WO2020252902A1 - 云服务器自检方法、装置、设备及计算机可读存储介质 - Google Patents

云服务器自检方法、装置、设备及计算机可读存储介质 Download PDF

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WO2020252902A1
WO2020252902A1 PCT/CN2019/102564 CN2019102564W WO2020252902A1 WO 2020252902 A1 WO2020252902 A1 WO 2020252902A1 CN 2019102564 W CN2019102564 W CN 2019102564W WO 2020252902 A1 WO2020252902 A1 WO 2020252902A1
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cloud server
port
load
server instance
abnormal load
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English (en)
French (fr)
Inventor
邓鸿斌
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/16—Threshold monitoring
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/50—Testing arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers

Definitions

  • This application relates to the field of cloud technology, and in particular to methods, devices, equipment and readable storage media for self-checking of cloud servers.
  • Cloud computing technology As a new generation of IT models has also been widely used.
  • Cloud computing technology has evolved from grid computing, parallel computing and distributed computing. Users can use it to conveniently access a configurable computing resource (such as computing, network, storage, applications, and services) through the network on demand.
  • the shared pool can quickly open or release resources with minimal management workload or service provider intervention.
  • the inventor realizes that as the business volume of enterprise applications is increasing every year, the demand for cloud servers is also increasing. It is difficult for operation and maintenance personnel to check the health of the server at all times, and then replace it in real time, which requires a lot of labor. Cost, cannot guarantee the stable operation of the business system at all times.
  • the main purpose of this application is to provide a self-checking method, device, equipment, and readable storage medium for a cloud server, aiming to solve the technical problem of how to implement the cloud server's regular self-check and automated operation.
  • this application provides a self-check method for a cloud server, and the cloud server self-check method includes:
  • the self-check time If the self-check time is currently reached, detect whether there are ports with abnormal load in the current scaling group, where the scaling group is a collection of cloud server instances facing the same scenario;
  • the cloud server instance corresponding to the port with the abnormal load is not operating normally, the cloud server instance corresponding to the port with the abnormal load is removed, and the normal cloud server instance is copied to the scaling group.
  • detecting whether there is an abnormal load port in the current scaling group further includes:
  • the load balancing device uses the load balancing device to collect statistics on the load status of the ports in the scaling group, where the load includes distributed traffic and inflow and outflow rates;
  • the port load is abnormal
  • detecting whether the cloud server instance corresponding to the port with the abnormal load is operating normally includes:
  • a port with an abnormal load is detected in the scaling group, based on the port with the abnormal load, determine the cloud server instance corresponding to the port with the abnormal load;
  • detecting whether the cloud server instance corresponding to the port with the abnormal load is operating normally further includes:
  • a port with an abnormal load If a port with an abnormal load is detected in the scaling group, report and count the cloud server instance corresponding to the port with the abnormal load, and count the number of cloud server instance abnormalities corresponding to the port with the abnormal load;
  • the cloud server instance corresponding to the port with the abnormal load has multiple consecutive abnormalities, and the number of abnormalities reaches a preset threshold, it is determined that the cloud server instance corresponding to the port with the abnormal load is not operating normally;
  • the cloud server instance corresponding to the port with the abnormal load does not have multiple consecutive abnormalities, and the number of abnormalities reaches the preset number threshold, it is determined that the cloud server instance corresponding to the port with the abnormal load is operating normally.
  • removing the cloud server instance corresponding to the port with the abnormal load, and copying the cloud server instance with normal operation to join the scaling group includes:
  • the cloud server instances higher than part of them are randomly removed.
  • Detecting the load situation of the cloud server instance of the scaling group where the load includes memory utilization, cpu utilization, disk read and write rate, inflow and outflow rate, and the number of device connections;
  • the cloud server instances of the scaling group are reduced according to the preset scaling rules.
  • the present application also provides a cloud server self-checking device, the cloud server self-checking device includes:
  • the monitoring module is used to monitor whether the current self-check time is reached based on the preset self-check time interval;
  • the first detection module is configured to detect whether there are ports with abnormal load in the current scaling group if the self-checking time is currently reached, wherein the scaling group is a collection of cloud server instances facing the same scenario;
  • the second detection module is configured to, if the abnormal load port is detected in the scaling group, based on a preset detection mechanism, detect whether the cloud server instance corresponding to the abnormal load port is operating normally;
  • the first removal module is configured to remove the cloud server instance corresponding to the port with the abnormal load if the cloud server instance corresponding to the port with the abnormal load is not operating normally, and copy the cloud server instance that runs normally to the scaling group .
  • the present application also provides a cloud server self-checking device.
  • the cloud server self-checking device includes a memory, a processor, and a cloud that is stored on the memory and can run on the processor.
  • a server self-check program which implements the steps of the cloud server self-check method described in any one of the above when the cloud server self-check program is executed by the processor.
  • the present application also provides a non-volatile computer-readable storage medium, the computer-readable storage medium stores a cloud server self-check program, and the cloud server self-check program is executed by the processor. During execution, the steps of the cloud server self-checking method described in any one of the above are implemented.
  • FIG. 1 is a schematic structural diagram of an operating environment of a cloud server self-checking device involved in a solution of an embodiment of the application;
  • FIG. 2 is a schematic flowchart of an embodiment of a self-check method for a cloud server according to the application
  • FIG. 3 is a detailed flowchart of an embodiment of step S20 in FIG. 2. ;
  • FIG. 4 is a detailed flowchart of an embodiment of step S30 in FIG. 2;
  • step S30 in FIG. 2 is a schematic diagram of another detailed flow of step S30 in FIG. 2;
  • FIG. 6 is a detailed flowchart of step S40 in FIG. 2;
  • FIG. 7 is a schematic flowchart of a second embodiment of a self-check method for a cloud server according to the application.
  • FIG. 8 is a schematic flowchart of a third embodiment of a self-check method for a cloud server according to the application.
  • FIG. 9 is a schematic diagram of functional modules of an embodiment of a self-checking device for a cloud server according to the present application.
  • This application provides a cloud server self-checking device.
  • FIG. 1 is a schematic structural diagram of an operating environment of a cloud server self-checking device involved in a solution in an embodiment of this application.
  • the cloud server self-checking device includes a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005.
  • the communication bus 1002 is used to implement connection and communication between these components.
  • the user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard), and the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).
  • the memory 1005 may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as a magnetic disk memory.
  • the memory 1005 may also be a storage device independent of the foregoing processor 1001.
  • a memory 1005 which is a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a computer program.
  • the operating system is a program that manages and controls the cloud server self-check equipment and software resources, and supports the cloud server self-check program and the operation of other software and/or programs.
  • the network interface 1004 is mainly used to access the network; the user interface 1003 is mainly used to detect confirmation instructions and edit instructions.
  • the processor 1001 may be used to call the cloud server self-check program stored in the memory 1005, and execute the operations of the following cloud server self-check methods.
  • FIG. 2 is a schematic flowchart of an embodiment of a self-checking method for a cloud server according to the present application.
  • the cloud server self-checking method includes the following steps:
  • Step S10 Based on the preset self-check time interval, monitor whether the current self-check time is reached;
  • the current time is monitored and time calculation is performed to determine whether the preset self-check time interval is currently reached.
  • the preset self-check time interval can be customized according to user needs or current business application conditions. time interval.
  • the self-check time interval can be 30 seconds, 1 minute, 10 minutes, 30 minutes, 1 hour, etc., and the specific duration is not limited.
