corresponding target detection status data could be a predicted average hard disk I / O wait time of 40ms for target server S123 in the next hour.

[0069] Optionally, abnormal state conditions are thresholds or conditions set for each detection metric in the anomaly detection library. These conditions are used to determine whether the target data to be detected exceeds the normal range, thereby identifying whether an abnormal operating state exists. For example, in the anomaly detection library, the abnormal state condition for memory usage is defined as exceeding 85%. If the detected memory usage reaches 87%, the abnormal state condition will be triggered, indicating that the memory module may be operating abnormally.

[0070] Optionally, the target abnormal state data is the predicted state data of the target detection indicators that meet the abnormal state conditions within the target state data. When one or more target detection state data meet the abnormal state conditions, the target server is determined to be in an abnormal operating state. The abnormal indicator is the target detection indicator corresponding to the target abnormal state data. For example, in server S12, its abnormal indicator is CPU utilization, because this indicator meets the abnormal state conditions. Accordingly, the CPU module in S12 is identified as the target device, i.e., the hardware part that is abnormal. For example, in the next hour, the predicted CPU utilization of the target server S123 continues to exceed 90% (target abnormal state data), meeting the CPU utilization abnormal detection condition. The predicted average hard disk I / O wait time of the target server S123 reaches 45ms, exceeding the abnormal threshold of 30ms, meeting the hard disk I / O abnormal detection condition.

[0071] In one exemplary embodiment, determining the target warning mode for the target device includes: determining a target flashing mode and a target sound mode for the target display device based on the target abnormal state data, wherein the target flashing mode and the target sound mode are used to indicate the warning level of the target device; determining a target display mode for the target display device based on the target detection index, wherein the target display mode is used to indicate that the target device is a device in the abnormal operating state; and determining the target flashing mode, the target sound mode, and the target display mode as the target warning mode.

[0072] Optionally, the target display device is a hardware device located near or directly integrated with the target server, used to receive warning signals carrying target warning methods and perform corresponding warning operations. The target display device includes, but is not limited to, LED light groups, LCD screens, or buzzers.

[0073] Optionally, the target flashing mode refers to the color of the light emitted by the target display device and the flashing frequency. The flashing frequency is positively correlated with the warning level; that is, the higher the flashing frequency, the higher the warning level. For example, slow flashing (0.5-1Hz): indicates a warning state requiring planned action. Fast flashing (2-4Hz): indicates an emergency state requiring immediate action.

[0074] Optionally, the target sound mode is the rhythm and pitch of the sound emitted by the target display device. A faster rhythm corresponds to a higher warning level, and a higher pitch corresponds to a higher warning level. For example, Level 1 alarm (emergency): continuous, rapid beeping. Level 2 alarm (warning): intermittent "beep-beep" sounds (e.g., 0.5 seconds on, 2 seconds off). Level 3 alarm (warning / information): silent.

[0075] Optionally, the target flashing pattern and target sound pattern can be combined to indicate the warning level of the target device. For example, Level 1 alarm (emergency): fast red flashing and continuous rapid beeping. Level 2 alarm (warning): slow yellow flashing and intermittent "beep-beep" sound (e.g., 0.5 seconds on, 2 seconds off). Level 3 alarm (warning / information): blue breathing / purple solid light and silent.

[0076] Optionally, the target display mode is a visual information used in the target warning method to directly display the fault type and status. It is typically integrated into a display screen or LED matrix within a physical alarm light group. It can provide detailed information about the abnormal status of the target device in the form of text, icons, or color codes. For example, the target display mode is the color of the light emitted by the target display device, with coded flashing to convey the specific abnormal operating status of the device. For instance, a [red-red-long pause] detection cycle indicates a CPU-related fault. A [red-yellow-long pause] detection cycle indicates a memory-related fault. A [yellow-yellow-yellow-long pause] detection cycle indicates a hard disk pre-failure warning.

[0077] This embodiment defines the target flashing mode, target sound mode, and target display mode as the target warning method, providing maintenance personnel with an immediate and clear alarm signal. This multi-sensory warning method ensures that even in complex data center environments, where both visual and auditory limitations exist, maintenance personnel can quickly receive alarm information and respond promptly.

