CN113037547A - Resource performance acquisition monitoring and warning system - Google Patents
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0604—Management of faults, events, alarms or notifications using filtering, e.g. reduction of information by using priority, element types, position or time
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0677—Localisation of faults
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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
- H04L43/04—Processing captured monitoring data, e.g. for logfile generation
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- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
- H04L43/0817—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45562—Creating, deleting, cloning virtual machine instances
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45595—Network integration; Enabling network access in virtual machine instances
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Abstract
The invention discloses a resource performance acquisition monitoring and warning system, and belongs to the technical field of performance acquisition monitoring. The resource performance acquisition monitoring and warning system comprises a data collection layer, a data extraction layer, a data display layer, a warning rule configuration layer, a warning generation layer and a user display layer. The data collection layer is used for collecting host data, system data and container data; the data extraction layer is used for normalizing and filtering the data acquired by the data collection layer; and the data display layer is used for uniformly displaying the data acquired by the data collection layer. The resource performance acquisition monitoring and warning system can timely and effectively know the current resource use condition, analyze the performance problem, quickly position and solve the problem when a fault occurs, and has good popularization and application values.
Description
Technical Field
The invention relates to the technical field of performance acquisition and monitoring, and particularly provides a resource performance acquisition monitoring and warning system.
Background
Under the condition that technologies such as cloud computing and big data are mature day by day, more and more service products are provided for users, and the demands of users for responding to the resource use condition of own resources, the operation condition and the health degree of services and timely receiving abnormal alarms are more and more urgent. Monitoring is used as a platform capable of performing three-dimensional monitoring on resources, and the gathering and displaying of monitoring alarms are very important.
Under the conditions of increasing service scale, increasing services and frequent changes, a series of problems are brought to the complex call link: how to quickly find the problem and how to judge the fault influence range. The monitoring is an integral part of the bottom infrastructure and is an indispensable part for guaranteeing the service stability of the production environment, the online problem is solved from discovery to positioning, the discovery and the positioning can be effectively covered by monitoring and alarming means, even the problem can be solved by means of fault self-healing and the like, and service development and operation and maintenance personnel can timely and effectively discover the abnormity of the service operation, thereby more efficiently troubleshooting and solving the problem. The Prometheus is an open-source monitoring framework, which completes data collection, data storage and alarm through different components, wherein the Prometheus server only provides data storage (time servers data), data processing (providing rich query syntax [ query, statistics, aggregation and the like ]), data exposes an http service interface to Prometheus for timed capture through a plurality of plug-ins (Prometheus is called exporters), and alarm is realized through Altermanger.
Disclosure of Invention
The technical task of the invention is to provide a resource performance acquisition monitoring and warning system which can effectively understand the current resource use condition in time, analyze the performance problem and quickly position and solve the problem when a fault occurs, aiming at the existing problems.
In order to achieve the purpose, the invention provides the following technical scheme:
a resource performance acquisition monitoring and warning system comprises a data collection layer, a data extraction layer, a data display layer, a warning rule configuration layer, a warning generation layer and a user display layer;
the data collection layer is used for collecting host data, system data and container data, and standardizing and storing the collected data;
the data extraction layer is used for normalizing and filtering the data acquired by the data collection layer and extracting the required data;
the data display layer is used for uniformly displaying the data acquired by the data collection layer;
the alarm rule configuration layer is used for setting alarm rules, alarm threshold values, alarm contact persons and alarm modes according to the data acquired by the data display layer;
the alarm generation layer is used for giving an alarm when the monitoring data reaches an alarm threshold value;
and the user display layer uniformly displays the monitoring statistical result and the alarm fault result.
And the alarm generation layer is used for alarming when the monitoring data reaches an alarm threshold value, and finally executing alarm actions through operations such as deduplication, inhibition and the like so as to support mails, slack, WeChat and webhook.
Preferably, the data collection layer builds a cluster according to service and resource requirements, the cluster is used as a monitoring target, an exporter and a cadvisor are installed in the cluster, and cluster performance data are obtained.
The exporter is a generic term for a type of data collection component of Prometheus, and is responsible for gathering data from a target and converting it into a format supported by Prometheus. cadvisor is a container monitoring tool developed by google, which is embedded into k8s as a monitoring component of k8 s.