  • Step S20 If the self-check time is currently reached, detect whether there is a port with abnormal load in the current scaling group, where the scaling group is a collection of cloud server instances facing the same scenario;
  • the port load threshold interval of the current scaling group cloud server instance is set. If the current time is accumulated to the self-check time, the load information of each port in the current scaling group is obtained, and the scaling is detected Whether the port load of the group cloud server instance is within the load threshold interval, if the ports of the scaling group are all within the load threshold interval, then there are ports with abnormal load in the scaling group; if the ports of the scaling group are If the load is less than or greater than the load threshold interval, there are ports with abnormal load in the scaling group.
  • Step S30 If it is detected that the port with the abnormal load in the scaling group is detected, based on a preset detection mechanism, detect whether the cloud server instance corresponding to the port with the abnormal load is operating normally;
  • the distribution traffic or load indicators of all cloud server instance ports in the current scaling group are detected, it is detected whether there is a distribution traffic or load abnormality on the cloud server instance ports of the scaling group. If there is a port with abnormal traffic distribution or load in the cloud server instance of the scaling group, the port with abnormal traffic distribution is located, and according to a preset detection mechanism, it is determined whether the cloud server instance corresponding to the port with abnormal load is operating normally.
  • the preset detection mechanism can be formulated in actual conditions, and is not limited here.
  • detect the No. 1 cloud server instance corresponding to port A and then determine Whether server instance 1 is running normally.
  • Step S40 If the cloud server instance corresponding to the port with the abnormal load is not operating normally, remove the cloud server instance corresponding to the port with the abnormal load, and copy the normal cloud server instance to the scaling group;
  • a preset script is used to call the load balancing device configuration control interface to send to the load balancing device Request, set the state of the cloud server instance to closed, stop distributing new requests to the cloud server instance corresponding to the port with the abnormal load, and disconnect the cloud server instance corresponding to the port with the abnormal load, Move the cloud server instance corresponding to the port with the abnormal load out of the scaling group.
  • detect the port in the current scaling group and find that the load of port A is greater than or less than the preset load range, or the distribution traffic is abnormal, according to the current preset detection mechanism, detect the No. 1 cloud server instance corresponding to port A, if If it is determined that the No. 1 cloud server instance is not operating normally, start the preset script, call the load balancing device configuration control interface, notify the load balance device to adjust the status of the No. 1 cloud server instance to shutdown, and stop the distribution to the No. 1 cloud server instance For a new request, disconnect from the cloud server instance No. 1, move the cloud server instance No. 1 out of the scaling group, copy a normal cloud server instance, and add it to the scaling group, replace the No. 1 cloud server instance, and assume the number 1 The distribution traffic of the cloud server instance.
  • the monitoring device monitors whether the self-inspection time is currently reached. If the self-inspection time is currently reached, it detects whether there are ports with abnormal load on all cloud server instance ports in the current scaling group. , Based on a preset detection mechanism, detect whether the cloud server instance corresponding to the port with abnormal load is operating normally, and if it is determined that the cloud server instance corresponding to the port with abnormal load is operating abnormally, remove the port corresponding to the abnormal load Cloud server instance, copy the normal cloud server instance to join the scaling group, replace the abnormal cloud server instance, and then ensure the normal operation of the business, realize automatic operation, without manual intervention, create and release the cloud according to the user preset strategy Server instance.
  • FIG. 3 is a detailed flowchart of an embodiment of step S20 in FIG. 2.
  • the above step S20 also includes:
  • Step S201 If the self-check time is currently reached, use the load balancing device to collect statistics on the load status of the ports in the scaling group, where the load includes distributed traffic and inflow and outflow rates;
  • Step S202 Determine whether the load of the ports in the scaling group is lower or higher than a preset load threshold interval
  • Step S203 If the load of a port in the scaling group is lower than or higher than a preset load threshold interval, the port load is abnormal;
  • Step S204 If the loads of the ports in the scaling group are all within the preset load threshold interval, the average load of the ports in the scaling group is normal.
  • the current time is monitored in real time, and when the time reaches the preset interval self-check time, the scaling group checks whether the cloud server instances in the group are running normally. Further, firstly, it is detected whether there are ports with abnormal load in the scaling group, and the load balancing device connected to the scaling group can collect statistics on the port load conditions in the scaling group. By comparing the relationship between the statistical result and the preset load interval, it is determined whether the port load in the scaling group is normal. Wherein, the load includes the distribution traffic, the inflow and outflow rate of the port load of the cloud server instances in the scaling group.
  • the preset port load distribution traffic threshold range of A scaling group is 5-8G, and the load balancing device connected to A scaling group obtains the distribution traffic of the cloud server instance port in the A scaling group. If there is a cloud in the A scaling group If the distribution traffic of the server instance port is lower or higher than the preset threshold interval, the cloud server instance port load in the A scaling group lower or higher than the preset threshold interval is abnormal; if the port in the A scaling group distributes traffic If they are all within the preset threshold range, the cloud server instance ports in the A scaling group have a normal load.
  • FIG. 4 is a detailed flowchart of an embodiment of step S30 in FIG.
  • the above step S30 also includes:
  • Step S301 If a port with an abnormal load is detected in the scaling group, based on the port with an abnormal load, determine the cloud server instance corresponding to the port with the abnormal load;
  • Step S302 Detect the cloud server instance corresponding to the port with the abnormal load, and count the current detection time;
  • Step S303 if the detection time is greater than the preset detection timeout time, it is determined that the detection timeout;
  • Step S304 Count the number of detection timeouts
  • Step S305 If detection timeouts occur continuously and the number of timeouts reaches the preset number threshold, it is determined that the cloud server instance corresponding to the port with the abnormal load is not operating normally;
  • Step S306 If the continuous detection timeout does not occur and the number reaches the preset number threshold, it is determined that the cloud server instance corresponding to the port with the abnormal load is operating normally.
  • a port with abnormal load when a port with abnormal load is detected in the scaling group, based on the port with abnormal load, search for the cloud server instance to which the abnormal load port belongs, and locate the cloud server instance, Perform detection on the cloud server instance, count the time-consuming detection of the cloud server instance, compare the statistical result with the preset detection timeout time, if the statistical result is greater than the preset detection timeout time, the current detection timeout.
  • the cloud server instance detection timeout the cloud server instance is re-examined, and the number of detection timeouts is counted according to the detection results of the re-inspection. If the detection timeout occurs for the cloud server instance multiple times in a row, continuous detection When the number of timeouts reaches the preset threshold, it is judged that the cloud server is operating abnormally.
  • the detection timeout time For example, set the detection timeout time to 1 minute.
  • the cloud server instance A is searched based on port a, the cloud server instance A is detected, and the time spent on detecting the cloud server instance A is counted 1 minute than the preset detection timeout time, this detection timed out.
  • the cloud server A will be re-checked and the re-check results will be counted.
  • the number of re-checks can be a preset threshold or customized. If the re-check results are continuous When the timeout expires and the number of times reaches the preset number threshold, the cloud server instance A is not operating normally.
  • FIG. 5 is a schematic diagram of another detailed flow of step S30 in FIG.