[0078] The method for monitoring server clusters in this application embodiment will be explained below with reference to optional examples.

[0079] like Figure 3 As shown, Figure 3 This demonstrates the overall structure of a physical alarm light group system for server cluster status. The server cluster connects to a communication module via a status monitoring module. The communication module sends data to a control module, which then drives the physical alarm light group. The status monitoring module collects hardware status data from components such as the CPU, memory, hard drive, and network. The communication module uses wired or wireless protocols, such as Ethernet or Wi-Fi, to transmit status data. The control module is based on a microcontroller, such as an Advanced Reduced Instruction Set Machine (ARM). The physical alarm light group includes multiple LEDs indicating the warning or alarm mode. Specifically, for example... Figure 4 The diagram illustrates the timing interactions between the status monitoring module, communication module, control module, and physical alarm light group. From top to bottom, the timeline shows: the status monitoring module periodically (e.g., every 5 seconds); the communication module receives and forwards the data; the controller in the control module processes the data and outputs control signals; and the physical alarm light group updates its display based on the control signals.

[0080] Figure 5 This is a flowchart of an optional method for monitoring a server cluster according to an embodiment of this application, such as... Figure 5 As shown, the process of this method may include the following steps:

[0081] Step S501, Begin.

[0082] In step S502, the status monitoring module detects the hardware status of each server in the server cluster through sensors or application programming interfaces to obtain the status data of the server cluster.

[0083] In step S503, the status monitoring module sends the status data of the server cluster to the control module through the communication module.

[0084] In step S504, the controller in the control module determines whether the status data of the server cluster is valid. If it is, step S505 is executed; otherwise, step S508 is executed.

[0085] In step S505, the controller in the control module parses the status data of the server cluster to obtain the status data of each detection indicator.

[0086] In step S506, the controller in the control module first determines the current state of the server based on the current state data. If it is determined that the current operating state of the server is an abnormal operating state, then step S507 is executed. Otherwise, based on the current state data, future state data is predicted, and the future state of the server is determined based on the future state data. If it is determined that the future operating state of the server is an abnormal operating state, then step S507 is executed. Specifically, the prediction method can be selected or designed according to the attribute information of the state data to be predicted. For example, for linear trend-type state data to be predicted, such as memory load rate, the Holt-Winters exponential smoothing model can be used to capture linear growth trends; for nonlinear fluctuation-type state data to be predicted, such as hard disk IOPS and GPU temperature, LSTM can be used to model time-series dependencies; for sudden anomaly-type state data to be predicted, such as power supply voltage, isolated forests can be introduced to identify outliers, and Markov chains can be combined to predict the probability of anomaly propagation.

[0087] The anomaly detection library in the control module pre-stores a series of association rules characterizing hardware dependencies. When multiple status data are abnormal simultaneously, the root cause can be diagnosed more accurately. For example, for heat dissipation fault diagnosis: if the CPU temperature > threshold T1 and the corresponding CPU fan speed < threshold RPM1, it is determined as "heat dissipation fan failure". For hard disk pre-failure diagnosis: if the hard disk SMART "reallocation sector count" increases rapidly and the IO response latency > threshold L1, it is determined as "hard disk is about to fail". For power redundancy degradation diagnosis: if the power module A is in "fault" status but the total power consumption is <90% of the rated power of a single power module, it is determined as "power redundancy loss", which is a warning rather than an emergency.

[0088] Meanwhile, the control module correlates hardware alarm thresholds with the server's real-time workload. For example, when CPU utilization is below a preset low-load threshold, it automatically raises alarm thresholds such as temperature to reduce interference. When CPU utilization is above a preset high-load threshold, it uses stricter alarm thresholds to ensure system stability under high pressure.

