CN116455781A - Method and system for realizing flow visualization based on flow analysis - Google Patents
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- 238000012800 visualization Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000005206 flow analysis Methods 0.000 title claims abstract description 15
- 230000002776 aggregation Effects 0.000 claims abstract description 36
- 238000004458 analytical method Methods 0.000 claims description 19
- 238000004220 aggregation Methods 0.000 claims description 7
- 230000004931 aggregating effect Effects 0.000 claims description 5
- 238000013079 data visualisation Methods 0.000 claims description 4
- 238000007794 visualization technique Methods 0.000 abstract 1
- 238000013144 data compression Methods 0.000 description 9
- 238000012545 processing Methods 0.000 description 7
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- 238000013473 artificial intelligence Methods 0.000 description 2
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- 230000002452 interceptive effect Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
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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
- H04L43/045—Processing captured monitoring data, e.g. for logfile generation for graphical visualisation of monitoring data
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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
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate
Abstract
The application relates to the technical field of cloud computing, in particular to a method and a system for realizing flow visualization based on flow analysis, wherein the method comprises the following steps: deploying a Flow aggregator program in the Kubernetes cluster and a Flow exporter program at each node; the Flow exporter program obtains the network Flow of the current node from the Flow table of the OVS and sends the network Flow to the Flow aggregation program; the Flow aggregation program aggregates and stores the network flows into a database; grafana takes the database as a data source to acquire data, and performs page drawing on the network flow through a Grafana plugin. The flow visualization method and device effectively solve the problem that flow visualization cannot be achieved conveniently.
Description
Technical Field
The application relates to the technical field of cloud computing, in particular to a method and a system for realizing flow visualization based on flow analysis.
Background
With the continuous development of cloud computing technology, more and more enterprises favor the cloud computing technology because of the advantages of rapidness, convenience and pay-per-view. With the continuous expansion of the service scale, the analysis requirements of the container network traffic are increasing, and in particular, the traffic needs to be visualized and presented to the user. The current implementation mode of flow visualization mainly comprises the steps of obtaining network flows from a network driver by using an ebpf technology through deep flow, and displaying data to a user through Grafana visualization after data aggregation.
However, when the deep flow uses ebfp technology, there is a requirement for kernel version of Linux, and it is inconvenient to perform system deployment for some older or stable systems, so that flow visualization cannot be realized more conveniently.
Disclosure of Invention
The application provides a method and a system for realizing flow visualization based on flow analysis, which can solve the problem that flow visualization cannot be realized conveniently. The application provides the following technical scheme:
in a first aspect, the present application provides a method for implementing traffic visualization based on traffic analysis, the method comprising:
deploying a Flow aggregator program in the Kubernetes cluster and a Flow exporter program at each node;
the Flow exporter program obtains the network Flow of the current node from the Flow table of the OVS and sends the network Flow to the Flow aggregation program;
the Flow aggregation program aggregates and stores the network flows into a database;
grafana takes the database as a data source to acquire data, and performs page drawing on the network flow through a Grafana plugin.
In a specific embodiment, the Flow exporter program obtains the network Flow of the current node from the Flow table of the OVS and sends it to the Flow aggregate program includes:
judging the network flow type and acquiring network flow basic information and a network strategy of a cluster;
and judging whether the network Flow information cache is empty, if so, writing the data acquired at the time into the cache, and if not, pushing the data acquired at the time into a Flow aggregation program.
In a specific embodiment, the determining the network flow type and obtaining the network flow basic information and the network policy of the cluster includes:
extracting a network flow according to the source IP address, the destination IP address, the source port and the destination port;
judging the type of the network flow according to src and dst;
acquiring basic information related to src and dst, wherein the basic information comprises a container name and a naming space;
and acquiring the network policy of the cluster according to the src and dst.
In a specific embodiment, if the buffer is not empty, the pushing the collected data to the Flow aggregator program further includes:
comparing the number of the packets of the current acquisition network flow with the number of the packets of the cache data;
judging whether the number of the packets is changed or not, if the number of the packets is unchanged, directly ending, and if the number of the packets is changed, pushing the network Flow into a Flow aggregation program.
