CN116010201A - Monitoring method, management equipment and computing system - Google Patents

Abstract

The application discloses a monitoring method, management equipment and a computing system, relates to the technical field of cloud primary monitoring, and is used for improving index monitoring efficiency. The method is applied to the management equipment of the computing cluster for monitoring and deploying the cloud primary service; the method comprises the following steps: displaying a first interface; the first interface comprises a selection inlet of candidate objects corresponding to the monitoring objects with the granularity respectively; receiving a first operation on a first interface, and determining at least one target monitoring object in the candidate objects; wherein the target monitoring object comprises any one or more of the following: a target computing device in the computing cluster, a target program in the target computing device, and a target variable in the target program; sending a monitoring request comprising the identification of the target monitoring object and the monitored target parameter to the computing equipment corresponding to at least one target monitoring object; the target parameters are used for indicating parameters related to the cloud primary service of the target monitoring object; target parameter information is received in response to the monitoring request.

Description

Monitoring method, management equipment and computing system

Technical Field

The present disclosure relates to the field of cloud computing technologies, and in particular, to a monitoring method, a management device, and a computing system.

Background

The stable operation of the business system is an important precondition for providing business services for users. Currently, maintenance and management of a service system are realized by monitoring various indexes of the service system. In a cloud-native scenario, a cluster is typically made up of multiple computing devices to provide business services to users. The resources of each computing device in the cluster are divided by adopting container technology, so that processes running in different containers are relatively independent. The method is beneficial to flexible deployment of the service, but greatly increases the monitoring difficulty of various indexes in the system.

Disclosure of Invention

The embodiment of the application provides a monitoring method, management equipment and a computing system, which are used for realizing various indexes of computing equipment in a monitoring cluster.

In order to achieve the above purpose, the embodiments of the present application adopt the following technical solutions:

in a first aspect, a monitoring method is provided and applied to a management device, wherein the management device is used for monitoring a computing cluster for deploying cloud native services, and the computing cluster comprises a plurality of computing devices; the method comprises the following steps: displaying a first interface; the first interface comprises a selection inlet of candidate objects corresponding to the monitoring objects with the granularity respectively; receiving a first operation of a selection portal for a candidate object in a first interface; determining at least one target monitoring object among the candidate objects in response to the first operation; wherein the target monitoring object comprises any one or more of the following: a target computing device in the computing cluster, a target program in the target computing device, and a target variable in the target program; sending a monitoring request to a computing device corresponding to at least one target monitoring object; the monitoring request comprises the identification of the target monitoring object and the monitored target parameter; the target parameters are used for indicating parameters related to the cloud primary service of the target monitoring object; and receiving target parameter information of the computing equipment corresponding to the target monitoring object in response to the monitoring request.

Because the current cluster resource relates to a plurality of computing devices and the cluster resource is added with a finer granularity dividing mode based on a container technology, the monitoring of various indexes in the cluster resource becomes difficult, the current cluster resource is usually monitored by a single computing device, the monitored index item cannot be flexibly changed, and the whole resource is monitored by quick positioning based on different granularities, so that the current monitoring efficiency is lower. In this regard, by adopting the method provided by the embodiment of the application, the management device can monitor various indexes in the cluster, wherein the user can adopt different monitoring granularities for the cluster resources to acquire corresponding data information, so that the monitoring effect of the user for the whole cluster resources is improved, and the running stability of the cloud primary service is further improved.

In one possible implementation, the plurality of granularity monitoring objects include: computing a device program and a variable; wherein the program comprises at least one of: pod, container, and process; the granularity is, in order from large to small, computing device, pod, container, process, and variable.

The possible implementation mode provides the granularity level of the monitoring of the management equipment, is beneficial to the user to acquire the index information of the monitoring objects with different granularities through the management equipment, and improves the monitoring effect.

In one possible implementation, before displaying the first interface, the method further includes: storing association relations among a plurality of granularity monitoring objects; in response to the first operation, determining at least one target monitoring object among the candidate objects, comprising: and responding to the first operation, and determining at least one target monitoring object from the candidate objects according to the association relation from large granularity to small granularity.

The possible implementation manner provides an implementation manner for selecting the target monitoring object based on the monitoring objects with multiple granularities, which is beneficial to a user to quickly locate the target monitoring object and improves the efficiency of determining the monitoring object.

In one possible implementation, before sending the monitoring request to the computing device corresponding to the at least one target monitoring object, the method further includes: displaying a second interface, wherein the second interface comprises operation options of operation parameters; receiving a second operation for an operation option of the operating parameter in the second interface; in response to the second operation, a target parameter is determined among the operating parameters.

In the possible implementation manner, the target parameters are determined through the operation of the user on the second interface, so that the scheme feasibility is improved.

In one possible implementation, the second interface further includes a modify operation option, and the method further includes: receiving a third operation of modifying the operation option for the operating parameter in the second interface; and in response to the third operation, sending a modification request to the target computing device, wherein the modification request comprises the operation parameters of the cloud primary service operated by the modified target monitoring object.

The possible implementation mode is beneficial to the user to modify the operation parameters, so that the index item of the system for monitoring the monitored object is flexibly adjusted, and the monitoring efficiency is improved.

In one possible implementation, the method further includes: converting the target parameter information into information identifiable by a visualization program, wherein the visualization program is used for converting the information identifiable by the visualization program into a visual image; a third interface is displayed, the third interface including a visual image.

The possible implementation mode is beneficial to providing more visual target parameter information for the user, so that the user experience is improved.

In one possible implementation, converting the target parameter information into information recognizable by the visualization program includes: mapping the target parameter information into readable labels and numerical values; and obtaining information which can be identified by the visualization program according to the label and the numerical value.

The possible implementation mode provides a processing procedure when the target parameter information is converted into the visual image, and improves the feasibility of the scheme.

In one possible implementation, the method further includes: and storing the target parameter information.

The possible implementation mode is helpful for counting historical operation parameters and is convenient for further analysis and processing of data.