[0089] Simultaneously, the control module generates alarm signals based on status data representing abnormal operating states and sends these signals to the physical alarm light group. The alarm signals carry specific warning or alarm methods. For example, color coding: in addition to red, yellow, and green, blue represents "information state / redundancy degradation," and purple represents "security / configuration change event." Mode coding: Solid light: steady-state normal or confirmed state. Breathing light: indicates a warning state or minor abnormality, requiring attention. Slow flashing (0.5-1Hz): indicates a warning state, requiring planned handling. Fast flashing (2-4Hz): indicates an emergency state, requiring immediate handling. Coded flashing: used to convey specific fault types. For example: [Red-Red-Long pause] cycle: indicates CPU-related faults. [Red-Yellow-Long pause] cycle: indicates memory-related faults. [Yellow-Yellow-Yellow-Long pause] cycle: indicates a hard drive pre-failure warning.

[0090] In step S507, the physical alarm light group displays the warning or alarm mode carried in the control signal. For example, when the display command of the abnormal server is automatically triggered, the alarm light of the abnormal server enters "addressing mode" and executes a warning mode of fast red flashing and continuous rapid beeping, indicating the highest level of urgency alarm, so as to quickly locate the abnormal server in the server cluster.

[0091] Step S508: Discard invalid data.

[0092] Step S509, End.

[0093] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0094] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / random access memory (RAM), magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0095] According to another aspect of the embodiments of this application, an apparatus for monitoring a server cluster is also provided. This apparatus can be used to implement the method for monitoring a server cluster provided in the above embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0096] Figure 6 This is a structural block diagram of an optional monitoring server cluster apparatus according to an embodiment of this application, such as... Figure 6 As shown, the apparatus for the monitoring server cluster includes:

[0097] The first acquisition module 602 is used to acquire the first state data of the target server in the server cluster within a first time range, wherein the target server is any one of the servers in the server cluster.

[0098] The first prediction module 604 is used to predict the target state data of the target server in a second time range based on the first state data, when it is determined that the target server is in a normal operating state within the first time range based on the first state data, wherein the second time range is a time range after the first time range.

[0099] The first determining module 606 is used to determine the target warning method of the target device when it is determined from the target status data that the target server is in an abnormal operating state within the second time range, wherein the target device is a device included in the target server that is in the abnormal operating state within the second time range.

[0100] The first sending module 608 is used to send the aforementioned target warning method to the target display device of the aforementioned target server, so as to instruct the aforementioned target display device to perform a warning operation based on the aforementioned target warning method.

[0101] It should be noted that the first acquisition module 602 in this embodiment can be used to execute the above step S202, the first prediction module 604 in this embodiment can be used to execute the above step S204, the first determination module 606 in this embodiment can be used to execute the above step S206, and the first sending module 608 in this embodiment can be used to execute the above step S208.

[0102] In an exemplary embodiment, the first acquisition module 602 includes: a first sending submodule, configured to send a first instruction, wherein the first instruction is used to request the acquisition of the status data of the server cluster; a first receiving submodule, configured to receive the status data of the server cluster, wherein the status data of the server cluster is data fed back by the detection device after responding to the first instruction; and a first determining submodule, configured to obtain status data including the target identification information from the status data of the server cluster based on the target identification information of the target server, and determine the status data including the target identification information as the first status data, wherein the target identification information is used to indicate the target server in the server cluster, and different servers have different identification information.

[0103] In one exemplary embodiment, the apparatus further includes: a second determining module, configured to determine the operating state of the target server within the first time range based on the first state data before predicting target state data of the target server within a second time range, when the target server is determined to be in a normal operating state within the first time range based on the first state data; and a third determining module, configured to determine a first alarm mode of the first device based on the first state data when the operating state indicates that the target server is in the abnormal operating state within the first time range, wherein the first device is a device included in the target server that is in the abnormal operating state within the first time range.