In a specific embodiment, the Flow aggregator program further includes, after aggregating and storing the network flows into a database:
and selecting whether an IPFIX exporter program needs to be configured, if so, configuring the IPFIX exporter program, wherein the IPFIX exporter program packages the data in the database by an IPFIX protocol and exposes the data to the outside, and if not, directly finishing.
In a specific embodiment, before the Flow exporter program sends the network Flow of the current node to the Flow aggregation program, the method further includes:
the network flows are classified according to their flow direction.
In a specific embodiment, the database is designated as the ClickHouse database.
In a second aspect, the present application provides a system for implementing flow visualization based on flow analysis, which adopts the following technical scheme:
a system for implementing flow visualization based on flow analysis, comprising:
the program deployment module is used for deploying the Flow aggregate program in the Kubernetes cluster and deploying the Flow exporter program at each node;
the data acquisition module is used for acquiring the network Flow of the current node from the Flow table of the OVS by the Flow exporter program and sending the network Flow to the Flow aggregation program;
the data aggregation module is used for aggregating the network Flow by the Flow aggregation program and storing the network Flow into a database;
and the data visualization module is used for acquiring data by using the database as a data source by Grafana and carrying out page drawing on the network flow through a Grafana plugin.
In a third aspect, the present application provides an electronic device comprising a processor and a memory; stored in the memory is a program that is loaded and executed by the processor to implement a method of implementing traffic visualization based on traffic analysis as claimed in any one of claims 1 to 7.
In a fourth aspect, the present application provides a computer readable storage medium having stored therein a program which when executed by a processor is adapted to carry out a method of carrying out flow visualization based on flow analysis as claimed in any one of claims 1 to 7.
In summary, the beneficial effects of the present application at least include: the whole cluster network flow is visualized, so that the network condition of the cluster is convenient to observe, and the operation and maintenance personnel can manage and aggregate the network flow of the whole cluster; users rise to the cluster level from the perspective of a single container of a single node, so that network flow data is durable, and historical network data can be easily checked; by exposing the network flows in IPFIX format, the generic protocol is more convenient for accessing any third party platform; the Grafana is used as a page display platform, so that a more universal page display system is adopted, secondary development is more convenient, and any other platform is convenient to access; the ClickHouse database is in column type storage, data compression is easier to carry out aiming at data storage, and each column selects a better data compression algorithm, so that the compression proportion of data is greatly improved, the data compression ratio is better, and the disk space is saved.
The method comprises the steps that a Flow aggregation program and a Flow exporter program are arranged in a Kubernetes cluster, the Flow exporter program is used for obtaining network flows of current nodes corresponding to each Flow exporter program from a Flow table of an OVS, then the Flow exporter program pushes the collected network flows to the Flow aggregation program, the Flow aggregation program is used for aggregating the network flows and storing the network flows into a Clickhouse database, finally Grafana takes the Clickhouse database as a data source to obtain network Flow data, and a Grafana plugin is used for carrying out page drawing on the network flows, so that the visualized operation of the network flows is realized; the problem that flow visualization cannot be achieved conveniently can be solved.
The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical means of the present application more clearly understood, it can be implemented according to the content of the specification, and the following detailed description of the preferred embodiments of the present application will be given with reference to the accompanying drawings.
Drawings
Fig. 1 is a flow chart of a method for implementing traffic visualization based on traffic analysis according to an embodiment of the present application.
Fig. 2 is an exemplary schematic diagram of a method for implementing traffic visualization based on traffic analysis according to an embodiment of the present application.
Fig. 3 is an exemplary schematic diagram of a method for implementing traffic visualization based on traffic analysis according to an embodiment of the present application.
Fig. 4 is a block diagram of a system for implementing traffic visualization based on traffic analysis according to one embodiment of the present application.
FIG. 5 is a block diagram of an electronic device implementing traffic visualization based on traffic analysis, provided in one embodiment of the present application.
Reference numerals: 410. a program deployment module; 420. a data acquisition module; 430. a data aggregation module; 440. and a data visualization module.
Detailed Description
The detailed description of the present application is further described in detail below with reference to the drawings and examples. The following examples are illustrative of the present application, but are not intended to limit the scope of the present application.
First, a description will be given of several terms involved in the present application.
Kubernetes is an open source system for automated deployment, expansion and management, and containerized applications. The system is designed by google and donated to CNCF.