In a second aspect, a management device is provided, including a processor, a display, and a communication interface, where the processor is connected to the communication interface and the display, respectively; a display for displaying the first interface; the first interface comprises a selection inlet of candidate objects corresponding to the monitoring objects with the granularity respectively; a communication interface for receiving a first operation for a selection portal of a candidate in the first interface; a processor for determining at least one target monitoring object among the candidate objects in response to the first operation; wherein the target monitoring object comprises any one or more of the following: a target computing device in the computing cluster, a target program in the target computing device, and a target variable in the target program; the communication interface is also used for sending a monitoring request to the computing equipment corresponding to the at least one target monitoring object; the monitoring request comprises the identification of the target monitoring object and the monitored target parameter; the target parameters are used for indicating parameters related to the cloud primary service of the target monitoring object; and the communication interface is also used for receiving target parameter information of the computing equipment corresponding to the target monitoring object in response to the monitoring request.

In a third aspect, a management device is provided, comprising functional units for performing any of the methods provided in the first aspect, the actions performed by the respective functional units being implemented by hardware or by hardware executing corresponding software implementations. For example, the management device may include: the device comprises a processing unit, a display unit and a communication unit. The display unit is used for displaying the first interface; the first interface comprises a selection inlet of candidate objects corresponding to the monitoring objects with the granularity respectively; a communication unit for receiving a first operation for a selection portal of a candidate object in a first interface; a processing unit for determining at least one target monitoring object among the candidate objects in response to the first operation; wherein the target monitoring object comprises any one or more of the following: a target computing device in the computing cluster, a target program in the target computing device, and a target variable in the target program; the communication unit is also used for sending a monitoring request to the computing equipment corresponding to the at least one target monitoring object; the monitoring request comprises the identification of the target monitoring object and the monitored target parameter; the target parameters are used for indicating parameters related to the cloud primary service of the target monitoring object; and the communication unit is also used for receiving the target parameter information of the computing equipment corresponding to the target monitoring object in response to the monitoring request.

In a fourth aspect, there is provided a management apparatus including: a processor and a memory. The processor is electrically connected to the memory for storing program instructions, the processor for executing the program instructions to cause the management device to perform any one of the methods provided in the first aspect.

In a fifth aspect, there is provided a computing system including a plurality of computing devices and the management device recited in the second to fourth aspects above; the computing device is used for running cloud native business; the management device is used for monitoring cloud native business related information running on the computing device, such as resource usage information of the computing device, pod, container, process and variables, and the like.

In a sixth aspect, there is provided a chip comprising: a processor and interface circuit; the interface circuit is used for receiving the code instruction and transmitting the code instruction to the processor; a processor for executing code instructions to perform any of the methods provided in the first aspect.

In a seventh aspect, there is provided a computer readable storage medium comprising computer executable instructions which, when run on a computer, cause the computer to perform any one of the methods provided in the first aspect.

In an eighth aspect, there is provided a computer program product comprising computer-executable instructions which, when run on a computer, cause the computer to perform any one of the methods provided in the first aspect.

Technical effects caused by any implementation manner of the second aspect to the eighth aspect may be referred to technical effects caused by corresponding implementation manners of the first aspect, and are not described herein.

Drawings

FIG. 1 is a schematic diagram of a system for implementing index monitoring based on a monitoring container;

FIG. 2 is a schematic diagram of a system for implementing index monitoring based on an index observation tool;

fig. 3 is a schematic view of a monitoring scenario provided in an embodiment of the present application;

fig. 4 is a schematic diagram of a communication device according to an embodiment of the present application;

fig. 5 is a schematic flow chart of a monitoring method according to an embodiment of the present application;

fig. 6 is a schematic diagram of a first interface for determining a target monitoring object according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a first interface for determining a target indicator according to an embodiment of the present disclosure;

FIG. 8 is a schematic diagram of a second interface for determining target indicators according to an embodiment of the present disclosure;

Fig. 9 is a schematic flow chart of a monitoring method according to an embodiment of the present application;

fig. 10 is a schematic diagram of mapping relation of original data according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of a management device according to an embodiment of the present application.

Detailed Description

In the description of the present application, "/" means "or" unless otherwise indicated, for example, a/B may mean a or B. "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. Furthermore, "at least one" means one or more, and "a plurality" means two or more. The terms "first," "second," and the like do not limit the number and order of execution, and the terms "first," "second," and the like do not necessarily differ.

In this application, the terms "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

Currently, a cloud native architecture is a mainstream architecture in cloud computing, has the characteristics of micro-service, continuous integration, containerization, developer operation and maintenance (development operations, devOps) and the like, and is beneficial to realizing flexible deployment of services based on resource environments with different granularities. For example, in a cloud native architecture, a cluster, typically made up of multiple computing devices, carries business services. Resources of the plurality of computing devices are partitioned based on container technology to obtain a plurality of independent operating environments for carrying one or more processes, respectively. Specifically, the container technology realizes isolation between processes by using control groups (abbreviated as cgroups) and namespaces (namespaces) mechanisms of Linux kernels. Where cgroup is used to represent a container resource (e.g., central processing unit (central processing unit, CPU) resource, memory, etc.). The command space mechanism is used to distinguish the same variable names in different operating environments. Based on the above manner, each container is enabled to have independent computing resources for running of the process, and conflicts resulting from the existence of the same variables between containers are avoided based on a namespace mechanism.

It is to be appreciated that computing devices to which embodiments of the present application relate include, but are not limited to, electronic devices having computing capabilities, such as servers, computers, and the like.

Currently, in order to realize cluster resource management, a kubernetes open source platform (hereinafter referred to as k8 s) is mainly adopted to schedule and schedule containers. Where a pod is the smallest deployable unit in k8s in which a pod runs, a pod may include one or more pods, which may also be referred to as a pod set. Thus, the cluster resources managed by k8s may be partitioned at different granularities based on computing device level, pod level, container level, and process level.

In the operation process of the business system, the monitoring of various indexes in the business system is the key for maintaining the stability of the business system. The index item is used for reflecting the operation condition of the service system from different dimensions. For example, the index items include operation parameters such as CPU occupancy rate, memory occupancy, operation time length, and the like. In the Linux system, the process management tool can monitor the index items described above and describe the state of the system process. For example, the top tool can reflect the dynamic information of the system process, that is, the state of the system process is updated in time along with the running of the current system process. For another example, a static snapshot of a process executed in the past by the system is reflected by the ps tool, that is, the state of the process corresponding to the query time is displayed.