  • the above step S30 also includes:
  • Step S3001 If a port with an abnormal load is detected in the scaling group, report and count the cloud server instance corresponding to the port with the abnormal load, and count the number of cloud server instance abnormalities corresponding to the port with the abnormal load;
  • Step S3002 If the cloud server instance corresponding to the port with the abnormal load has multiple consecutive abnormalities, and the number of abnormalities reaches the preset threshold, it is determined that the cloud server instance corresponding to the port with the abnormal load is not operating normally;
  • Step S3003 If the cloud server instance corresponding to the port with the abnormal load does not have multiple consecutive abnormalities, and the number of abnormalities reaches the preset threshold, it is determined that the cloud server instance corresponding to the port with the abnormal load is operating normally.
  • the cloud server instance corresponding to the port with the abnormal load is determined, and the cloud server instance corresponding to the port with the abnormal load is reported to the scaling group to connect
  • the load balancing device records the current abnormality of the cloud server instance corresponding to the port with the abnormal load. If the self-check is performed again, the port load of the cloud server instance corresponding to the port with the abnormal load is detected again, and the load is reported The cloud server instance corresponding to the port with the abnormal load is abnormal for the second time.
  • the corresponding port of the cloud server corresponding to the port with the abnormal load has multiple abnormalities in succession, and the number of abnormalities reaches the preset threshold, it is determined that the load is abnormal
  • the cloud server instance corresponding to the port is not operating normally.
  • the cloud server instance corresponding to the port corresponding to the cloud server instance A The reported statistics is that the cloud server instance A is abnormal once.
  • the second periodic check still finds the cloud server instance A abnormal.
  • the reported statistics shows that the cloud server instance A is abnormal 2 times.
  • the third periodic check finds that the cloud server instance A is still abnormal.
  • the reported statistics show that the cloud server instance A is abnormal 3 times.
  • the cloud server instance A is found to be abnormal in the 3 inspections, it is determined that the cloud server instance A is not operating normally.
  • the cloud server instance A is found to be normal in the third periodic inspection, the cloud server instance A is abnormal for 2 consecutive times, and the preset threshold is not reached, it is determined that the cloud server instance A is operating normally.
  • FIG. 6 is a detailed flowchart of step S40 in FIG. 2.
  • the above step S40 also includes:
  • Step S401 if the cloud server instance corresponding to the port with the abnormal load is not operating normally, call the load balancing device configuration control interface;
  • Step S402 Adjust the state of the cloud server instance corresponding to the port with abnormal load to be closed through the load balancing device;
  • Step S403 request the load balancing device to stop distributing new traffic to the cloud server instance corresponding to the abnormal load port, and disconnect the cloud server instance corresponding to the abnormal load port;
  • Step S404 Remove the cloud server instance corresponding to the port with the abnormal load, and copy the normal cloud server instance to the scaling group.
  • the control interface of the cloud server instance is searched, and the load balancing device is called to configure the control interface through a preset script.
  • the control interface adjusts the state of the cloud server instance to closed, stops distributing new requests to the cloud server instance, disconnects the cloud server, and moves the cloud server instance out of the scaling group.
  • the No. 1 cloud server instance corresponding to port A is detected. If the cloud server instance is not running normally, start the preset script, call the load balancing device configuration control interface, notify the load balance device to adjust the status of the cloud server instance 1 to shut down, and stop distributing new requests to the cloud server instance 1 , Disconnect the connection with the No. 1 cloud server instance, and move the No. 1 cloud server instance out of the scaling group.
  • FIG. 7 is a schematic flowchart of a second embodiment of a self-check method for a cloud server according to this application. Based on the foregoing embodiment, in this embodiment, after step S40, the method further includes:
  • Step S001 Detect the number of cloud server instances in the scaling group
  • Step S002 If the number of cloud server instances is less than the preset minimum value, replicate the normal cloud server instances and join the scaling group;
  • Step S003 If the number of cloud server instances is greater than the preset maximum value, randomly remove the excess cloud server instances.
  • the load balancing device connected to the scaling group is used to count the number of cloud server instances in the scaling group, based on the statistical results and presets A preset interval of cloud server instances is used to determine whether the scaling group needs to increase or decrease cloud server instances. Wherein, if the number of cloud server instances in the scaling group is less than the preset minimum value, then replicate normal cloud server instances are added to the scaling group; if the number of cloud server instances is greater than the preset maximum value, randomly remove the excess part Cloud server instance.
  • the current business requires 50-60 cloud server instances.
  • the cloud service instances in the scaling group are less than 50, then copy the normal cloud server instances , Join the scaling group until it reaches 50 cloud server instances, and then consider whether to increase it according to business needs; if there are more than 60 cloud server instances in the scaling group, randomly select cloud server instances for removal , Until the number of cloud server instances drops to less than 60.
  • FIG. 8 is a schematic flowchart of a third embodiment of a self-check method for a cloud server according to this application. Based on the foregoing embodiment, in this embodiment, after step S40, the method further includes:
  • Step S0001 Detect the load situation of the cloud server instance of the scaling group, where the load includes memory utilization, cpu utilization, disk read and write rate, inflow and outflow rate, and the number of device connections;
  • Step S0002 When the real-time load average value of the cloud server instances of the scaling group is greater than the preset maximum threshold, add new cloud server instances of the scaling group according to preset scaling rules, and evenly distribute the scaling through the load balancing device Group load tasks of cloud server instances, where the rule is to add or reduce N cloud server instances, and N is an integer;
  • Step S0003 When the real-time load average value of the scaling group is less than the preset minimum threshold, reduce the cloud server instances of the scaling group according to the preset scaling rules, and evenly distribute the load of the cloud server instances of the scaling group through the load balancing device task.
  • the load of the current scaling group cloud server instances is detected, and the scaling group is determined based on the detection result and the preset load interval of the scaling group Whether to add or reduce cloud server instances.
  • the cloud server instances are correspondingly reduced according to the preset scaling rules; when the average real-time load of the cloud server instances in the scaling group When it is less than the preset minimum threshold, a new cloud server instance will be added according to the preset scaling rules.
  • the preset scaling rule is to increase or decrease 2 instances and monitor the real-time average load of the scaling group load in real time.
  • the scaling group will respond accordingly Two new instances are added, and the two new instances distribute business traffic evenly through load balancing, thereby reducing resource pressure and ensuring the stability of the business system; and when it is monitored that the real-time load average of the scaling group load is lower than the preset
  • the scaling group selects 2 instances accordingly and moves out of the scaling group to save resource costs.
  • the application also provides a cloud server self-checking device.
  • FIG. 9 is a schematic diagram of functional modules of an embodiment of a self-checking device for a cloud server according to the present application.
  • the cloud server self-checking device includes:
  • the monitoring module 10 is used to monitor whether the current self-check time is reached based on the preset interval self-check time;
  • the first detection module 20 is configured to detect whether there are ports with abnormal load in the current scaling group if the self-checking time is currently reached, where the scaling group is a collection of cloud server instances facing the same scenario;
  • the second detection module 30 is configured to, if the abnormal load port is detected in the scaling group, based on a preset detection mechanism, detect whether the cloud server instance corresponding to the abnormal load port is operating normally;
  • the removal module 40 is configured to remove the cloud server instance corresponding to the port with the abnormal load if the cloud server instance corresponding to the port with the abnormal load is not operating normally, and copy the cloud server instance with the normal operation to join the scaling group.
  • the monitoring module 10 monitors whether the current time reaches the self-check time based on the preset interval self-check time, and the first detection module 20 detects whether there is abnormal load in the current scaling group when the current time reaches the self-check time.