OpenvSwitch, sometimes abbreviated as OVS, is an open source implementation of a distributed virtual multi-layer switch. The main purpose of OpenvSwitch is to provide a switching stack for a hardware virtualization environment while supporting a variety of protocols and standards used in computer networks.
Grafana: is a multi-platform open source analysis and interactive visualization Web application program. When connected to a supported data source, it provides charts, graphics, and alarms for the Web. There is also a licensed version Grafana Enterprise which has additional functionality that can be used as an account on a self-hosting installation or Grafana Labs cloud service. The expansion may be performed by a plug-in system. An end user may create a complex monitoring dashboard using an interactive query builder.
Flow Table is a set of policy Table entries for data forwarding of OpenFlow switches, indicating how the switches handle traffic, and all messages entering the switches are forwarded according to the Flow Table. The generation, maintenance and issuing of the flow table are realized by the controller.
Conntrack, a function of the linux kernel module, allows the kernel to track all network links, identifying all packets for each flow to be handled together in unison.
IPFIX (IP Flow Information Export) IP data flow information output, which is a standard protocol published by the IETF for flow information measurement in networks. The protocol is mainly used for unifying the statistical output standard and the output format of the IP data stream, and has strong expansibility. The IPFIX defined format is based on the cisco Netflow Version9 data output format, which allows IP information to be transferred from one exporter (export) to a Collector (Collector). IPFIX is thus a template-based format output protocol for data flow profiling.
ClickHouse: is a column database management system for online analysis, abbreviated CK, which was developed by Yandex in russia in 2016. Benefits of columnar storage: the statistical operations of aggregation, counting, summation and the like for columns are superior to the line storage; because the data types of a certain column are the same, the data compression is easier to carry out aiming at the data storage, and each column selects a better data compression algorithm, so that the compression proportion of the data is greatly improved; the data compression ratio is better, on one hand, the disk space is saved, and on the other hand, the cache has larger playing space.
Optionally, the method for implementing flow visualization based on flow analysis provided by each embodiment is used for illustration in an electronic device, where the electronic device is a terminal or a server, and the terminal may be a mobile phone, a computer, a tablet computer, a scanner, an electronic eye, a monitoring camera, etc., and the embodiment does not limit the type of the electronic device.
Referring to fig. 1, a flow chart of a method for implementing flow visualization based on flow analysis according to an embodiment of the present application is provided, where the method at least includes the following steps:
step 101, a Flow aggregator program and a Flow exporter program are deployed in a Kubernetes cluster.
Referring to fig. 2, the Flow aggregate program is first deployed in the Kubernetes cluster, while the Flow exporter program is deployed at each node. The Flow exporter program is used for periodically acquiring Flow table information, and the Flow aggregation program is used for aggregating network Flow information of the whole cluster.
And 102, acquiring the network Flow from a Flow table of the OVS by using a Flow exporter program.
Referring to fig. 3, after the program deployment, the Flow exporter program is used to obtain the network Flow of the current node corresponding to each Flow exporter program from the Flow table of the OVS. Specifically, firstly, network flows are extracted according to a source IP address, a destination IP address, a source port and a destination port, then the types of the network flows are judged according to src and dst, basic information related to the src and the dst is acquired, the basic information comprises a container name and a naming space, and finally network strategies of the clusters are acquired according to the src and the dst.
After the network flow information is obtained, judging whether the network flow information cache is empty, if so, writing the collected data into the cache, so that a user can rise to a cluster layer from the view angle of a single container of a single node, the network flow data is durable, and historical network data can be easily checked. If the cache is not empty, comparing the number of the packets of the network flow acquired currently with the number of the packets of the cache data, and judging whether the number of the packets is changed, wherein the cache data refers to the network flow data acquired last time, if the number of the packets is unchanged, the acquired network flow is proved to be meaningless, and then the network flow is ended directly. If the number of the packets changes, the acquired network Flow is proved to be meaningful, and the acquired network Flow is pushed to a Flow aggregation program.
It should be noted that before pushing the collected network flows into the Flow aggregator, the network flows need to be classified according to the Flow directions of the network flows, where the Flow directions of the network flows are seven flows from Pod to Pod, pod to node, or Pod to service.
