CN117176727A - Cloud native application protection system, method, equipment and medium based on bandwidth control - Google Patents
Cloud native application protection system, method, equipment and medium based on bandwidth control Download PDFInfo
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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
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Abstract
The application discloses a cloud native application protection system, method, equipment and medium based on bandwidth control, which mainly relate to the technical field of cloud native applications and are used for solving the problems of rough bandwidth control and insufficient granularity of the cloud native application caused by the lack of combination of dynamic bandwidth setting and static management in the existing bandwidth control method. Comprising the following steps: the data acquisition module is used for acquiring application data of the cloud native application, marking events and storing the events; the flow model module is used for obtaining a real-time network bandwidth prediction model; a plurality of cluster data; respectively performing deep learning on the clustered data to obtain a plurality of event-triggered network bandwidth prediction models; the bandwidth control module is used for obtaining the predicted network bandwidth through the real-time network bandwidth prediction model; obtaining a predicted network bandwidth; generating an execution instruction corresponding to the predicted network bandwidth; and the bandwidth execution module is used for adjusting the current network bandwidth of the cloud native application to be the predicted network bandwidth.
Description
Technical Field
The application relates to the technical field of cloud native application, in particular to a cloud native application protection system, method, equipment and medium based on bandwidth control.
Background
After the cloud native technology is used, a developer does not need to consider the technical realization of the bottom layer, the elasticity and the distributed advantages of the cloud platform can be fully exerted, and quick deployment, on-demand expansion, delivery without shutdown and the like are realized.
With the development and application of cloud native applications, a technical scheme for observing the flow of cloud workload (such as a container) is layered endlessly. The flow collection monitoring based on ebpf and the flow limiting scheme based on EDT (Earliest Departure Time) are comparatively characterized. These schemes provide a useful flow management and control means for cloud native application security boundary protection. In addition, as an important function of cloud native application performance management, application performance indexes (Metric), log acquisition (Logging), link tracking (tracking) and other means are adopted to realize the acquisition of the application performance indexes, and the observability indexes related to the cloud native application are provided.
However, the above method (1) is often applied to workload level in terms of traffic observation and flow limitation, and bandwidth setting is static and preset, and is often not good in terms of traffic prediction management, traffic dynamic management, bursty traffic processing and the like. (2) The bandwidth management basically does not consider the actual resource use and the demand change of the cloud primary application, and the performance management index of the application cannot be well utilized.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a cloud native application protection system, a method, equipment and a medium based on bandwidth control, which are used for solving the problems of rough bandwidth control and insufficient granularity of the cloud native application caused by the lack of dynamic bandwidth setting and static management combination of the existing bandwidth control method.
In a first aspect, the present application provides a cloud native application protection system based on bandwidth control, the system comprising: the data acquisition module is used for acquiring application data of the cloud native application, marking events and storing the events; wherein, the application data at least comprises: application index data and flow transceiving data, wherein the flow transceiving data comprises network bandwidth data; the flow model module is used for carrying out association learning on index data and flow receiving and transmitting data in the application data in a preset time period so as to obtain a real-time network bandwidth prediction model; clustering the application data according to the labeling event to obtain a plurality of clustering data; respectively performing deep learning on the clustered data to obtain a plurality of event-triggered network bandwidth prediction models; the bandwidth control module is used for obtaining the predicted network bandwidth through the real-time network bandwidth prediction model and the application data; when an event triggering instruction is received, determining a corresponding event triggering network bandwidth prediction model according to an event, and further obtaining a predicted network bandwidth corresponding to application data; generating an execution instruction corresponding to the predicted network bandwidth; and the bandwidth execution module is used for adjusting the current network bandwidth of the cloud native application into the predicted network bandwidth according to the execution instruction and the preset flow limiting algorithm.