The acquired cluster performance comprises resource data information such as a cpu, a memory, a disk, a network and the like.
Preferably, the data collection layer collects different monitoring indexes through an exporter and exposes the monitoring indexes through a data format supported by Prometheus, and the Prometheus periodically pulls data.
Preferably, the data collection layer collects container, Pod related performance index data via cadvisor and captures Prometheus via exposed metrics interface.
Preferably, the data collection layer collects performance index data of the host through a prometheus-node-exporter and captures the performance index data by prometheus through an exposed metrics interface.
Preferably, the data extraction layer normalizes and filters the data acquired by the data collection layer through an alarm rule language in a yaml file written at the time of deployment.
Preferably, in the data extraction layer, the Prometheus stores the collected data in a unified format through an exporter and stores the data into a self-contained time sequence database of the Prometheus, processes the received alarm according to the configuration file, sends the alarm to a graphical interface, and visually collects the data.
The data extraction layer relates to Prometheus building and installation, and the specific implementation operation comprises the following steps:
1) packaging the Prometheus images and putting the Prometheus images into a cluster image warehouse for later installation of Prometheus;
2) a user-defined name space is created in the constructed Kubernets cluster, and the name space is mainly used for storing a container operated by Prometheus;
3) allocating a cluster reading curve to the name space, wherein the cluster reading curve is used for resource related information of the API lake region cluster of Prometous through Kubernetes;
4) creating a ConfigMap in the namespace for storing some configurations of the Prometheus container and configurations of dynamically discovered pod and running services in the kubernets cluster;
5) creating Prometous in the Delpoyment model, passing the Prometous through a yaml file;
6) prometeus is connected, and Prometeus internal ports are mapped into external ports through a yaml file, so that the Kubernets cluster is automatically connected to Prometeus, namely Prometeus deployment is successful.
And the Prometeus server periodically pulls monitoring data from the configured exporters and locally stores the collected monitoring indexes.
Preferably, the data display layer is a web display interface, and the data display mode comprises a graph, a histogram and a pie state. By imaging the data, the operation and maintenance personnel can be helped to know the operation state and the operation trend of the host or the network within a period of time and can be used as the basis for the operation and maintenance personnel to check or solve problems.
Compared with the prior art, the resource performance acquisition monitoring and warning system has the following outstanding beneficial effects: the resource performance acquisition monitoring and warning system can timely and effectively know the current resource use condition of the system, analyze the performance problem, can quickly position and solve the problem when a fault occurs, and has good popularization and application values.
Drawings
FIG. 1 is a topological diagram of a resource performance collection monitoring and warning system according to the present invention.
Detailed Description
The resource performance collection monitoring and warning system of the present invention will be further described in detail with reference to the accompanying drawings and embodiments.
Examples
As shown in fig. 1, the resource performance collection monitoring and warning system of the present invention includes a data collection layer, a data extraction layer, a data display layer, a warning rule configuration layer, a warning generation layer, and a user display layer.
The data collection layer is used for collecting host data, system data and container data, and standardizing and storing the collected data. And the data collection layer builds a cluster according to the service and resource requirements, takes the cluster as a monitoring target, and installs an exporter and a cadvisor in the cluster to obtain cluster performance data. The acquired cluster performance comprises resource data information such as a cpu, a memory, a disk, a network and the like. Different monitoring indexes are collected through an exporter and are exposed through a data format supported by Prometheus, and the Prometheus regularly pulls data. Container, Pod related performance index data was collected via cadvisor and captured with Prometheus via exposed metrics interface. And collecting the performance index data of the host through a prometheus-node-exporter, and capturing the performance index data by prometheus through an exposed metrics interface.