However, the cluster resources carrying the cloud primary service divide each computing device resource in the cluster by adopting the container technology to form an independent running environment, so that the running condition of the granularity of the container cannot be obtained by a process detection tool commonly used in the system. In this regard, a monitoring method is currently proposed, which is to implement monitoring of the operation condition of each service container in the pod by adding parallel monitoring containers in the pod. As shown in fig. 1, a pod with a monitoring container may perform index monitoring for each business container in the pod. In this way, the monitoring container can be flexibly deployed according to the monitoring requirement of the user based on the service container. For example, in fig. 1, in pod1, a monitoring container 1 is deployed for business containers 1 and 2. In pod2, a monitoring container 2 is deployed for business container 3. In pod3, the undeployed monitoring container performs index monitoring on the service container 3, so that the method is not limited to the deployment form of the monitoring container in pod. Wherein, a plurality of monitoring containers can be deployed in one pod for monitoring different service containers in the pod.

The monitoring mode is a monitoring strategy adopted by a sensu monitoring tool provided in k8s, specifically, the sensu runs a monitoring container in a side car mode in k8s, and the required index items are collected through communication of the monitoring container and each service container in the same pod.

In the above monitoring manner, since the monitoring container operates under the user-state resource, the kernel resource cannot be accessed to obtain the index monitoring based on the process granularity. The smallest monitoring unit of sensu is therefore the container. In addition, by adopting the mode, a user is required to attach the monitoring container to the running pod of the monitoring object to realize monitoring, so that each pod occupies certain resources, one or more monitoring containers are deployed according to the requirement of the user, and the monitoring efficiency is low.

Another monitoring method is currently proposed, and a program in an index observation tool set (instrktor-gadget) is called to access kernel resources, so as to obtain the running condition of a process in a container. Specifically, the index observation tool set is an extended berkeley packet filter (extended Berkeley packet filter, eBPF) framework implementation based on a Linux kernel to access kernel resources. The index observation tool set can monitor more index items, for example, acquire information such as Input Output (IO) flow of a disk, network connection time delay, file opening objects and the like. As shown in fig. 2, a user invokes a monitoring instruction for a certain index in the index observation tool set, and the instruction accesses kernel resources through the eBPF framework to acquire corresponding monitoring information and feeds the corresponding monitoring information back to the user.

In this monitoring approach, a set of index observation tools is stored in each computing device, and index item monitoring is performed based on user needs. However, in the cloud native scene, based on cluster resources formed by multiple computing devices, quick call of instructions cannot be realized according to user requirements.

In this regard, the present application proposes a monitoring method, applied to a management device, where the management device communicates with a plurality of computing devices deployed with cloud native services, and is configured to respond to an index monitoring request based on different granularity by a user, and locate the corresponding computing device, and obtain data information corresponding to a corresponding index item to feed back to the user, thereby facilitating flexible obtaining of an operation condition of the computing device in a cluster, and improving efficiency of index monitoring through unified management of the plurality of computing devices.

As shown in fig. 3, a schematic view of a scenario for index monitoring provided in the present application includes a management device for managing a plurality of computing devices (computing device 1, computing devices 2, … …, and computing device n, where n is an integer greater than 1) in a cluster. Each computing device comprises one or more pod, each pod comprises one or more containers, a process in each container runs cloud native business, and each computing device further comprises an observation program set, wherein the observation program set is used for calling corresponding observation programs for different index items and obtaining corresponding data information. Illustratively, the computing device reads data from the kernel by observing a trace point or piling point of the program in the kernel, such as kprobe, trace point, etc. According to the monitoring method provided by the embodiment of the application, the management equipment sends the index monitoring request to the target computing equipment in the plurality of computing equipment, the target computing equipment calls the related monitoring instruction based on the target parameter requested by the index monitoring request, the target parameter information is obtained, the target parameter information is fed back to the management equipment, and the management equipment feeds back to the user after receiving the target parameter information, so that the index monitoring of different monitoring granularities of the plurality of computing equipment in the cluster resource is realized under the scene of the cloud primary service, and the monitoring efficiency of the whole cluster resource is improved.

The management device to which the monitoring method provided by the embodiment of the present application is applied may execute the method provided by the present application for an independent communication device. The specific form of the communication device is not limited in the present application, and may be a terminal or a server. The terminal may be a mobile phone, an augmented reality (augmented reality, AR) device, a Virtual Reality (VR) device, a tablet, a notebook, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (personal digital assistant, PDA), and the like. The server may be a physical or logical server.

In a hardware implementation, the above-mentioned communication device may implement corresponding functions through the communication device as shown in fig. 4. As shown in fig. 4, a hardware structure of a communication device 40 according to an embodiment of the present application is shown.

The communication device 40 shown in fig. 4 may include: a processor 401, a memory 402, a communication interface 403, a bus 404, and a display 405. The processor 401, the memory 402, the communication interface 403, and the display 405 may be connected by a bus 404.

The processor 401 is a control center of the communication device 40, and may be a general-purpose CPU, another general-purpose processor, or the like. Wherein the general purpose processor may be a microprocessor or any conventional processor or the like.

As one example, processor 401 may include one or more CPUs, such as CPU 0 and CPU1 shown in fig. 4.

Memory 402 may be, but is not limited to, read-only memory (ROM) or other type of static storage device that can store static information and instructions, random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, as well as electrically erasable programmable read-only memory (EEPROM), magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

In one possible implementation, the memory 402 may exist independent of the processor 401. The memory 402 may be coupled to the processor 401 via a bus 404 for storing data, instructions or program code. The monitoring method provided in the embodiment of the present application can be implemented when the processor 401 calls and executes instructions or program codes stored in the memory 402.

In another possible implementation, the memory 402 may also be integrated with the processor 401.

A communication interface 403 for connecting the communication device 40 with other devices via a communication network, which may be ethernet, a radio access network (radio access network, RAN), a wireless local area network (wireless local area networks, WLAN), etc. The communication interface 403 may include a receiving unit for receiving data and a transmitting unit for transmitting data.

Bus 404 may be an industry standard architecture (industry standard architecture, ISA) bus, an external device interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.

A display 405 for displaying images, videos, and the like. The display includes a display screen, a display panel, and the like. The display panel may employ a liquid crystal display (liquid crystal display, LCD), an organic light-emitting diode (OLED), an active-matrix organic light emitting diode (AMOLED), a flexible light-emitting diode (flex), a mini, a Micro-OLED, a quantum dot light-emitting diode (quantum dot light emitting diodes, QLED), or the like. In some embodiments, communication device 40 may include 1 or N displays, N being a positive integer greater than 1. In the embodiment of the application, the display can display the monitored target parameter information and receive the selection operation or the input operation of the user on the monitored object and the monitored parameter.