Further, the data acquisition module comprises a tracking embedded point software library and a network flow agent library; the embedded point tracking software library is used for acquiring application index data of the cloud primary application; and the network flow agent library is used for collecting flow receiving and transmitting data of the network interface of the corresponding work load of the cloud native application.
Further, the data acquisition module comprises an event labeling unit, wherein the event labeling unit is used for determining an event corresponding to the application data through a semantic analysis algorithm, and further adding the event serving as labeling data to the application data.
Further, the bandwidth control module comprises an event triggering unit; the event triggering unit is used for generating an event triggering instruction when the event is triggered.
Further, the application index data includes at least: CPU usage data, memory usage data, service TPS data, service latency data, and application health status; the traffic transceiving data at least comprises: cloud application information data, network card information, network 5-tuple data, uplink and downlink rates, ping time delay, disconnection rate, internet connection success rate and network bandwidth.
In a second aspect, the present application provides a method for protecting a cloud native application based on bandwidth control, the method comprising: collecting and labeling events for application data of the cloud native application and storing the events; wherein, the application data at least comprises: application index data and flow transceiving data, wherein the flow transceiving data comprises network bandwidth data; performing association learning on index data and flow receiving and transmitting data in application data in a preset time period to obtain a real-time network bandwidth prediction model; clustering the application data according to the labeling event to obtain a plurality of clustering data; respectively performing deep learning on the clustered data to obtain a plurality of event-triggered network bandwidth prediction models; acquiring a predicted network bandwidth through a real-time network bandwidth prediction model and application data; when an event triggering instruction is received, determining a corresponding event triggering network bandwidth prediction model according to an event, and further obtaining a predicted network bandwidth corresponding to application data; generating an execution instruction corresponding to the predicted network bandwidth; and according to the execution instruction and a preset flow limiting algorithm, adjusting the current network bandwidth of the cloud native application to be the predicted network bandwidth.
Further, the method for collecting and storing the application data of the cloud native application specifically comprises the following steps: determining an event corresponding to the application data through a semantic analysis algorithm, and further adding the event serving as marking data to the application data to finish event marking.
Further characterized by generating an event trigger instruction when the event is triggered.
In a third aspect, the present application provides a cloud native application protection device based on bandwidth control, the device comprising: a processor; and a memory having executable code stored thereon that, when executed, causes the processor to perform a bandwidth control-based cloud native application protection method as in any of the above.
In a fourth aspect, the present application provides a non-volatile computer storage medium having stored thereon computer instructions which, when executed, implement a bandwidth control based cloud native application protection method as in any of the above.
As will be appreciated by those skilled in the art, the present application has at least the following beneficial effects:
the application realizes the effective collection of data by collecting application data (application index data and flow receiving and transmitting data) and labeling and storing events; through association learning (deep learning), the combination of bandwidth dynamic setting (flow receiving and transmitting data) and static management (application index data) is realized, and the dynamic property, predictability and fineness of bandwidth control are improved. The real-time network bandwidth prediction model is updated in real time through real-time association learning of application data in the last time (preset time period), so that the real-time network bandwidth prediction model has good fit to the latest data. In the face of an emergency (labeling event), the application data corresponding to the emergency (labeling event) is collected in advance, a deep learning algorithm is trained, an event-triggered network bandwidth prediction model is obtained, and data can be better processed when the emergency is faced, namely, the cloud primary application actual resource use and demand change are considered, and prediction accuracy is improved.
Drawings
Some embodiments of the present disclosure are described below with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram of an internal structure of a cloud native application protection system based on bandwidth control according to an embodiment of the present application.
Fig. 2 is a flowchart of a cloud native application protection method based on bandwidth control according to an embodiment of the present application.
Fig. 3 is a schematic diagram of an internal structure of a cloud native application protection device based on bandwidth control according to an embodiment of the present application.