  • the second detection module 30 detects that the port with the abnormal load in the scaling group, based on a preset detection mechanism, detects whether the cloud server instance corresponding to the port with the abnormal load is operating normally, and removes the module 40
  • the cloud server instance corresponding to the port with the abnormal load is not operating normally, the cloud server instance corresponding to the port with the abnormal load is removed, and the cloud server instance that is running normally is copied to the scaling group.
  • the application also provides a non-volatile computer-readable storage medium.
  • a cloud server self-check program is stored on the computer-readable storage medium, and the cloud server self-check program is executed by a processor to implement the cloud server self-check method as described in any of the above embodiments A step of.
  • the cloud server self-check program when executed by the processor, the following steps of the cloud server self-check method are implemented:
  • the self-check time If the self-check time is currently reached, detect whether there are ports with abnormal load in the current scaling group, where the scaling group is a collection of cloud server instances facing the same scenario;
  • the cloud server instance corresponding to the port with the abnormal load is not operating normally, the cloud server instance corresponding to the port with the abnormal load is removed, and the normal cloud server instance is copied to the scaling group.
  • the following steps of the cloud server self-check method are implemented when the cloud server self-check program is executed by the processor:
  • the load balancing device uses the load balancing device to collect statistics on the load status of the ports in the scaling group, where the load includes distributed traffic and inflow and outflow rates;
  • the port load is abnormal
  • the cloud server self-check program when executed by the processor, the following steps of the cloud server self-check method are implemented:
  • a port with an abnormal load is detected in the scaling group, based on the port with the abnormal load, determine the cloud server instance corresponding to the port with the abnormal load;
  • the method of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. ⁇
  • the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product.
  • the computer software product is stored in a storage medium (such as ROM/RAM), including Several instructions are used to make a terminal (which may be a mobile phone, a computer, a server, or a network device, etc.) execute the method described in each embodiment of the present application.

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Abstract

一种云服务器自检方法、装置、设备及计算机可读存储介质，涉及云技术领域。所述方法包括以下步骤：基于预置的自检时间间隔，监控当前是否达到自检时间（S10）；若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口（S20）；若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应云服务器实例是否运行正常（S30）；若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组（S40）。所述方法可保证云服务业务正常运行，实现自动化运营，无需人工干预，根据用户预设策略创建和释放云服务器实例。

Description

云服务器自检方法、装置、设备及计算机可读存储介质
本申请要求于2019年6月18日提交中国专利局、申请号为201910525623.3，发明名为“云服务器自检方法、装置、设备及计算机可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及云技术领域，尤其涉及关于云服务器自检方法、装置、设备及可读存储介质。
背景技术
随着现代社会互联网技术的发展，云计算技术作为新一代的IT模式也得到了越来越广泛的应用。云计算技术从网格计算，并行计算和分布式计算发展而来，用户可以利用它来便捷地按需通过网络访问一个可配置的计算资源(如计算、网络、存储、应用和服务等)的共享池，只需最小化的管理工作量或服务提供商干预就可以快速地开通或释放资源。
发明人意识到随着企业的应用业务量是每年递增的，对云服务器的需求也越来越多，运维人员难以做到时刻检查服务器的健康状态，进而实时替换，这样需要耗费大量的人工成本，无法保证业务系统时时稳定运行。
发明内容
本申请的主要目的在于提供一种关于云服务器自检方法、装置、设备及可读存储介质，旨在解决如何实现云服务器定时自检，自动化运营的技术问题。
为实现上述目的，本申请提供的一种关于云服务器自检方法，所述云服务器自检方法包括：
基于预置的自检时间间隔，监控当前是否达到自检时间；
若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口，其中，所述伸缩组为面向同一场景的云服务器实例集合；
若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应云服务器实例是否运行正常；
若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
可选地，所述若当前达到自检时间，则检测当前伸缩组内是否存在负载 异常的端口还包括：
若当前达到自检时间，则通过负载均衡设备统计所述伸缩组内端口的负载情况，其中，所述负载包括分发流量、流入流出速率；
判断所述伸缩组内端口的负载是否低于或高于预置负载阈值区间；
若所述伸缩组内存在端口的负载低于或高于预置负载阈值区间，则所述端口负载异常；