It should be noted that the structure shown in fig. 4 does not constitute a limitation of the communication device 40, and that the communication device 40 may comprise more or less components than shown in fig. 4, or may combine certain components, or may be arranged in different components.

Fig. 5 is a schematic flow chart of a monitoring method according to an embodiment of the present application. The method is applied to the management equipment of a plurality of computing equipment for monitoring and deploying the cloud primary service, and comprises the steps of S501-S505.

S501, displaying a first interface.

The first interface comprises candidate object selection entries corresponding to the monitoring objects with the granularity respectively.

Wherein, the monitoring object of a plurality of granularities includes: computing devices, programs, and variables. The program comprises at least one of the following: pod, container, process. The granularity is, in order from large to small, computing device, pod, container, process, and variable.

It can be understood that, in the embodiment provided in the application, the current service system includes a plurality of computing devices carrying cloud native services, and in accordance with the division of computing resources in the service system, a corresponding monitoring service is provided. Specifically, the monitoring granularity includes a computing device level, a pod level, a container level, a process level, and a variable level. The target monitoring object can be determined to be a specific monitoring object under a certain monitoring granularity based on each monitoring granularity.

Exemplary, as shown in fig. 6, a schematic diagram of a first interface is provided in an embodiment of the present application. The first interface comprises a selection entry based on candidate objects corresponding to the monitoring objects with different granularities. In the first interface shown in fig. 6, an identification indicating the individual monitoring granularity, such as a computing device, a container group pod, a container, and a process, is contained. Correspondingly, each monitoring granularity includes one or more candidates.

S502, a first operation of a selection entry for a candidate object in a first interface is received.

Wherein the first operation is for determining a target monitor object in the selection entry of the candidate object.

Illustratively, in FIG. 6, each monitoring granularity corresponds to a selection entry for one candidate object that is used by the user to select among one or more candidate objects at that monitoring granularity. For example, in FIG. 6, by clicking on the entry, a list of candidates for each monitoring granularity is displayed, the list of candidates being made up of the identity of one or more candidates. For example, the candidate object list corresponding to the computing device includes a computing device a, a computing device b and a computing device c. Similarly, the candidate object list to which pod corresponds may include pod a, pod b, and pod c.

S503, in response to the first operation, determining a target monitoring object in the candidate objects; wherein the target monitoring object comprises any one or more of the following: a target computing device in a computing cluster, a target program in the target computing device, and a target variable in the target program.

When the target monitoring object is a target computing device in the computing cluster, the index monitoring request is used for performing index monitoring based on the computing device level granularity, and the target index acquired by the management device is the running condition of the target computing device in the process of running the cloud primary service, such as the data information of the CPU (central processing unit) utilization rate, the memory occupation condition and the like of the whole resource of the target computing device.

When the target program is a pod, the target monitoring object is a pod in target computing devices in the multiple computing devices, the index monitoring request is used for monitoring based on the granularity of the pod level, and the index monitoring request is used for requesting that the acquired target index is data information such as the CPU utilization rate, the memory occupation condition and the like of the pod in the target computing device.

For example, when the target program is a container or a process, the target index monitoring request is used for monitoring based on container-level or process-level granularity, wherein the requested target index is a running condition of a certain container or a certain process in the target computing device.

Illustratively, the monitoring granularity also includes a variation level. The target monitoring object is a certain variable in the target program. The process running in the computing device may include a plurality of variables, and the index monitoring request may be detected based on a variable level granularity, where the index monitoring request is used to request that the obtained target index is data information such as CPU usage rate, memory occupation condition, and the like of the variables in the computing device.

In combination with the above step S501 and step S502, in one possible implementation manner, the management device stores the identifiers of the multiple computing devices in the service system, and the association relationships between the multiple computing devices and the program. As shown in table 1 below, an example of an association between a computing device and a program.

Table 1, examples of associations between computing devices and programs

In one example, the candidate object list corresponding to the computing device shown in fig. 6 includes a computing device a, a computing device b, and a computing device c, the candidate object list corresponding to the pod includes pod a1, pod a2, pod b, and pod c, and the candidate object list corresponding to the container includes container a1, container a2, container a3, container b1, container b2, and container c.

In another example, in connection with the example of table 1, the candidate object list corresponding to the computing device shown in fig. 6 includes a computing device a, a computing device b, and a computing device c, and the candidate object list corresponding to the pod includes pod a1 and pod a2 based on the selection of the candidate object list in the computing device by the user and the association relationship between the program and the computing device in table 1, for example, when the user selects the computing device a, the candidate object list corresponding to the pod is determined to include pod a1 and pod a2 based on the association relationship between the computing device a and the program. Similarly, when the user selects pod a1, the candidate object list corresponding to the container includes container a1 and container a2.

It can be appreciated that the presentation manner of the candidate object identifier in the first interface described based on the above example is helpful to improve convenience of interaction with the user.

It should be noted that the above description of the first interface element included in the first interface is only taken as an example, and in the practical application process, the first interface element may be further optimized, for example, in another example, different first interface elements are displayed on different interfaces according to the monitoring granularity selected by the user step by step, when the user selects the computing device a to jump to the pod and the corresponding candidate object list thereof, the user selects based on the candidate object list in the interface, which is not limited in this application.

Note that, in the first interface shown in fig. 6, the identifier of the candidate object is displayed in a form of a drop-down menu, and in practical application, the identifier may also be displayed in other forms, which is not limited in this application. In addition, the first interface may further include more or fewer interface elements, for example, a process-level candidate object list, so as to enable the user to perform operations in the first interface to determine the monitoring object, which is not limited in this application.

In another example, the user selects the candidate object step by step according to the monitoring granularity, and takes the candidate object with the smallest monitoring granularity as the target monitoring object. As shown in fig. 7 (a), after the user selects the computing device a among the candidate objects of the computing device based on fig. 6, further selects pod a1 among the pods; in fig. 7 (b), the container a1 is selected based on the container a1 and the container a2 corresponding to the pod a1, and at this time, the container a1 is the identification of the target monitoring object. If in the example shown in fig. 6, the user selects computing device a and does not select a finer-grained pod, the identification of the target monitoring object is computing device a.