The data extraction layer is mainly used for normalizing and filtering the data acquired by the data collection layer through an alarm rule language in a yaml file compiled during deployment, extracting the required data to the monitoring alarm module, and storing the collected data into a timing database of Prometheus through an exporter in a unified format. And processing the received alarm according to the configuration file, sending an alarm in a graphical interface, and visually acquiring data. The data extraction layer relates to Prometheus building and installation, and the specific implementation operation comprises the following steps:
1) packaging the Prometheus images and putting the Prometheus images into a cluster image warehouse for later installation of Prometheus;
2) a user-defined name space is created in the constructed Kubernets cluster, and the name space is mainly used for storing a container operated by Prometheus;
3) allocating a cluster reading curve to the name space, wherein the cluster reading curve is used for resource related information of the API lake region cluster of Prometous through Kubernetes;
4) creating a ConfigMap in the namespace for storing some configurations of the Prometheus container and configurations of dynamically discovered pod and running services in the kubernets cluster;
5) creating Prometous in the Delpoyment model, passing the Prometous through a yaml file;
6) prometeus is connected, and Prometeus internal ports are mapped into external ports through a yaml file, so that the Kubernets cluster is automatically connected to Prometeus, namely Prometeus deployment is successful.
And the Prometeus server periodically pulls monitoring data from the configured exporters and locally stores the collected monitoring indexes.
And the data display layer is used for uniformly displaying the data acquired by the data collection layer. The data display layer is a web display interface and mainly used for uniformly displaying the data acquired by the data collection layer, the display modes can be a curve graph, a bar graph, a cake state and the like, and the data are graphical, so that operation and maintenance personnel can be helped to know the operation state and the operation trend of a host or a network within a period of time and can be used as the basis for the operation and maintenance personnel to troubleshoot problems or solve the problems.
And the alarm rule configuration layer is used for setting alarm rules, alarm threshold values, alarm contact persons and alarm modes according to the data acquired by the data display layer.
And the alarm generation layer is used for alarming when the monitoring data reaches an alarm threshold value. If the monitoring data reaches the alarm threshold, Prometheus Server will send an alarm to the alarm module alert manager via HTTP. And the alarm is subjected to the operations of duplicate removal, suppression and the like by an alert manager, and finally an alarm action is executed, so that the mail, the sleep, the Welch and the webhook are supported at present.
The user display layer is a web display interface and is used for uniformly displaying the monitoring statistical result and the alarm fault result.
The above-described embodiments are merely preferred embodiments of the present invention, and general changes and substitutions by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention.
Claims (8)
1. A resource performance acquisition monitoring and warning system is characterized in that: the system comprises a data collection layer, a data extraction layer, a data display layer, an alarm rule configuration layer, an alarm generation layer and a user display layer;
the data collection layer is used for collecting host data, system data and container data, and standardizing and storing the collected data;
the data extraction layer is used for normalizing and filtering the data acquired by the data collection layer and extracting the required data;
the data display layer is used for uniformly displaying the data acquired by the data collection layer;
the alarm rule configuration layer is used for setting alarm rules, alarm threshold values, alarm contact persons and alarm modes according to the data acquired by the data display layer;
the alarm generation layer is used for giving an alarm when the monitoring data reaches an alarm threshold value;
and the user display layer uniformly displays the monitoring statistical result and the alarm fault result.
2. The resource performance acquisition monitoring and warning system of claim 1, wherein: and the data collection layer builds a cluster according to the service and resource requirements, takes the cluster as a monitoring target, and installs an exporter and a cadvisor in the cluster to obtain cluster performance data.
3. The resource performance acquisition monitoring and warning system of claim 2, wherein: the data collection layer collects different monitoring indexes through an exporter and exposes the monitoring indexes through a data format supported by Prometheus.
4. The resource performance acquisition monitoring and alert system according to claim 3, wherein: the data collection layer collects the performance index data related to the containers and the Pod through cadvisor and captures the data with Prometheus through an exposed metrics interface.
5. The resource performance acquisition monitoring and warning system of claim 4, wherein: and the data collection layer collects the performance index data of the host through a prometheus-node-exporter and captures the performance index data by using prometheus through an exposed metrics interface.
6. The resource performance acquisition monitoring and alert system according to claim 5, wherein: and the data extraction layer normalizes and filters the data acquired by the data collection layer through the alarm rule language in the yaml file written during deployment.
7. The resource performance acquisition monitoring and alert system according to claim 6, wherein: in the data extraction layer, the Prometheus stores the collected data in a unified format into a self-contained time sequence database of the Prometheus through an exporter, processes the received alarm according to the configuration file, sends the alarm in a graphical interface and visually collects the data.
8. The resource performance acquisition monitoring and alert system according to claim 7, wherein: the data display layer is a web display interface, and the data display mode comprises a curve graph, a bar chart and a cake state.
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