若所述伸缩组内端口的负载均在预置负载阈值区间内，则所述伸缩组内的端口均负载正常。
可选地，所述若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应的云服务器实例是否运行正常包括：
若检测到所述伸缩组存在负载异常的端口，则基于所述负载异常的端口，确定所述负载异常的端口对应的云服务器实例；
检测所述负载异常的端口对应的云服务器实例，并统计当前检测时间；
若当前检测时间大于预置检测超时时间，则判定检测超时；
统计检测超时次数；
若连续出现检测超时，且超时次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
若未出现连续检测超时且次数达到预置次数阈值，则判断所述负载异常的端口对应的云服务器实例运行正常。
可选地，所述若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应的云服务器实例是否运行正常还包括：
若检测到所述伸缩组存在负载异常的端口，则上报统计所述负载异常的端口对应的云服务器实例，并统计所述负载异常的端口对应的云服务器实例异常次数；
若所述负载异常的端口对应的云服务器实例存在连续多次异常，且异常次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
若所述负载异常的端口对应的云服务器实例未存在连续多次异常，且异常次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行正常。
可选地，所述若所述负载异常的端口对应的云服务器实例运行不正常，移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组包括：
若所述负载异常的端口对应的云服务器实例运行不正常，则调用负载均衡设备配置控制接口；
通过所述负载均衡设备，将所述负载异常的端口对应的云服务器实例状态调整为关闭；
请求所述负载均衡设备停止向所述负载异常的端口对应的云服务器实例分发新的流量，并断开与所述负载异常的端口对应的云服务器实例的连接；
移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
可选地，在若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组的步骤之后，还包括：
检测所述伸缩组中的云服务器实例数量；
若所述云服务器实例数量小于预置最小值，则复制正常的云服务器实例加入伸缩组；
若所述云服务器实例数量大于预置最大值，则随机移除高于部分的云服务器实例。
可选地，在若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组的步骤之后，还包括：
检测所述伸缩组云服务器实例负载情况，其中，所述负载包括内存利用率、cpu利用率、磁盘读写速率、流入流出速率、设备连接数；
当所述伸缩组云服务器实例的实时负载平均值大于预设最大阈值时，根据预置伸缩规则，增加所述伸缩组的云服务器实例，其中，所述规则为增加或减少N个云服务器实例，N为整数；
当伸缩组的实时负载平均值小于预设最小阈值时，根据预置伸缩规则，减少所述伸缩组的云服务器实例。
进一步地，为实现上述目的，本申请还提供一种云服务器自检装置，所述云服务器自检装置包括：
监控模块，用于基于预置的自检时间间隔，监控当前是否达到自检时间；
第一检测模块，用于若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口，其中，所述伸缩组为面向同一场景的云服务器实例集合；
第二检测模块，用于若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应云服务器实例是否运行正常；
第一移除模块，用于若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
进一步地，为实现上述目的，本申请还提供一种云服务器自检设备，所述云服务器自检设备包括存储器、处理器以及存储在所述存储器上并可在所述处理器上运行的云服务器自检程序，所述云服务器自检程序被所述处理器执行时实现如上述任一项所述的云服务器自检方法的步骤。
进一步地，为实现上述目的，本申请还提供一种非易失性计算机可读存储介质，所述计算机可读存储介质上存储有云服务器自检程序，所述云服务器自检程序被处理器执行时实现如上述任一项所述的云服务器自检方法的步骤。
附图说明
图1为本申请实施例方案涉及的云服务器自检设备运行环境的结构示意图；
图2为本申请云服务器自检方法一实施例的流程示意图；
图3为图2中步骤S20一实施例的细化流程示意图。；
图4为图2中步骤S30一实施例的细化流程示意图；
图5为图2中步骤S30的另一细化流程示意图；
图6为图2中步骤S40的细化流程示意图；
图7为本申请云服务器自检方法第二实施例的流程示意图；
图8为本申请云服务器自检方法第三实施例的流程示意图；
图9为本申请云服务器自检装置一实施例的功能模块示意图。
具体实施方式
本申请提供一种云服务器自检设备。
参照图1，图1为本申请实施例方案涉及的云服务器自检设备运行环境的结构示意图。
如图1所示，该云服务器自检设备包括：处理器1001，例如CPU，通信总线1002、用户接口1003，网络接口1004，存储器1005。其中，通信总线1002用于实现这些组件之间的连接通信。用户接口1003可以包括显示屏(Display)、输入单元比如键盘(Keyboard)，网络接口1004可选的可以包括标准的有线接口、无线接口(如WI-FI接口)。存储器1005可以是高速RAM 存储器，也可以是稳定的存储器(non-volatile memory)，例如磁盘存储器。存储器1005可选的还可以是独立于前述处理器1001的存储装置。
如图1所示，作为一种计算机可读存储介质的存储器1005中可以包括操作系统、网络通信模块、用户接口模块以及计算机程序。其中，操作系统是管理和控制云服务器自检设备和软件资源的程序，支持云服务器自检程序以及其它软件和/或程序的运行。
在图1所示的云服务器自检设备的硬件结构中，网络接口1004主要用于接入网络；用户接口1003主要用于侦测确认指令和编辑指令等。而处理器1001可以用于调用存储器1005中存储的云服务器自检程序，并执行以下云服务器自检方法的各实施例的操作。
基于上述云服务器自检设备硬件结构，提出本申请云服务器自检方法的各个实施例。
参照图2，图2为本申请云服务器自检方法一实施例的流程示意图。本实施例中，所述云服务器自检方法包括以下步骤：
步骤S10：基于预置的自检时间间隔，监控当前是否达到自检时间；
本实施例中，监控当前时间，并进行时间计算，判断当前是否达到预置的自检时间间隔，其中，预置的自检时间间隔可以根据用户需要，或当前业务应用情况，自定义自检时间间隔。例如，自检时间间隔可以是间隔30秒，1分钟，10分钟，30分钟，1小时等，具体时长不限。
步骤S20：若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口，其中，所述伸缩组为面向同一场景的云服务器实例集合；
本实施例中，基于负载均衡设备，设置所述当前伸缩组云服务器实例的端口负载阈值区间，若当前时间累积到自检时间，获取当前伸缩组内每个端口的负载信息，检测所述伸缩组云服务器实例的端口负载是否在所述负载阈值区间，若所述伸缩组的端口均在所述负载阈值区间内，则所述伸缩组内部存在负载异常的端口；若所述伸缩组的端口负载存在小于或大于所述负载阈值区间，则所述伸缩组组存在负载异常的端口。
步骤S30：若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应云服务器实例是否运行正常；
本实施例中，基于上述步骤的检测结果，若检测当前伸缩组内所有云服务器实例端口的分发流量或负载指标，检测所述伸缩组云服务器实例端口是否存在分发流量或负载异常，若检测到所述伸缩组云服务器实例存在流量分发或负载异常的端口，则定位所述流量分发异常的端口，根据预置的检测机制，确定所述负载异常的端口所对应的云服务器实例运行是否正常。其中， 预置的检测机制可以实际情况制定，此处不作限定。
例如，检测当前伸缩组中的端口，发现端口A流量分发异常，负载大于或小于预置负载区间，根据当前预置的检测机制，对端口A所对应的1号云服务器实例进行检测，进而判断1号服务器实例运行是否正常。
步骤S40：若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制正常的云服务器实例加入伸缩组；
本实施例中，基于预置的检测机制，若确定所述负载异常的端口对应的云服务器实例运行不正常，通过预置的脚本，调用负载均衡设备配置控制接口，向所述负载均衡设备发送请求，将所述云服务器实例的状态置为关闭，并停止对所述负载异常的端口对应的云服务器实例分发新的请求，断开与所述负载异常的端口对应的云服务器实例的连接，将所述负载异常的端口对应的云服务器实例移出所述伸缩组。
例如，检测当前伸缩组中的端口，发现端口A负载大于或小于预置负载区间，或者分发流量异常，根据当前预置的检测机制，对端口A所对应的1号云服务器实例进行检测，若确定1号云服务器实例运行不正常，则启动预置的脚本，调用负载均衡设备配置控制接口，通知负载均衡设备将1号云服务器实例的状态调整为关闭，并停止对1号云服务器实例分发新的请求，断开与1号云服务器实例的连接，将1号云服务器实例移出伸缩组，并复制一个正常的云服务器实例，加入到伸缩组中，替换1号云服务器实例，承担1号云服务器实例的分发流量。