It should be noted that, the first operation is related to the control operable in the first interface, and may be flexibly adjusted according to the control deployed in the first interface, which is not limited in this application.

Optionally, in response to the first operation, the management device determines at least one target monitoring object. Wherein the at least one target monitoring object may be a monitoring object at different monitoring granularities. Illustratively, in fig. 6 or 7, a user selects one or more computing devices, or one or more programs; alternatively, the user selects one or more computing devices, and one or more programs. That is, the target monitoring object may be one or more monitoring objects with the same granularity, or may be a plurality of monitoring objects with different granularities.

S504, sending a monitoring request to the computing equipment corresponding to the at least one target monitoring object.

The monitoring request comprises identification of target parameter information and monitored target parameters, wherein the target parameters are used for indicating parameters related to cloud primary service of a target monitoring object.

In one example, the target parameter is any one or more of the index items described above.

Optionally, before the above step S504, the management apparatus determines the target parameter through the following steps S11 to S13.

S11, displaying a second interface; wherein the second interface includes operational options for the operational parameters.

Wherein the operation parameter is used for indicating resource use information in the service system. It is understood that the index items described above.

Exemplary, as shown in fig. 8, a schematic diagram of a second interface is provided in an embodiment of the present application. In the second interface shown in fig. 8, an identification of a plurality of operation parameters, such as CPU occupancy, memory usage, and operation time, is included. The second interface also includes operation options corresponding to each operation parameter, such as on and off. Specifically, when the user selects to open an operation option corresponding to a certain operation parameter, information indicating that the operation parameter needs to be acquired is displayed.

In fig. 8, the running time in the running parameters refers to the running time of the computing device or the running time of the program in the computing device after being started (not closed during the running time), for example, the running time of the computing device after being started, the running time of the pod after being created, and the like.

Optionally, the management device may also monitor the input/output of the disk, and the operation parameters further include the number of reads/writes per second (input/output operations per second, IOPS) for monitoring the disk performance. The management device may also perform network monitoring, and the operating parameters may also include network packet loss rate, network delay, etc.

In one possible implementation, the one or more operating parameters have a correspondence to the target monitoring object. Illustratively, when the target monitor object is a process, the operating parameters may include, in addition to one or more of the operating parameters described in the above examples, a process handle number. The information of the process handle number is monitored, so that the phenomenon that a certain process occupies excessive resources to influence the overall performance of the system is avoided. In this implementation manner, it is also understood that the management device determines the second interface based on the first interface determination target monitoring object.

Further, when the plurality of target monitoring objects are determined in step S503, the second interfaces corresponding to the respective target monitoring objects may be determined according to the correspondence between the target monitoring objects and the operation parameters. For example, in fig. 6, where a user selects computing device a and computing device b, in response to the user's selection, the second interface includes an interface for the operating parameters corresponding to computing device a and an interface for the operating parameters corresponding to computing device b.

It will be appreciated that the number and form of the operating parameters included in the second interface described above are merely examples, and that in practical applications, more or fewer operating parameters may be included, as well as other options corresponding to the operating parameters, which the present application is not limited to.

S12, receiving a second operation of the operation options for one or more operation parameters in the second interface.

Specifically, the second operation is performed based on the specific form of the operation option. Such as a click, double click or input.

Illustratively, in FIG. 8, the operation options include on or off for the user to perform a second operation based on the needs of the observed metrics.

S13, in response to the second operation, determining target parameter information in one or more parameter information.

For example, in fig. 8, when the user selects "on" based on the "CPU occupancy rate", the management apparatus determines the "CPU occupancy rate" as the target parameter.

It will be appreciated that the target parameter may be one or more for meeting the monitoring needs of the user.

The above process of determining the target parameter may also be understood as turning on or off the monitoring function of one or more index items.

Through the steps S11-S13, the management device determines the target parameter by displaying the second interface for the user and receiving the operation of the user on the second interface.

When the target monitoring object is the target computing device, step S504 specifically includes: an address of the target computing device is determined from the locally stored computing device information, and a target parameter is sent based on the address. When the target monitoring object is a target program or a target variable, step S502 specifically includes: according to the association relation between the computing equipment and the program or the variable, determining the computing equipment corresponding to the target program based on the target program indicated by the target monitoring object; and sending a monitoring request to the computing device corresponding to the target program.

In one possible implementation, the management device stores an address of the computing device, and determines an address for sending the monitoring request based on an identifier of the computing device corresponding to the target monitoring object.

Based on the above steps S11-S13, the management device determines the target parameter for the target monitoring object and the monitored target parameter, and sends a monitoring request including the identifier of the target parameter to the computing device corresponding to the target monitoring object.

S505, receiving target parameter information of the computing equipment corresponding to the target monitoring object in response to the monitoring request.

The target parameter information is used for reflecting specific data information of target parameters of the target monitoring object in the corresponding computing equipment.

After the monitoring request is sent in step S504, the computing device corresponding to the target monitoring object receives the monitoring request, and obtains the target parameter information based on the monitoring request.

As shown in fig. 3, the management device sends a monitoring request to the computing device 1, and the computing device 1 invokes a corresponding program in the observation program set according to the identifier of the target parameter included in the monitoring request, acquires the target parameter information, and feeds back to the management device. The observation program set comprises observation programs corresponding to one or more operation parameters displayed on the second interface. The observation program is used for realizing access to the kernel based on the eBPF framework and acquiring corresponding data information.

Because the current service system comprises a plurality of computing devices and the resource of the service system is added with a finer granularity dividing mode based on a container technology, the monitoring of various indexes in the resource of the service system becomes difficult, currently, the monitored index items cannot be flexibly changed for the single computing device, and the rapid positioning cannot be realized for the whole resource based on the monitoring of different granularities, so that the current monitoring efficiency is lower. In contrast, by adopting the method provided by the embodiment of the application, the management equipment provides the operation platform, so that a user can complete monitoring of various indexes in the cluster through simple operation. The user can adopt different monitoring granularities aiming at cluster resources to acquire corresponding parameter information, so that the monitoring effect of the user on the resources of the service system is improved, and the running stability of the cloud primary service is further improved.

Optionally, after step S505, the method further includes: the management device stores the target parameter information.