Detailed Description
It should be understood by those skilled in the art that the embodiments described below are only preferred embodiments of the present disclosure, and do not represent that the present disclosure can be realized only by the preferred embodiments, which are merely for explaining the technical principles of the present disclosure, not for limiting the scope of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments that may be obtained by one of ordinary skill in the art without inventive effort shall still fall within the scope of the present disclosure.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The following describes the technical scheme provided by the embodiment of the application in detail through the attached drawings.
Fig. 1 is a cloud native application protection system based on bandwidth control according to an embodiment of the present application. As shown in fig. 1, the system provided by the embodiment of the present application mainly includes:
the system collects and stores application data of the cloud native application through the data acquisition module 110.
It should be noted that, the data acquisition module 110 is any feasible device or apparatus capable of performing data processing. In the present application, the data acquisition module 110 transmits the application data to the traffic model module 120 and simultaneously to the cloud application bandwidth control module 130.
The application data includes at least: application index data and traffic transceiving data. The application index data includes at least: CPU usage data, memory usage data, service TPS data, service latency data, and application health status; the traffic transceiving data at least comprises: cloud application information data, network card information, network 5-tuple data, uplink and downlink rates, ping time delay, disconnection rate, internet connection success rate and network bandwidth.
The process of collection may be specifically implemented by the tracking embedded point software library 111 and the network traffic agent library 112 in the data acquisition module 110.
Specifically, the tracking embedded point software library 111 collects application index data of the cloud native application; the network traffic agent library 112 collects traffic transceiving data of the cloud native application corresponding to the workload network interface. It should be noted that, the tracking embedded point software library 111 is a multi-language supported software library package, and collects application indexes of cloud native in a non-invasive manner (such as a sidecar or an agent); the network flow agent library 112 is a lightweight lib library and agent, which operates inside a host machine or a workload, collects flow transceiving conditions of a corresponding workload network interface, and combines application information operated by the workload to realize flow observability of cloud native applications.
The event annotation process in the data acquisition module 110 may be specifically: through the event labeling unit 113 in the data acquisition module 110, the event corresponding to the application data is determined through a semantic analysis algorithm, and then the event is added to the application data as labeling data.
In addition, the specific content of the event marked in the event marking can be determined by a person skilled in the art according to the actual situation. The event may be a period of time, such as a weekend; the event may be a temperature range, such as greater than 40 degrees celsius, or the like.
The system carries out association learning on index data and flow receiving and transmitting data in application data in a preset time period through a flow model module 120 so as to obtain a real-time network bandwidth prediction model; clustering the application data according to the labeling event to obtain a plurality of clustering data; and respectively performing deep learning on the clustered data to obtain a plurality of event-triggered network bandwidth prediction models.
It should be noted that, the preset time period may be a week or a month last to the current time, and the obtained real-time network bandwidth prediction model has a better prediction effect on the current application data by selecting the application data of the last period to perform the association learning. In addition, the associative learning may be implemented by a deep neural network algorithm. Deep learning may also be achieved by deep neural network algorithms.
The bandwidth control module 130 in the system obtains the predicted network bandwidth through the real-time network bandwidth prediction model and the application data; when an event triggering instruction is received, determining a corresponding event triggering network bandwidth prediction model according to an event, and further obtaining a predicted network bandwidth corresponding to application data; and generating an execution instruction corresponding to the predicted network bandwidth.
It should be noted that, the bandwidth control module 130 may apply a policy decision system for bandwidth control for the cloud. And switching between event-triggered network bandwidth prediction models or between the event-triggered network bandwidth prediction models and the real-time network bandwidth prediction models by using the network bandwidth prediction models and taking the configurable triggering event as a triggering point.
The acquisition scheme of the event triggering instruction may specifically be: by an event triggering unit 131 in the bandwidth control module 130; detecting each preset event, and generating an event triggering instruction when the event is triggered.
The bandwidth execution module 140 is configured to adjust a current network bandwidth of the cloud native application to a predicted network bandwidth according to the execution instruction and a preset traffic flow restriction algorithm.