本实施例基于预置的自检时间间隔，由监控设备监控当前是否达到自检时间，若当前达到自检时间，则检测当前伸缩组内所有云服务器实例端口是否存在负载异常的端口，若存在，基于预置检测机制，检测所述负载异常的端口对应的云服务器实例是否运行正常，若确定所述负载异常的端口对应的云服务器实例运行不正常，移除所述负载异常的端口对应的云服务器实例，复制运行正常的云服务器实例加入到伸缩组，替代所述运行不正常的云服务器实例，进而保证业务正常运行，实现自动化运营，无需人工干预，根据用户预设策略创建和释放云服务器实例。
参照图3，图3为图2中步骤S20一实施例的细化流程示意图。上述步骤S20还包括：
步骤S201：若当前达到自检时间，则通过负载均衡设备统计所述伸缩组内端口的负载情况，其中，所述负载包括分发流量、流入流出速率；
步骤S202：判断所述伸缩组内端口的负载是否低于或高于预置负载阈值 区间；
步骤S203：若所述伸缩组内存在端口的负载低于或高于预置负载阈值区间，则所述端口负载异常；
步骤S204：若所述伸缩组内端口的负载均在预置负载阈值区间内，则所述伸缩组内的端口均负载正常。
本实施例中，实时监控当前时间，当时间达到预置的间隔自检时间，所述伸缩组对组内云服务器实例检测是否运行正常。进一步地，首先检测所述伸缩组内是否存在负载异常的端口，可以通过所述伸缩组连接的负载均衡设备统计所述伸缩组内的端口负载情况。通过比较统计结果与预置负载区间的关系，判断所述伸缩组内的端口负载是否正常。其中，所述负载包括所述伸缩组中云服务器实例的端口负载的分发流量、流入流出速率等。
例如，预置A伸缩组中的端口负载分发流量阈值区间为5-8G，通过A伸缩组连接的负载均衡设备，获取A伸缩组中云服务器实例端口的分发流量，若A伸缩组中存在云服务器实例端口的分发流量低于或高于预置的阈值区间，则A伸缩组内低于或高于预置的阈值区间的云服务器实例端口负载不正常；若A伸缩组中的端口分发流量均在预置的阈值区间之内，则A伸缩组中的云服务器实例端口均负载正常。
参照图4，图4为图2中步骤S30一实施例的细化流程示意图。上述步骤S30还包括：
步骤S301：若检测到所述伸缩组存在负载异常的端口，则基于所述负载异常的端口，确定所述负载异常的端口对应的云服务器实例；
步骤S302：检测所述负载异常的端口对应的云服务器实例，并统计当前检测时间；
步骤S303：若检测时间大于预置检测超时时间，则判定检测超时；
步骤S304：统计检测超时次数；
步骤S305：若连续出现检测超时，且超时次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
步骤S306：若未出现连续检测超时且次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行正常。
本实施例中，基于上述实施例，当检测到所述伸缩组存在负载异常的端口时，基于所述负载异常的端口，查找所述负载异常端口所属云服务器实例，定位所述云服务器实例，对所述云服务器实例进行检测，统计所述云服务器实例的检测耗费时长，将统计结果与预置检测超时时间做比较，若统计结果大于预置检测超时时间，则本次检测超时。此外，当发现有云服务器实例检 测超时时，对该台云服务器实例进行复检，并根据复检的检测结果，统计检测超时次数，若该台云服务器实例连续多次出现检测超时，连续检测超时的次数达到预置次数阈值，则判断该台云服务器运行不正常。
例如，设置检测超时时间为1分钟，当检测到伸缩组中端口a存在负载异常，基于端口a查找所述云服务器实例A，对云服务器实例A进行检测，统计检测云服务器实例A的耗费时长比预置的检测超时时间1分钟，本次检测超时。此外，在检测到云服务器实例A存在检测超时时，则对云服务器A进行复检，统计复检结果，其中，复检次数可以是预置次数阈值，也可以自定义，若复检结果连续超时，且次数达到预置次数阈值，则云服务器实例A运行不正常。
参照图5，图5为图2中步骤S30的另一细化流程示意图。上述步骤S30还包括：
步骤S3001：若检测到所述伸缩组存在负载异常的端口，则上报统计所述负载异常的端口对应的云服务器实例，并统计所述负载异常的端口对应的云服务器实例异常次数；
步骤S3002：若所述负载异常的端口对应的云服务器实例存在连续多次异常，且异常次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
步骤S3003：若所述负载异常的端口对应的云服务器实例未存在连续多次异常，且异常次数达到预置次数阈值，则判断所述负载异常的端口对应的云服务器实例运行正常。
本实施例中，当检测到所述伸缩组存在负载异常的端口时，判定所述负载异常的端口对应的云服务器实例，上报所述负载异常的端口对应的云服务器实例给所述伸缩组连接的负载均衡设备，记录所述负载异常的端口对应的云服务器实例的本次异常，若再次自检时，再次检测到所述负载异常的端口对应的云服务器实例的端口负载异常，上报所述负载异常的端口对应的云服务器实例二次异常，当所述负载异常的端口对应的云服务器的对应端口连续出现多次异常，且异常次数达到预置次数阈值时，则判定所述负载异常的端口对应的云服务器实例运行不正常。
例如，预设连续3次检测到同一云服务器实例异常，即判定该云服务器实例运行不正常，若在第一次周期检测到当前伸缩组负载异常的端口对应的云服务器实例为云服务器实例A，上报统计为云服务器实例A异常1次，,第二次周期检查，还是发现云服务器实例A异常，上报统计为云服务器实例A异常2次，第三次周期检查发现云服务器实例A依旧异常，上报统计为云服 务器实例A异常3次，此时3次检查都发现云服务器实例A异常，则判定云服务器实例A运行不正常。其中，若第三次周期检测发现云服务器实例A正常，则云服务器实例A异常连续次数为2次，未达到预置的阈值，则判定断云服务器实例A运行正常。
参照图6，图6为图2中步骤S40的细化流程示意图。上述步骤S40还包括：
步骤S401：若所述负载异常的端口对应的云服务器实例运行不正常，则调用负载均衡设备配置控制接口；
步骤S402：通过所述负载均衡设备，将所述负载异常的端口对应的云服务器实例状态调整为关闭；
步骤S403：请求所述负载均衡设备停止向所述负载异常的端口对应的云服务器实例分发新的流量，并断开与所述负载异常的端口对应的云服务器实例的连接；
步骤S404：移除所述负载异常的端口对应的云服务器实例，并复制正常的云服务器实例加入伸缩组。
本实施例中，当确定一台云服务器实例运行不正常时，查找该云服务器实例的控制接口，通过预置脚本，调用负载均衡设备配置所述控制接口，基于所述负载均衡设备，通过所述控制接口将该云服务器实例的状态调整为关闭，并停止向该云服务器实例分发新的请求，断开与该云服务器的连接，将该云服务器实例移出伸缩组。
例如，检测当前伸缩组中的端口，发现端口A负载异常，或者分发流量低于正常水平，根据当前预置的检测机制，对端口A所对应的1号云服务器实例进行检测，若确定1号云服务器实例运行不正常，则启动预置的脚本，调用负载均衡设备配置控制接口，通知负载均衡设备将1号云服务器实例的状态调整为关闭，并停止对1号云服务器实例分发新的请求，断开与1号云服务器实例的连接，将1号云服务器实例移出伸缩组。
参照图7，图7为本申请云服务器自检方法第二实施例的流程示意图。基于上述实施例，本实施例中，在步骤S40之后，还包括：
步骤S001：检测所述伸缩组中的云服务器实例数量；
步骤S002：若所述云服务器实例数量小于预置最小值，则复制正常的云服务器实例加入伸缩组；
步骤S003：若所述云服务器实例数量大于预置最大值，则随机移除超出部分的云服务器实例。
本实施例中，在定期自检，在移除运行不正常的云服务器实例后，通过所述伸缩组连接的负载均衡设备，统计所述伸缩组的云服务器实例数量，基于统计结果与预置云服务器实例预置区间，判断所述伸缩组是否需要增加或减少云服务器实例。其中，若所述伸缩组的云服务器实例数量小于预置最小值，则复制正常的云服务器实例加入伸缩组中；若所述云服务器实例数量大于预置最大值，则随机移除超出部分的云服务器实例。
例如，当前业务需要云服务器实例50-60台，在一次定期检测中，移除运行不正常的云服务器实例后，伸缩组中的云服务实例低于50台，则复制运行正常的云服务器实例，加入到所述伸缩组，直至达到50台云服务器实例，再根据业务需要考虑是否再进行增加；若所述伸缩组中的云服务器实例高于60台，则随机选择云服务器实例进行移除，直至云服务器实例数量降到60台以内。
参照图8，图8为本申请云服务器自检方法第三实施例的流程示意图。基于上述实施例，本实施例中，在步骤S40之后，还包括：
步骤S0001：检测所述伸缩组云服务器实例负载情况，其中，所述负载包括内存利用率、cpu利用率、磁盘读写速率、流入流出速率、设备连接数；
步骤S0002：当所述伸缩组云服务器实例的实时负载平均值大于预设最大阈值时，根据预置伸缩规则，新增所述伸缩组的云服务器实例，通过负载均衡设备，均匀分配所述伸缩组云服务器实例的负载任务，其中，所述规则为新增或减少N个云服务器实例，N为整数；
步骤S0003：当伸缩组的实时负载平均值小于预设最小阈值时，根据预置伸缩规则，减少所述伸缩组的云服务器实例，通过负载均衡设备，均匀分配所述伸缩组云服务器实例的负载任务。