Wherein the management device stores the target parameter information locally or in other devices or databases independent of the management device.

It will be appreciated that the user may need to obtain data information for a certain item of target parameter information over a period of time, instead of immediate data information, so as to perform analysis based on the obtained data information over the period of time, for example, obtain a highest value reached by the CPU occupancy rate in a preset period of time, and determine whether to adjust the service carried in the computing device based on the highest value.

Optionally, after the step S505, the method further includes: the management equipment displays the monitoring data; the monitoring data comprises target parameter information of a target monitoring object responded by the target computing device, or the monitoring data comprises target parameter information of the target monitoring object responded by the target computing device and identification of the computing device to which the target monitoring object belongs.

In one example, when the target monitoring object is a target computing device and the identifier of the target parameter information is a CPU occupancy rate, the monitoring data includes a value of the current CPU occupancy rate of the target computing device or further includes an identifier of the target computing device. When the target monitoring object is a pod and the identification of the target parameter information is the CPU occupancy rate, the monitoring data comprises the value of the CPU occupancy rate of the pod in the affiliated computing equipment. Or further includes an identification of the computing device to which the pod belongs.

In another example, when the target monitoring object is a target program, such as a container, and the identification of the target parameter information is the CPU occupancy rate, then the monitoring data includes a value of the CPU occupancy rate of the container in the computing device to which the container belongs; alternatively, the monitoring data also includes an identification of the pod to which the container belongs and an identification of the computing device. Similarly, when the target monitoring object is a process, the monitoring data includes target parameter information, and may further include an identifier of a container to which the process belongs, an identifier of a pod, and an identifier of a computing device.

By the method, the program or the computing equipment identifier with other monitoring granularity related to the target monitoring object is provided for the user, so that the user can conveniently check related information, and the monitoring intelligence is improved.

Optionally, the target parameter information received in step S505 is raw data obtained from the operating system kernel by the computing device of the target monitoring object, and after step S505, the method further includes: converting the target parameter information into information identifiable by a visualization program, wherein the visualization program is used for converting the information identifiable by the visualization program into a visual image; a third interface is displayed that includes the visual image.

It will be appreciated that when the target monitoring object is a target program, the corresponding observer program needs to access the kernel to acquire the required data. For example, the kernel records the operating parameters of the container via the cgroup information at the time of container creation. When the target program is pod, container or process, the running parameters can be obtained through the cgroup information.

The process of obtaining a visualized image by adopting a visualization program is called a visualization processing process, and the visualization processing refers to converting data information into a graph or an image based on an image processing technology. By way of example, the user obtains the disk IOPS within a period of time, and obtains a line graph of the change of the IOPS with time within the period of time through visualization processing, so that the user can intuitively know the trend of the change of the IOPS with time based on the line graph, and whether more or less resources are configured for the service carried by the computing device is determined.

It can be understood that by the mode, a user is facilitated to acquire more visual data information through the visual image, and the monitoring efficiency of the user is improved.

Specifically, the management apparatus obtains information identifiable by the visualization program through steps shown in fig. 9, including steps S901 to S904.

S901, judging whether the target parameter information is formatted data.

If yes, executing step S904 to output formatted data; if not, the process continues to step S902.

The formatted data refers to the data which can be identified by the visualization program.

It will be appreciated that the data acquired by the kernel is typically output in accordance with the data format specified by the observer program, and accordingly the data recipient needs to read in accordance with the specified data format to be converted into formatted data that can be identified by the visualizer program. When the visualization processing is carried out, the requirements of the visualization program on the data structure and the length of the character string are strict, and the data meeting the requirements of the visualization program is formatted data.

S902, mapping the target parameter information into readable labels and numerical values.

When the observation program obtains target parameter information of the target program in the kernel of the computing device, the information in the kernel is derived according to a preset rule. The preset rule may be in a data storage format of labels and values. A set of labels corresponds to a set of data with each set of data corresponding to one type of observation information. Correspondingly, the management equipment analyzes according to the preset rule to obtain readable labels and numerical values; the readable label and number are the target parameter information readable by the management device. Illustratively, as shown in FIG. 10, the raw data includes data set 1, data set 2, and data set 3. And mapping to obtain the pod ID, the container ID and the PID. The three labels respectively correspond to the respective numerical values, so that the needed data information is obtained based on the original data.

In one possible implementation, the observer program needs to trace data according to the data acquired in the kernel and call other programs in the kernel. For example, the kernel can directly obtain the PID, and the container ID can be obtained by tracing the corresponding cgroup information based on the PID, for example, the following method is adopted:

cat/proc/$PID/cgroup|awk-F'/''{print$5}'

in addition, PID-based can also be traced back to pod ID through moutinfo, for example, in the following manner:

cat/proc/$PID/mountinfo|grep"etc-hosts"|awk-F/{'print$6'}

by the method, more relevant information is traced, more relevant data are presented for a user for reference, and monitoring efficiency of cluster resources is improved.

S903, formatting the label and the numerical value.

Specifically, the label and data formatting process is to process data according to the data structure and the character string length specified by the visualization program, so as to obtain formatted data.

S904, outputting the formatted data.

It can be understood that in the above scheme, the management device obtains the operation parameters in the corresponding computing device through the monitoring request of the user, and further, the obtained information can be processed to obtain the visual image through the visual program. In step S904, the management device outputs the formatted data to the device for performing the visualization process, and after this step, the method may further include receiving the visualized image and displaying the visualized image. For example, the management device obtains a profile of disk I/O traffic over a period of time.

It should be noted that, the visualization program may be directly deployed in the management device, and then step S904 may directly output and display the visualized image.

It can be appreciated that, in the above manner, based on the raw data acquired from the kernel, the raw data is converted into formatted data which can be read by the visualization program through formatting, so as to provide an implementation basis for implementing the data visualization process.

Optionally, the management device stores the formatted data and/or visual images locally, or in other devices or databases independent of the business system.

Optionally, after the step S505, the method further includes: receiving a first modification request sent by a target computing device, wherein the first modification request is used for adding or deleting computing devices or programs in a plurality of computing devices; in response to the first modification request, information of the computing device in the business system and an association relationship between the computing device and the program are updated.

It will be appreciated that the resources in the cluster may be changed, which needs to be updated in the management device, so that the target monitoring object of the index monitoring is accurately located during the index monitoring.