Step 103, the network flows are aggregated and stored into a database using the Flow aggregation program.
After the Flow exporter program pushes the collected network flows to the Flow aggregation program, the Flow aggregation program is used to aggregate and store the network flows into the database. The database is designated as a ClickHouse database, the ClickHouse database is stored in a column mode, data compression is easier to conduct aiming at data storage, and each column selects a better data compression algorithm, so that compression proportion of data is greatly improved, data compression proportion is better, and therefore disk space is saved.
After the Flow aggregation program aggregates and stores the network Flow into the ClickHouse database, whether the IPFIX exporter program needs to be configured is selected, if so, the IPFIX exporter program is configured, and the IPFIX exporter program can encapsulate and expose the data in the ClickHouse database by an IPFIX protocol, and if not, the IPFIX exporter program directly ends. The IPFIX protocol is a popular general protocol, exposing network flows in IPFIX format, which is more convenient for accessing any third party platform.
And 104, performing page drawing on the network flow by using Grafana.
After the network flow is stored in the ClickHouse database, the Grafana takes the ClickHouse database as a data source to acquire network flow data, and the Grafana plugin is used for carrying out page drawing on the network flow, so that the visualized operation of the network flow is realized. Grafana is used as a page display platform, so that a more universal page display system is adopted, secondary development is more convenient, and any other platform is convenient to access.
In summary, referring to fig. 2 and fig. 3, by deploying a Flow aggregation program and a Flow exporter program in a Kubernetes cluster, using the Flow exporter program to obtain a network Flow of a current node corresponding to each Flow exporter program from a Flow table of an OVS, then the Flow exporter program pushes the collected network Flow to the Flow aggregation program, using the Flow aggregation program to aggregate the network Flow and store the aggregated network Flow in a Clickhouse database, finally Grafana uses the Clickhouse database as a data source to obtain network Flow data, and performs page drawing on the network Flow through a Grafana plugin, thereby realizing the visualization operation of the network Flow, visualizing the whole cluster network Flow, facilitating observation of the network condition of the cluster, and facilitating management and aggregation of the network Flow of the whole cluster by an operation staff; users can rise to the cluster level from the perspective of a single container of a single node, so that network flow data is durable, and historical network data can be easily checked; by exposing the network flows in IPFIX format, the generic protocol is more convenient for accessing any third party platform; grafana is used as a page display platform, so that a more universal page display system is adopted, secondary development is more convenient, and any other platform is convenient to access.
Fig. 4 is a block diagram of a system for implementing traffic visualization based on traffic analysis according to one embodiment of the present application. The device at least comprises the following modules:
a program deployment module 410 for deploying the Flow aggregate program in the Kubernetes cluster and deploying the Flow exporter program at each node.
The data acquisition module 420 is configured to acquire, by each Flow exporter program, a network Flow of the current node from a Flow table of the OVS and send the network Flow to the Flow aggregation program.
The data aggregation module 430 is configured to aggregate and store the network flows into the database by using the Flow aggregation program.
The data visualization module 440 is configured to obtain data from the Grafana by using the database as a data source, and perform page drawing on the network flow through the Grafana plugin.
For relevant details reference is made to the method embodiments described above.
Fig. 5 is a block diagram of an electronic device provided in one embodiment of the present application. The device comprises at least a processor 401 and a memory 402.
Processor 401 may include one or more processing cores such as: 4 core processors, 8 core processors, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 401 may also include a main processor, which is a processor for processing data in an awake state, also called a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 401 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 401 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.
Memory 402 may include one or more computer-readable storage media, which may be non-transitory. Memory 402 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 402 is used to store at least one instruction for execution by processor 401 to implement the method for traffic visualization based on traffic analysis provided by the method embodiments herein.
In some embodiments, the electronic device may further optionally include: a peripheral interface and at least one peripheral. The processor 401, memory 402, and peripheral interfaces may be connected by buses or signal lines. The individual peripheral devices may be connected to the peripheral device interface via buses, signal lines or circuit boards. Illustratively, peripheral devices include, but are not limited to: radio frequency circuitry, touch display screens, audio circuitry, and power supplies, among others.
Of course, the electronic device may also include fewer or more components, as the present embodiment is not limited in this regard.