[0104] In an exemplary embodiment, the second determining module includes: a second determining submodule, configured to determine, based on a plurality of detection indicators included in the anomaly detection library, a first state data to be detected corresponding to each first detection indicator included in the first state data, wherein the plurality of detection indicators includes the first detection indicator; a third determining submodule, configured to, when one or more of the first state data to be detected includes first abnormal state data that satisfies the first abnormal state condition of the first detection indicator in the anomaly detection library, determine that the operating state of the target server within the first time range is the abnormal operating state, determine that the first detection indicator corresponding to the first abnormal state data is an abnormal indicator, and the first device corresponding to the first abnormal state data is a device included in the target server that is in the abnormal operating state within the first time range; and a fourth determining submodule, configured to, when one or more of the first state data to be detected does not include data that satisfies the first abnormal state, determine that the operating state of the target server within the first time range is the normal operating state.

[0105] In an exemplary embodiment, the first prediction module 604 includes: a fifth determining submodule, configured to determine predictable state data corresponding to each device to be detected from the first state data, wherein the device to be detected is one or more devices included in the target server; a sixth determining submodule, configured to determine a prediction method for each predictable state data based on attribute information of each predictable state data, wherein the attribute information is used to indicate the attributes of the predictable state data that change over time; a first prediction submodule, configured to perform a prediction operation on each predictable state data based on the prediction method of each predictable state data to obtain target prediction data corresponding to each device to be detected; and a seventh determining submodule, configured to determine one or more target prediction data as target state data.

[0106] In an exemplary embodiment, the apparatus further includes a third determining module, configured to determine the operating state of the target server within the second time range based on the target state data before determining the target warning method for the target device when it is determined based on the target state data that the target server is in an abnormal operating state within the second time range.

[0107] In an exemplary embodiment, the third determining module includes: an eighth determining submodule, configured to determine, based on a plurality of detection indicators included in the anomaly detection library, target state data corresponding to each target detection indicator included in the target state data, wherein the plurality of detection indicators includes the target detection indicator; and a ninth determining submodule, configured to determine, when one or more of the target state data to be detected include target abnormal state data that satisfies the target abnormal state conditions of the target detection indicator in the anomaly detection library, the operating state of the target server within the second time range is the abnormal operating state, the target detection indicator corresponding to the target abnormal state data is an abnormal indicator, and the device corresponding to the target abnormal state data is the target device.

[0108] In an exemplary embodiment, the first determining module includes: a tenth determining submodule, configured to determine a target flashing mode and a target sound mode of the target display device based on the target abnormal state data, wherein the target flashing mode and the target sound mode are used to indicate the warning level of the target device; an eleventh determining submodule, configured to determine a target display mode of the target display device based on the target detection index, wherein the target display mode is used to indicate that the target device is a device in the abnormal operating state; and a twelfth determining submodule, configured to determine the target flashing mode, the target sound mode, and the target display mode as the target warning mode.

[0109] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0110] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes the steps in any of the above method embodiments when it is run.

[0111] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0112] According to another aspect of the embodiments of this 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 processor is configured to perform the steps of any of the method embodiments described above via the computer program. In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0113] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0114] According to another aspect of the embodiments of this application, a computer program product is also provided, which includes a computer program / instructions containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs various functions provided in the embodiments of this application. The sequence numbers of the embodiments of this application above are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0115] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0116] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0117] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0118] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for monitoring a server cluster, characterized in that, include: Obtain the first state data of the target server in the server cluster within a first time range, wherein the target server is any one of the servers in the server cluster; If it is determined based on the first state data that the target server is in normal operating state within the first time range, the target state data of the target server within a second time range is predicted based on the first state data, wherein the second time range is a time range after the first time range. If it is determined based on the target status data that the target server is in an abnormal operating state within the second time range, a target early warning method for the target device is determined, wherein the target device is a device included in the target server that is in the abnormal operating state within the second time range; The target warning method is sent to the target display device of the target server to instruct the target display device to perform a warning operation based on the target warning method.

2. The method according to claim 1, characterized in that, Obtain the first state data of the target server in the server cluster within a first time range, including: Send a first instruction, wherein the first instruction is used to request the acquisition of the status data of the server cluster; Receive the status data of the server cluster, wherein the status data of the server cluster is the data fed back by the detection device after responding to the first instruction; Based on the target identification information of the target server, status data including the target identification information is obtained from the status data of the server cluster, and the status data including the target identification information is determined as the first status data. The target identification information is used to indicate the target server in the server cluster, and different servers have different identification information.