In one possible implementation, the target computing device includes an identification of the changed computing device or program in the first modification request, and sends the first modification request to the management device. For example, if a new container is added to the target computing device, the identifier of the changed computing device or program at least includes the identifier of the container, the identifier of the pod to which the container belongs, and the identifier of the computing device to which the container belongs. If an existing container is deleted from the target computing device, only the identity of the container may be included.

It can be understood that the management device maintains the association relationship between the target computing device and the target program, that is, the association relationship after the resources in the current cluster are divided based on different granularities, and if the new addition is performed, the management device can correspondingly update the association relationship based on the position where the new addition program is located. If deletion is performed, the management device may update the association relationship accordingly based on the identification of the deletion program.

It should be noted that, the above-mentioned modification procedure is taken as an example, and the actual application further includes adding and deleting computing devices, and the manner of updating the information of the computing devices in the service system by the management device is similar to the above-mentioned implementation manner, and the description will not be repeated.

Optionally, the management device updates the first interface element in the first interface, including updating a candidate list of the target computing device and/or a candidate list of the target program.

Optionally, the second interface further includes a modification operation option, and after step S505, the method further includes: receiving a third operation of modifying the operation option for the operating parameter in the second interface; and responding to a third operation, and sending a modification request to the target computing equipment, wherein the modification request comprises the operation parameters of the cloud native service operated by the modified target monitoring object.

It will be appreciated that in the example described above, the user may indicate the target indicator to be monitored by running the corresponding operation options of the parameters in the second interface. The second interface also comprises a modification operation option for modifying the operation parameters in the interface, and the modification operation option is used for adding or deleting the observation instruction of the current existing target index.

In one possible implementation, the modification operation option is used to receive input information of a user, where the input information may be an add or delete instruction for any one or more of the at least one instruction items of the target monitoring device. Responding to input information of a user, the management equipment sends the modification request to the computing equipment to which the target monitoring equipment belongs, and the computing equipment receiving the modification request generates a new observation program based on the eBPF framework and operates the new observation program to acquire corresponding data information and feed the corresponding data information back to the management equipment; alternatively, the computing device deletes the observation program corresponding to the instruction item in the observation instruction set sum. After the computing device completes the steps, the computing device feeds back response information to the management device, and the management device updates the operation parameters in the second interface based on the response information. Illustratively, bpftrace, bcc or other bpf procedures may add or subtract trace points (kprobe, trace point, etc.) through a classifier to achieve the goal of the dynamic switch corresponding plug-in to obtain the desired information.

Optionally, the management device updates the identification of the operating parameter and the corresponding operation option in the second interface.

It should be noted that, the above-mentioned modification operation parameters may be modified separately for the computing device to which the selected target monitoring object belongs, or may be modified for multiple computing devices in the service system, which is not limited in this application.

By the method, the observation program of each instruction in each computing device can be flexibly adjusted according to the user requirements, and the overall monitoring efficiency of the cluster resources is improved.

In the implementation process of the above scheme, the number of the target monitoring objects may be one or more, and the number of the target parameters may be one or more, and the target parameters of different target monitoring objects may be the same or different, which is not limited.

The foregoing description of the embodiments of the present application has been presented primarily from a method perspective. It will be appreciated that the management device, in order to implement the above-described functions, includes at least one of a hardware structure and a software module for performing the respective functions. Those of skill in the art will readily appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The embodiment of the application may divide the functional units of the management device according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated in one processing unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice.

Fig. 11 shows a possible structural diagram of the management apparatus involved in the above-described embodiment in the case where respective functional blocks are divided with corresponding respective functions. As shown in fig. 11, the management apparatus 110 includes a display unit 1101, a communication unit 1102, and a processing unit 1103.

A display unit 1101 for displaying a first interface; the first interface comprises candidate object selection entries corresponding to the monitoring objects with the granularity respectively.

A communication unit 1102 is configured to receive a first operation for a selection entry of a candidate object in a first interface.

A processing unit 1103 for determining at least one target monitoring object among the candidate objects in response to the first operation; wherein the target monitoring object comprises any one or more of the following: a target computing device in a computing cluster, a target program in the target computing device, and a target variable in the target program.

The communication unit 1102 is further configured to send a monitoring request to a computing device corresponding to at least one target monitoring object; the monitoring request comprises the identification of the target monitoring object and the monitored target parameter; the target parameters are used for indicating parameters related to the cloud primary service of the target monitoring object.

The communication unit 1102 is further configured to receive target parameter information of a computing device corresponding to the target monitoring object in response to the monitoring request.

In one example, the plurality of granularity monitoring objects includes: computing a device program and a variable; wherein the program comprises at least one of: pod, container, and process; the granularity is, in order from large to small, computing device, pod, container, process, and variable.

In one example, the management apparatus 110 further includes a storage unit 1104 for storing association relations between the plurality of granularity monitoring objects; the processing unit 1103 is specifically configured to determine, in response to the first operation, at least one target monitoring object from among the candidate objects according to the association relationship from large to small according to granularity.

In one example, the display unit 1101 is further configured to display a second interface, where the second interface includes operation options of the operation parameters; a communication unit 1102 further for receiving a second operation for an operation option of the operation parameter in the second interface; the processing unit 1103 is further configured to determine a target parameter among the operation parameters in response to the second operation.

In one example, the second interface further includes an operation option modification, and the communication unit 1102 is further configured to receive a third operation of the operation option for the operation parameter in the second interface; the communication unit 1102 is further configured to send, in response to the third operation, a modification request to the target computing device, where the modification request includes an operation parameter of the modified target monitoring object to operate the cloud native service.

In one example, the processing unit 1103 is further configured to convert the target parameter information into information identifiable by a visualization program, where the visualization program is configured to convert the information identifiable by the visualization program into a visual image; the display unit 1101 is further configured to display a third interface, where the third interface includes a visual image.

In one example, the processing unit 1103 is specifically configured to map the target parameter information into readable labels and values; and obtaining information which can be identified by the visualization program according to the label and the numerical value.

In one example, the storage unit 1104 is further configured to store target parameter information.

In one example, the storage unit 1104 is further configured to store computer-executable instructions, and other units in the management device may perform corresponding actions according to the computer-executable instructions stored in the storage unit 1104.