Optionally, the application further provides a computer readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the method for implementing the flow visualization based on flow analysis in the above method embodiment.
Optionally, the application further provides a computer product, where the computer product includes a computer readable storage medium, where a program is stored, and the program is loaded and executed by a processor to implement the method for implementing the flow visualization based on flow analysis in the above method embodiment.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method for implementing flow visualization based on flow analysis, the method comprising:
deploying a Flow aggregator program in the Kubernetes cluster and a Flow exporter program at each node;
the Flow exporter program obtains the network Flow of the current node from the Flow table of the OVS and sends the network Flow to the Flow aggregation program;
the Flow aggregation program aggregates and stores the network flows into a database;
grafana takes the database as a data source to acquire data, and performs page drawing on the network flow through a Grafana plugin.
2. The method for implementing traffic visualization based on traffic analysis according to claim 1, wherein the Flow exporter program obtains the network Flow of the current node from the Flow table of the OVS and sends the network Flow to the Flow aggregator program comprises:
judging the network flow type and acquiring network flow basic information and a network strategy of a cluster;
and judging whether the network Flow information cache is empty, if so, writing the data acquired at the time into the cache, and if not, pushing the data acquired at the time into a Flow aggregation program.
3. The method for implementing traffic visualization based on traffic analysis according to claim 2, wherein the determining the network flow type and obtaining the network flow basic information and the network policy of the cluster comprises:
extracting a network flow according to the source IP address, the destination IP address, the source port and the destination port;
judging the type of the network flow according to src and dst;
acquiring basic information related to src and dst, wherein the basic information comprises a container name and a naming space;
and acquiring the network policy of the cluster according to the src and dst.
4. The method for implementing traffic visualization based on traffic analysis according to claim 2, wherein if the buffer is not empty, pushing the collected data to the Flow aggregator program further comprises:
comparing the number of the packets of the current acquisition network flow with the number of the packets of the cache data;
judging whether the number of the packets is changed or not, if the number of the packets is unchanged, directly ending, and if the number of the packets is changed, pushing the network Flow into a Flow aggregation program.
5. The method for implementing traffic visualization based on traffic analysis according to claim 1, wherein after the Flow aggregator aggregates and stores network flows into a database, the method further comprises:
and selecting whether an IPFIX exporter program needs to be configured, if so, configuring the IPFIX exporter program, wherein the IPFIX exporter program packages the data in the database by an IPFIX protocol and exposes the data to the outside, and if not, directly finishing.
6. The method for implementing traffic visualization based on traffic analysis according to claim 1, wherein before the Flow exporter program sends the network Flow of the current node to the Flow aggregate program, the method further comprises:
the network flows are classified according to their flow direction.
7. The method for implementing traffic visualization based on traffic analysis of claim 1, wherein the database is designated as a clickHouse database.
8. A system for implementing flow visualization based on flow analysis, comprising:
a program deployment module (410) for deploying a Flow aggregator program in the Kubernetes cluster and a Flow exporter program at each node;
a data acquisition module (420) for acquiring the network Flow of the current node from the Flow table of the OVS by the Flow exporter program and transmitting the network Flow to the Flow aggregation program;
the data aggregation module (430) is used for aggregating the network flows by the Flow aggregation program and storing the network flows into a database;
and the data visualization module (440) is used for the Grafana to acquire data by taking the database as a data source and carrying out page drawing on the network flow through the Grafana plugin.
9. An electronic device comprising a processor and a memory; stored in the memory is a program that is loaded and executed by the processor to implement a method of implementing traffic visualization based on traffic analysis as claimed in any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the storage medium has stored therein a program which, when executed by a processor, is adapted to carry out a method of carrying out a flow visualization based on a flow analysis according to any one of claims 1 to 7.
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| CN108848157A (en) * | 2018-06-12 | 2018-11-20 | 郑州云海信息技术有限公司 | A kind of method and apparatus of Kubernetes cluster container monitors |
| CN110928740A (en) * | 2018-09-20 | 2020-03-27 | 中国石油化工股份有限公司 | Centralized visualization method and system for operation and maintenance data of cloud computing center |
| US20200348984A1 (en) * | 2019-05-05 | 2020-11-05 | Mastercard International Incorporated | Control cluster for multi-cluster container environments |
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