3. The method according to claim 1, characterized in that, Before predicting the target server's target state data in a second time range based on the first state data, after determining that the target server is in a normal operating state within the first time range based on the first state data, the method further includes: Based on the first status data, the operating status of the target server within the first time range is determined; When the operating status indicates that the target server is in the abnormal operating state within the first time range, a first alarm method of the first device is determined based on the first status data, wherein the first device is a device included in the target server that is in the abnormal operating state within the first time range.

4. The method according to claim 3, characterized in that, Based on the first state data, determining the operating state of the target server within the first time range includes: Based on multiple detection indicators included in the anomaly detection library, the first state data to be detected is determined for each first detection indicator included in the first state data, wherein the first detection indicator is included among the multiple detection indicators. If one or more of the first state data to be detected include first abnormal state data that meets the first abnormal state condition of the first detection index in the abnormal detection library, the operating state of the target server within the first time range is determined to be the abnormal operating state, the first detection index corresponding to the first abnormal state data is determined to be the abnormal index, and the first device corresponding to the first abnormal state data is a device in the target server that is in the abnormal operating state within the first time range. If one or more of the first detected state data do not include the first abnormal state data, the operating state of the target server within the first time range is determined to be the normal operating state.

5. The method according to claim 1, characterized in that, If, based on the first state data, it is determined that the target server is in a normal operating state within the first time range, then, based on the first state data, the target state data of the target server within a second time range is predicted, including: Determine the predicted state data for each device to be detected from the first state data, wherein the device to be detected is one or more devices included in the target server; Based on the attribute information of each predicted state data, the prediction method for each predicted state data is determined, wherein the attribute information is used to indicate the attributes of the predicted state data that change over time. Based on the prediction method for each of the state data to be predicted, a prediction operation is performed on each of the state data to be predicted to obtain the target prediction data corresponding to each of the devices to be detected. One or more of the target prediction data are determined as the target state data.

6. The method according to claim 1, characterized in that, Before determining the target warning method for the target device based on the target status data, the method further includes: Based on the target status data, the operating status of the target server within the second time range is determined.

7. The method according to claim 6, characterized in that, Based on the target status data, determining the operating status of the target server within the second time range includes: Based on multiple detection indicators included in the anomaly detection library, the target state data to be detected corresponding to each target detection indicator included in the target state data is determined, wherein the multiple detection indicators include the target detection indicator; If one or more of the target state data to be detected include target abnormal state data that meets the target abnormal state conditions of the target detection index in the anomaly detection library, the operating state of the target server within the second time range is determined to be the abnormal operating state, the target detection index corresponding to the target abnormal state data is determined to be the abnormal index, and the device corresponding to the target abnormal state data is the target device.

8. The method according to claim 7, characterized in that, Determine the target early warning method for the target equipment, including: Based on the target abnormal state data, the target flashing mode and the target sound mode of the target display device are determined, wherein the target flashing mode and the target sound mode are used to indicate the warning level of the target device; Based on the target detection indicators, the target display mode of the target display device is determined, wherein the target display mode is used to indicate that the target device is a device in the abnormal operating state; The target flashing mode, the target sound mode, and the target display mode are determined as the target warning mode.

9. A device for monitoring a server cluster, characterized in that, include: The first acquisition module is used to acquire the first state data of a target server in the server cluster within a first time range, wherein the target server is any one of the servers in the server cluster; The first prediction module is configured to predict the target state data of the target server in a second time range based on the first state data, when it is determined that the target server is in a normal operating state within the first time range based on the first state data, wherein the second time range is a time range after the first time range. The first determining module is used to determine the target warning method for the target device when it is determined based on the target status data that the target server is in an abnormal operating state within the second time range, wherein the target device is a device included in the target server that is in the abnormal operating state within the second time range; The first sending module is used to send the target warning method to the target display device of the target server, so as to instruct the target display device to perform a warning operation based on the target warning method.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 8.

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