本实施例中，在定期自检，移除运行不正常的云服务器实例后，检测当前伸缩组云服务器实例的负载情况，基于检测结果与所述伸缩组预置负载区间，判断所述伸缩组是否需要新增或减少云服务器实例。其中，当所述伸缩组中云服务器实例的实时负载平均值大于预设最大阈值时，根据预置伸缩规则，相应的减少云服务器实例；当所述伸缩组中云服务器实例的实时负载平均值小于预置最小阈值时，根据预置伸缩规则，相应的新增云服务器实例。
例如，预设伸缩规则为增加或减少2个实例，实时监控伸缩组负载的实时负载平均值，则当监控到所述伸缩组负载的实时负载平均值超过预置的最大阈值时，伸缩组相应新增2个实例，新增2个实例通过负载均衡，均匀进行业务流量分发，从而降低资源压力，确保业务系统稳定；而当监控到所述伸缩组负载的实时负载平均值低于预置的最小阈值时，伸缩组相应选择2个 实例，移出伸缩组，节省资源成本。
本申请还提供一种云服务器自检装置。
参照图9，图9为本申请云服务器自检装置一实施例的功能模块示意图。本实施例中，所述云服务器自检装置包括：
监控模块10，用于基于预置的间隔自检时间，监控当前是否达到自检时间；
第一检测模块20，用于若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口，其中，所述伸缩组为面向同一场景的云服务器实例集合；
第二检测模块30，用于若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应云服务器实例是否运行正常；
移除模块40，用于若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
本实施例中，监控模块10基于预置的间隔自检时间，监控当前时间是否达到自检时间，第一检测模块20在当前时间达到自检时间时，检测当前伸缩组内是否存在负载异常的端口，第二检测模块30在检测到所述伸缩组中存在所述负载异常的端口时，基于预置检测机制，检测所述负载异常的端口对应的云服务器实例是否运行正常，移除模块40在所述负载异常的端口对应的云服务器实例运行不正常时，移除所述负载异常端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
本申请还提供一种非易失性计算机可读存储介质。
本实施例中，所述计算机可读存储介质上存储有云服务器自检程序，所述云服务器自检程序被处理器执行时实现如上述任一项实施例中所述的云服务器自检方法的步骤。
可选地，在一具体实施例中，所述云服务器自检程序被所述处理器执行时实现如下云服务器自检方法的步骤：
基于预置的自检时间间隔，监控当前是否达到自检时间；
若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口，其中，所述伸缩组为面向同一场景的云服务器实例集合；
若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应云服务器实例是否运行正常；
若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
可选地，在一具体实施例中，所述云服务器自检程序被所述处理器执行时实现如下云服务器自检方法的步骤：
若当前达到自检时间，则通过负载均衡设备统计所述伸缩组内端口的负载情况，其中，所述负载包括分发流量、流入流出速率；
判断所述伸缩组内端口的负载是否低于或高于预置负载阈值区间；
若所述伸缩组内存在端口的负载低于或高于预置负载阈值区间，则所述端口负载异常；
若所述伸缩组内端口的负载均在预置负载阈值区间内，则所述伸缩组内的端口均负载正常。
可选地，在一具体实施例中，所述云服务器自检程序被所述处理器执行时实现如下云服务器自检方法的步骤：
若检测到所述伸缩组存在负载异常的端口，则基于所述负载异常的端口，确定所述负载异常的端口对应的云服务器实例；
检测所述负载异常的端口对应的云服务器实例，并统计当前检测时间；
若当前检测时间大于预置检测超时时间，则判定检测超时；
统计检测超时次数；
若连续出现检测超时，且超时次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
若未出现连续检测超时且次数达到预置次数阈值，则判断所述负载异常的端口对应的云服务器实例运行正常。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器或者网络设备等)执行本申请各个实施例所述的方法。

Claims (20)

  1. 一种云服务器自检方法，所述云服务器自检方法包括以下步骤：
    基于预置的自检时间间隔，监控当前是否达到自检时间；
    若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口，其中，所述伸缩组为面向同一场景的云服务器实例集合；
    若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应云服务器实例是否运行正常；
    若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
  2. 如权利要求1所述的云服务器自检方法，所述若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口还包括：
    若当前达到自检时间，则通过负载均衡设备统计所述伸缩组内端口的负载情况；其中，所述负载包括分发流量、流入流出速率；
    判断所述伸缩组内端口的负载是否低于或高于预置负载阈值区间；
    若所述伸缩组内存在端口的负载低于或高于预置负载阈值区间，则所述端口负载异常；
    若所述伸缩组内端口的负载均在预置负载阈值区间内，则所述伸缩组内的端口均负载正常。
  3. 如权利要求1所述的云服务器自检方法，所述若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应的云服务器实例是否运行正常包括：
    若检测到所述伸缩组存在负载异常的端口，则基于所述负载异常的端口，确定所述负载异常的端口对应的云服务器实例；
    检测所述负载异常的端口对应的云服务器实例，并统计当前检测时间；
    若当前检测时间大于预置检测超时时间，则判定检测超时；
    统计检测超时次数；
    若连续出现检测超时，且超时次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
    若未出现连续检测超时且次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行正常。
  4. 如权利要求1所述的云服务器自检方法，所述若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应的云服务器实例是否运行正常还包括：
    若检测到所述伸缩组存在负载异常的端口，则上报统计所述负载异常的端口对应的云服务器实例，并统计所述负载异常的端口对应的云服务器实例异常次数；
    若所述负载异常的端口对应的云服务器实例存在连续多次异常，且异常次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
    若所述负载异常的端口对应的云服务器实例未存在连续多次异常，且异常次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行正常。
  5. 如权利要求1所述的云服务器自检方法，所述若所述负载异常的端口对应的云服务器实例运行不正常，移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组包括：
    若所述负载异常的端口对应的云服务器实例运行不正常，则调用负载均衡设备配置控制接口；
    通过所述负载均衡设备，将所述负载异常的端口对应的云服务器实例状态调整为关闭；
    请求所述负载均衡设备停止向所述负载异常的端口对应的云服务器实例分发新的流量，并断开与所述负载异常的端口对应的云服务器实例的连接；
    移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
  6. 如权利要求1所述的云服务器自检方法，在若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组的步骤之后，还包括：
    检测所述伸缩组中的云服务器实例数量；
    若所述云服务器实例数量小于预置最小值，则复制正常的云服务器实例加入伸缩组；
    若所述云服务器实例数量大于预置最大值，则随机移除超出部分的云服务器实例。
  7. 如权利要求1所述的云服务器自检方法，在若所述负载异常的端口对 应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组的步骤之后，还包括：
    检测所述伸缩组云服务器实例负载情况，其中，所述负载包括内存利用率、cpu利用率、磁盘读写速率、流入流出速率、设备连接数；
    当所述伸缩组云服务器实例的实时负载平均值大于预设最大阈值时，根据预置伸缩规则，新增所述伸缩组的云服务器实例，其中，所述规则为新增或减少N个云服务器实例，N为整数；
    当伸缩组的实时负载平均值小于预设最小阈值时，根据预置伸缩规则，减少所述伸缩组的云服务器实例。
  8. 一种云服务器自检装置，所述云服务实例自检装置包括：
    监控模块，用于基于预置的间隔自检时间，监控当前是否达到自检时间；