For a specific description of the above alternative modes, reference may be made to the foregoing method embodiments, and details are not repeated here. In addition, any explanation and description of the beneficial effects of the management device 110 provided above may refer to the corresponding method embodiments described above, and will not be repeated.

The embodiment of the application also provides a management device, referring to the hardware structure schematic diagram of the communication device shown in fig. 4, where the management device includes: the device comprises a processor, a display and a communication interface, wherein the processor is respectively connected with the communication interface and the display. A display for displaying the first interface; the first interface comprises a selection inlet of candidate objects corresponding to the monitoring objects with the granularity respectively; a communication interface for receiving a first operation for a selection portal of a candidate in the first interface; a processor for determining at least one target monitoring object among the candidate objects in response to the first operation; wherein the target monitoring object comprises any one or more of the following: a target computing device in the computing cluster, a target program in the target computing device, and a target variable in the target program; the communication interface is also used for sending a monitoring request to the computing equipment corresponding to the at least one target monitoring object; the monitoring request comprises the identification of the target monitoring object and the monitored target parameter; the target parameters are used for indicating parameters related to the cloud primary service of the target monitoring object; and the communication interface is also used for receiving target parameter information of the computing equipment corresponding to the target monitoring object in response to the monitoring request.

The management device also includes a memory. Wherein the memory may contain computer program code. The processor is configured to execute the computer program code stored in the memory, thereby implementing the method provided by the embodiments of the present application.

In implementation, the steps in the method provided in this embodiment may be implemented by an integrated logic circuit managing hardware in a processor of the device or by instructions in the form of software. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution.

The present embodiments also provide a computer readable storage medium having stored thereon a computer program which, when run on a computer, causes the computer to perform the method performed by any of the management devices provided above.

For the explanation of the relevant content and the description of the beneficial effects in any of the above-mentioned computer-readable storage media, reference may be made to the above-mentioned corresponding embodiments, and the description thereof will not be repeated here.

The embodiment of the application also provides a chip. The chip has integrated therein a control circuit and one or more ports for implementing the functions of the management device described above. Optionally, the functions supported by the chip may be referred to above, and will not be described herein. Those of ordinary skill in the art will appreciate that all or a portion of the steps implementing the above-described embodiments may be implemented by a program to instruct associated hardware. The program may be stored in a computer readable storage medium. The above-mentioned storage medium may be a read-only memory, a random access memory, or the like. The processing unit or processor may be a central processing unit, a general purpose processor, an application specific integrated circuit (application specific integrated circuit, ASIC), a microprocessor (digital signal processor, DSP), a field programmable gate array (field programmable gate array, FPGA) or other programmable logic device, transistor logic device, hardware components, or any combination thereof.

Embodiments of the present application also provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform any of the methods of the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, a website, computer, server, or data center via a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices including one or more servers, data centers, etc. that can be integrated with the media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

It should be noted that the above-mentioned devices for storing computer instructions or computer programs, such as, but not limited to, the above-mentioned memories, computer-readable storage media, communication chips, and the like, provided in the embodiments of the present application all have non-volatility (non-transparency).

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, a website, computer, server, or data center via a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices including one or more servers, data centers, etc. that can be integrated with the media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.

Although the present application has been described herein in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a review of the figures, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Although the present application has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are merely exemplary illustrations of the present application as defined in the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the present application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.

Claims (10)

1. The monitoring method is applied to a management device, wherein the management device is used for monitoring a plurality of computing devices in a computing cluster for deploying cloud native services;

the method comprises the following steps:

displaying a first interface; the first interface comprises candidate object selection inlets corresponding to the monitoring objects with the granularity respectively;

receiving a first operation of a selection portal for the candidate object in the first interface;

determining at least one target monitoring object among the candidate objects in response to the first operation; wherein the target monitoring object comprises any one or more of the following: a target computing device in the computing cluster, a target program in the target computing device, and a target variable in the target program;

sending a monitoring request to the computing equipment corresponding to the at least one target monitoring object; the monitoring request comprises the identification of the target monitoring object and the monitored target parameter; the target parameter is used for indicating parameters related to the cloud primary service of the target monitoring object;

and receiving target parameter information of the computing equipment corresponding to the target monitoring object in response to the monitoring request.

2. The method of claim 1, wherein the plurality of granularity monitoring objects comprises: computing a device program and a variable; wherein the program comprises at least one of: pod, container, and process; the granularity is sequentially from big to small, and is a computing device, a pod, a container, a process and a variable.

3. The method of claim 2, wherein prior to said displaying the first interface, the method further comprises: storing the association relation among the monitoring objects with the granularity;

the determining, in response to the first operation, at least one target monitoring object among the candidate objects includes:

and responding to the first operation, and determining the at least one target monitoring object from the candidate objects according to the association relation from large granularity to small granularity.

4. A method according to any one of claims 1 to 3, wherein before said sending a monitoring request to a computing device to which said at least one target monitoring object corresponds, the method further comprises:

displaying a second interface, wherein the second interface comprises operation options of operation parameters;

receiving a second operation for an operation option of the operating parameter in the second interface;

In response to the second operation, the target parameter is determined among the operating parameters.

5. The method of claim 4, further comprising modifying an operational option in the second interface, the method further comprising:

receiving a third operation of modifying an operation option for the operating parameter in the second interface;

and responding to the third operation, and sending a modification request to the target computing device, wherein the modification request comprises the modified operation parameters of the cloud native service operated by the target monitoring object.

6. The method according to any one of claims 1 to 5, further comprising:

converting the target parameter information into information identifiable by a visualization program, wherein the visualization program is used for converting the information identifiable by the visualization program into a visual image;

and displaying a third interface, wherein the third interface comprises the visual image.

7. The method of claim 5, wherein said converting said target parameter information into information recognizable by a visualization program comprises:

mapping the target parameter information into readable labels and numerical values;

And obtaining the identifiable information of the visualization program according to the label and the numerical value.

8. The method according to any one of claims 1 to 7, further comprising: and storing the target parameter information.

9. A management device comprising a memory and a processor, the memory being electrically connected to the processor; wherein the memory is configured to store program instructions, and the processor is configured to execute the program instructions to cause the management device to perform the monitoring method of any one of claims 1-8.

10. A computing system comprising a plurality of computing devices and the management device of claim 9; wherein the plurality of computing devices are to run cloud native business; the management device is used for monitoring running information of cloud native business on the plurality of computing devices.

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