It should be noted that, the bandwidth execution module 140 is a lightweight lib library and agent, or may be a traffic bandwidth management software extension package of the relevant virtual network device, which runs inside the host machine or the workload, and adopts a preset traffic flow limiting algorithm, which is an execution point for implementing the bandwidth limitation of the relevant workload specific network endpoint.
In addition, the embodiment of the application also provides a cloud native application protection method based on bandwidth control, as shown in fig. 2, and the method provided by the embodiment of the application mainly comprises the following steps:
and 210, collecting and labeling events for application data of the cloud native application and storing the events.
It should be noted that, the application data at least includes: application index data and traffic transceiving data. The application index data includes at least: CPU usage data, memory usage data, service TPS data, service latency data, and application health status; the traffic transceiving data at least comprises: cloud application information data, network card information, network 5-tuple data, uplink and downlink rates, ping time delay, disconnection rate, internet connection success rate and network bandwidth.
The method for collecting and storing the application data of the cloud native application comprises the following steps:
determining an event corresponding to the application data through a semantic analysis algorithm, and further adding the event serving as marking data to the application data to finish event marking.
Step 220, performing association learning on index data and flow receiving and transmitting data in application data in a preset time period to obtain a real-time network bandwidth prediction model; clustering the application data according to the labeling event to obtain a plurality of clustering data; and respectively performing deep learning on the clustered data to obtain a plurality of event-triggered network bandwidth prediction models.
Step 230, obtaining a predicted network bandwidth through a real-time network bandwidth prediction model and application data; when an event triggering instruction is received, determining a corresponding event triggering network bandwidth prediction model according to an event, and further obtaining a predicted network bandwidth corresponding to application data; and generating an execution instruction corresponding to the predicted network bandwidth.
When an event is triggered, an event trigger instruction is generated.
And step 240, according to the execution instruction and a preset flow limiting algorithm, the current network bandwidth of the cloud native application is adjusted to be the predicted network bandwidth.
The method embodiment of the application is based on the same inventive concept, and the embodiment of the application also provides a cloud native application protection device based on bandwidth control. As shown in fig. 3, the apparatus includes: a processor; and a memory having executable code stored thereon that, when executed, causes the processor to perform a bandwidth control-based cloud native application protection method as in the above embodiments.
Specifically, the server side collects and marks events on application data of the cloud native application and stores the events; wherein, the application data at least comprises: application index data and flow transceiving data, wherein the flow transceiving data comprises network bandwidth data; performing association learning on index data and flow receiving and transmitting data in application data in a preset time period to obtain a real-time network bandwidth prediction model; clustering the application data according to the labeling event to obtain a plurality of clustering data; respectively performing deep learning on the clustered data to obtain a plurality of event-triggered network bandwidth prediction models; acquiring a predicted network bandwidth through a real-time network bandwidth prediction model and application data; when an event triggering instruction is received, determining a corresponding event triggering network bandwidth prediction model according to an event, and further obtaining a predicted network bandwidth corresponding to application data; generating an execution instruction corresponding to the predicted network bandwidth; and according to the execution instruction and a preset flow limiting algorithm, adjusting the current network bandwidth of the cloud native application to be the predicted network bandwidth.
In addition, the embodiment of the application also provides a nonvolatile computer storage medium, on which executable instructions are stored, and when the executable instructions are executed, the cloud native application protection method based on bandwidth control is realized.
Thus far, the technical solution of the present disclosure has been described in connection with the foregoing embodiments, but it is easily understood by those skilled in the art that the protective scope of the present disclosure is not limited to only these specific embodiments. The technical solutions in the above embodiments may be split and combined by those skilled in the art without departing from the technical principles of the present disclosure, and equivalent modifications or substitutions may be made to related technical features, which all fall within the scope of the present disclosure.