    第一检测模块，用于若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口，其中，所述伸缩组为面向同一场景的云服务器实例集合；
    第二检测模块，用于若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应云服务器实例是否运行正常；
    移除模块，用于若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制正常的云服务器实例加入伸缩组。
  9. 如权利要求8所述的云服务器自检装置，所述第一检测模块包括：
    统计单元，用于若当前达到自检时间，则通过负载均衡设备统计所述伸缩组内端口的负载情况，其中，所述负载包括分发流量、流入流出速率；
    第一判断单元，用于判断所述伸缩组内端口的负载是否低于或高于预置负载阈值区间；
    第一确定单元，用于若所述伸缩组内存在端口的负载低于或高于预置负载阈值区间，则所述端口负载异常；
    第二确定单元，用于若所述伸缩组内端口的负载均在预置负载阈值区间内，则所述伸缩组内的端口均负载正常。
  10. 如权利要求8所述的云服务器自检装置，所述第二检测模块包括：
    第三确定单元，用于若检测到所述伸缩组存在负载异常的端口，则基于所述负载异常的端口，确定所述负载异常的端口对应的云服务器实例；
    第二统计单元，用于检测所述负载异常的端口对应的云服务器实例，并 统计当前检测时间；
    第二判断单元，用于若当前检测时间大于预置检测超时时间，则判定检测超时；
    第三统计单元，用于统计检测超时次数；
    第四确定单元，用于若连续出现检测超时，且超时次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
    第五确定单元，用于若未出现连续检测超时且次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行正常。
  11. 如权利要求8所述的云服务器自检装置，所述第二检测模块还包括：
    上报单元，用于若检测到所述伸缩组存在负载异常的端口，则上报统计所述负载异常的端口对应的云服务器实例，并统计所述负载异常的端口对应的云服务器实例异常次数；
    第六确定单元，用于若所述负载异常的端口对应的云服务器实例存在连续多次异常，且异常次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
    第七确定单元，用于若所述负载异常的端口对应的云服务器实例未存在连续多次异常，且异常次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行正常。
  12. 如权利要求8所述的云服务器自检装置，所述第一移除模块包括：
    配置单元，用于若所述负载异常的端口对应的云服务器实例运行不正常，则调用负载均衡设备配置控制接口；
    调整单元，用于通过所述负载均衡设备，将所述负载异常的端口对应的云服务器实例状态调整为关闭；
    连接单元，用于请求所述负载均衡设备停止向所述负载异常的端口对应的云服务器实例分发新的流量，并断开与所述负载异常的端口对应的云服务器实例的连接；
    移除单元，用于移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
  13. 如权利要求8所述的云服务器自检装置，所述云服务器自检装置还包括：
    第三检测模块，用于检测所述伸缩组中的云服务器实例数量；
    复制模块，用于若所述云服务器实例数量小于预置最小值，则复制正常 的云服务器实例加入伸缩组；
    第二移除模块，用于若所述云服务器实例数量大于预置最大值，则随机移除超出部分的云服务器实例。
  14. 如权利要求8所述的云服务器自检装置，所述云服务器自检装置还包括：
    第四检测模块，用于检测所述伸缩组云服务器实例负载情况，其中，所述负载包括内存利用率、cpu利用率、磁盘读写速率、流入流出速率、设备连接数；
    新增模块，用于当所述伸缩组云服务器实例的实时负载平均值大于预设最大阈值时，根据预置伸缩规则，新增所述伸缩组的云服务器实例，其中，所述规则为新增或减少N个云服务器实例，N为整数；
    减少模块，用于当伸缩组的实时负载平均值小于预设最小阈值时，根据预置伸缩规则，减少所述伸缩组的云服务器实例。
  15. 一种云服务器自检设备，所述云服务器自检设备包括存储器、处理器以及存储在所述存储器上并可在所述处理器上运行的云服务器自检程序，所述云服务器自检程序被所述处理器执行时实现如下云服务器自检方法的步骤：
    基于预置的自检时间间隔，监控当前是否达到自检时间；
    若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口，其中，所述伸缩组为面向同一场景的云服务器实例集合；
    若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应云服务器实例是否运行正常；
    若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
  16. 如权利要求15所述的云服务器自检设备，所述云服务器自检程序被所述处理器执行时还实现如下云服务器自检方法的步骤：
    若当前达到自检时间，则通过负载均衡设备统计所述伸缩组内端口的负载情况；其中，所述负载包括分发流量、流入流出速率；
    判断所述伸缩组内端口的负载是否低于或高于预置负载阈值区间；
    若所述伸缩组内存在端口的负载低于或高于预置负载阈值区间，则所述端口负载异常；
    若所述伸缩组内端口的负载均在预置负载阈值区间内，则所述伸缩组内的端口均负载正常。
  17. 如权利要求15所述的云服务器自检设备，所述云服务器自检程序被所述处理器执行时还实现如下云服务器自检方法的步骤：
    若检测到所述伸缩组存在负载异常的端口，则基于所述负载异常的端口，确定所述负载异常的端口对应的云服务器实例；
    检测所述负载异常的端口对应的云服务器实例，并统计当前检测时间；
    若当前检测时间大于预置检测超时时间，则判定检测超时；
    统计检测超时次数；
    若连续出现检测超时，且超时次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
    若未出现连续检测超时且次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行正常。
  18. 如权利要求15所述的云服务器自检设备，所述云服务器自检程序被所述处理器执行时还实现如下云服务器自检方法的步骤：
    若检测到所述伸缩组存在负载异常的端口，则上报统计所述负载异常的端口对应的云服务器实例，并统计所述负载异常的端口对应的云服务器实例异常次数；
    若所述负载异常的端口对应的云服务器实例存在连续多次异常，且异常次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行不正常；
    若所述负载异常的端口对应的云服务器实例未存在连续多次异常，且异常次数达到预置次数阈值，则判定所述负载异常的端口对应的云服务器实例运行正常。
  19. 如权利要求15所述的云服务器自检设备，所述云服务器自检程序被所述处理器执行时还实现如下云服务器自检方法的步骤：
    若所述负载异常的端口对应的云服务器实例运行不正常，则调用负载均衡设备配置控制接口；
    通过所述负载均衡设备，将所述负载异常的端口对应的云服务器实例状态调整为关闭；
    请求所述负载均衡设备停止向所述负载异常的端口对应的云服务器实例分发新的流量，并断开与所述负载异常的端口对应的云服务器实例的连接；
    移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
  20. 一种非易失性计算机可读存储介质，所述计算机可读存储介质上存储有云服务器自检程序，所述云服务器自检程序被处理器执行时实现如下云服务器自检方法的步骤：
    基于预置的自检时间间隔，监控当前是否达到自检时间；
    若当前达到自检时间，则检测当前伸缩组内是否存在负载异常的端口，其中，所述伸缩组为面向同一场景的云服务器实例集合；
    若检测到所述伸缩组中存在所述负载异常的端口，则基于预置检测机制，检测所述负载异常的端口对应云服务器实例是否运行正常；
    若所述负载异常的端口对应的云服务器实例运行不正常，则移除所述负载异常的端口对应的云服务器实例，并复制运行正常的云服务器实例加入伸缩组。
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CN114048098A (zh) * 2021-11-15 2022-02-15 盐城金堤科技有限公司 异常服务实例的监控方法及其装置
CN114356567A (zh) * 2021-12-30 2022-04-15 阿里巴巴（中国）有限公司 一种slurm集群的伸缩方法、系统及设备
CN116302791A (zh) * 2023-01-11 2023-06-23 上海智能制造功能平台有限公司 一种基于时间策略的物流设备状态外部判断系统及方法

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