Claims (10)
1. A cloud native application protection system based on bandwidth control, the system comprising:
the data acquisition module is used for acquiring application data of the cloud native application, marking events and storing the events; wherein, the application data at least comprises: application index data and flow transceiving data, wherein the flow transceiving data comprises network bandwidth data;
the flow model module is used for carrying out association learning on index data and flow receiving and transmitting data in the application data in a preset time period so as to obtain a real-time network bandwidth prediction model; clustering the application data according to the labeling event to obtain a plurality of clustering data; respectively performing deep learning on the clustered data to obtain a plurality of event-triggered network bandwidth prediction models;
the bandwidth control module is used for obtaining the predicted network bandwidth through the real-time network bandwidth prediction model and the application data; when an event triggering instruction is received, determining a corresponding event triggering network bandwidth prediction model according to an event, and further obtaining a predicted network bandwidth corresponding to application data; generating an execution instruction corresponding to the predicted network bandwidth;
and the bandwidth execution module is used for adjusting the current network bandwidth of the cloud native application into the predicted network bandwidth according to the execution instruction and the preset flow limiting algorithm.
2. The bandwidth control-based cloud native application protection system of claim 1, wherein the data acquisition module comprises a tracking embedded point software library and a network traffic agent library;
the embedded point tracking software library is used for acquiring application index data of the cloud primary application;
and the network flow agent library is used for collecting flow receiving and transmitting data of the network interface of the corresponding work load of the cloud native application.
3. The bandwidth control-based cloud native application protection system of claim 1, wherein the data acquisition module comprises an event annotation unit,
the event labeling unit is used for determining the event corresponding to the application data through a semantic analysis algorithm, and further adding the event serving as the labeling data to the application data.
4. The bandwidth control-based cloud native application protection system of claim 1, wherein the bandwidth control module comprises an event trigger unit;
the event triggering unit is used for generating an event triggering instruction when the event is triggered.
5. The bandwidth control-based cloud native application protection system of claim 1,
the application index data includes at least: CPU usage data, memory usage data, service TPS data, service latency data, and application health status;
the traffic transceiving data at least comprises: cloud application information data, network card information, network 5-tuple data, uplink and downlink rates, ping time delay, disconnection rate, internet connection success rate and network bandwidth.
6. A method for protecting a cloud native application based on bandwidth control, the method comprising:
collecting and labeling events for application data of the cloud native application and storing the events; wherein, the application data at least comprises: application index data and flow transceiving data, wherein the flow transceiving data comprises network bandwidth data;
performing association learning on index data and flow receiving and transmitting data in application data in a preset time period to obtain a real-time network bandwidth prediction model; clustering the application data according to the labeling event to obtain a plurality of clustering data; respectively performing deep learning on the clustered data to obtain a plurality of event-triggered network bandwidth prediction models;
acquiring a predicted network bandwidth through a real-time network bandwidth prediction model and application data; when an event triggering instruction is received, determining a corresponding event triggering network bandwidth prediction model according to an event, and further obtaining a predicted network bandwidth corresponding to application data; generating an execution instruction corresponding to the predicted network bandwidth;
and according to the execution instruction and a preset flow limiting algorithm, adjusting the current network bandwidth of the cloud native application to be the predicted network bandwidth.
7. The method for protecting the cloud native application based on the bandwidth control according to claim 6, wherein the method for collecting, event labeling and storing the application data of the cloud native application specifically comprises:
determining an event corresponding to the application data through a semantic analysis algorithm, and further adding the event serving as marking data to the application data to finish event marking.
8. The method of claim 6, wherein the method further comprises the step of,
when an event is triggered, an event trigger instruction is generated.
9. A cloud native application protection device based on bandwidth control, the device comprising:
a processor;
and a memory having executable code stored thereon that, when executed, causes the processor to perform a bandwidth control-based cloud native application protection method of any of claims 6-8.
10. A non-transitory computer storage medium having stored thereon computer instructions that, when executed, implement a bandwidth control-based cloud native application protection method according to any of claims 6-8.
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