CN117170816A - DPU-based containerized data acquisition method, system and deployment method - Google Patents
DPU-based containerized data acquisition method, system and deployment method Download PDFInfo
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Abstract
The application provides a DPU-based containerized data acquisition method, a DPU-based containerized data acquisition system and a DPU-based containerized data deployment method, wherein the DPU-based containerized data acquisition method comprises the following steps: each service data is collected in a container of a system on chip of the DPU, and data processing is carried out on each service data to obtain standard format data corresponding to each service data, wherein each service data comprises: software data of the host side and/or software and hardware data of the DPU; each of the standard format data is stored in a container of the system-on-chip to enable an external system to access the standard format data from the container. The application designs the data acquisition method in the system on chip which can independently run in the DPU by using the containerization technology, does not need to insert piles and embed points in an application program, can effectively improve the universality and reusability of the data acquisition method, and can effectively improve the comprehensiveness and reliability of service data acquisition in the host side and the DPU.
Description
Technical Field
The application relates to the technical field of DPUs, in particular to a method, a system and a deployment method for collecting containerized data based on a DPU.
Background
The data processor DPU (Data process unit) runs an independent operating system and has a plurality of different working modes, so that the data processor can be used as a common network card or can work cooperatively with a host operating system to finish a plurality of hardware unloading functions. The hardware acceleration function of the DPU is utilized, so that unloading capability can be provided for network, security and storage, the CPU at the host side can be reduced in load, and the data center can be energized. In order to ensure the running stability of the application programs in the host and the DPU, the service data of the host and the DPU need to be collected, and the running state of the application programs can be obtained by analyzing the collected data.
At present, because the forms of software functions on the DPU are different and the provided software and hardware functions are greatly different, the traditional data acquisition mode suitable for the host side cannot be directly multiplexed on the DPU, and the traditional data acquisition method needs to perform pile insertion and point burying in an original application program, so that the method is strong in invasiveness and not easy to modify. Therefore, there is a need to design a data collection method with strong universality and independence suitable for a DPU, so that the problems that the universality and the reusability of a traditional data collection system are poor, and service data in a host side and the DPU cannot be comprehensively collected can be solved.
Disclosure of Invention
In view of this, embodiments of the present application provide a DPU-based containerized data collection method, system, and deployment method that obviate or mitigate one or more disadvantages in the prior art.
One aspect of the present application provides a method for collecting containerized data based on a DPU, comprising:
collecting each service data in a container of a system on chip of the DPU, and carrying out data processing on each service data to obtain standard format data corresponding to each service data, wherein each service data comprises: software data of a host side and/or software and hardware data of the DPU;
storing each of the standard format data in a container of the system-on-chip to enable an external system to access the standard format data from the container.
In some embodiments of the present application, the collecting the respective service data in a container of a system on a chip of a DPU includes:
collecting software data of a host side and a DPU, the software data comprising: operating system data;
and, self-defining and collecting software and hardware data of the DPU, wherein the software and hardware data comprise: openvswitch soft switches offload data, network state data, and store offload state data.
In some embodiments of the present application, the processing the data of each service data to obtain standard format data corresponding to each service data includes:
the method comprises the steps that aggregation processing is carried out on each currently acquired service data in a container of a system-on-chip of a DPU;
filtering the aggregated service data based on a preset filtering mode, wherein the filtering mode comprises the following steps: removing duplication and/or filtering according to a preset label;
and carrying out format conversion on the filtered service data according to a preset standard format to obtain corresponding standard format data.
In some embodiments of the application, the storing each of the standard format data in a container of the system-on-chip to enable an external system to access the standard format data from the container includes:
according to the data types of the standard format data, storing the standard format data in a container of the system-on-chip respectively so that an external system can access the standard format data with different data types respectively;
wherein the data types include: log, telemetry data, and link tracking data;
the external system includes: an external log system for accessing the log, a telemetry monitoring and alert system for accessing the telemetry data, and a link tracking and performance monitoring system for accessing the link tracking data.
Another aspect of the present application provides a DPU-based containerized data acquisition system, comprising:
the data acquisition and processing module is used for acquiring each service data in a container of the system on chip of the DPU and processing the data of each service data to obtain standard format data corresponding to each service data, wherein each service data comprises: software data of a host side and/or software and hardware data of the DPU;
and the data output module is used for storing each standard format data in a container of the system-on-chip so as to enable an external system to access the standard format data from the container.
In some embodiments of the application, the data acquisition and processing module comprises:
a general-purpose operating system collector for collecting software data of a host side and a DPU, the software data comprising: operating system data;
the DPU custom collector is used for custom collecting software and hardware data of the DPU, wherein the software and hardware data comprise: openvswitch soft switch offload data, network state data, and store offload state data;
wherein, DPU custom collector includes:
an OVS data collector for collecting OpenvSwtch soft switch unloading data of the DPU, an NP data collector for collecting network state data of the DPU and an NVME storage half-unloading data collector for collecting storage unloading state data of the DPU.
In some embodiments of the application, the data acquisition and processing module comprises:
the data processing unit is used for carrying out aggregation processing on each currently acquired service data in a container of the system-on-chip of the DPU; filtering the aggregated service data based on a preset filtering mode, wherein the filtering mode comprises the following steps: removing duplication and/or filtering according to a preset label; and carrying out format conversion on the filtered service data according to a preset standard format to obtain corresponding standard format data.
In some embodiments of the application, the data output module includes:
the data provider is used for respectively storing the standard format data in the containers of the system-on-chip according to the data types of the standard format data so as to enable an external system to respectively access the standard format data with different data types; wherein the data types include: log, telemetry data, and link tracking data;
wherein the data provider comprises: a log provider for storing a log, a telemetry data provider for storing the telemetry data, and a link data trace provider for storing the link trace data;
The external system includes: an external log system for the log provider to access the log, a telemetry monitor alarm system for the telemetry data provider to access the telemetry data, and a link tracking and performance monitoring system for the link tracking provider to access the link tracking data.
A third aspect of the present application provides a deployment method of the DPU-based containerized data acquisition system, including:
setting a code warehouse corresponding to the DPU-based containerized data acquisition system in a source code management platform;
if a trigger instruction aiming at the code warehouse is received, compiling and packaging target codes based on the source code management platform to generate corresponding installation packages or images;
and sending the installation package to a system on chip of the DPU with the container running environment installed in advance, and installing and containerizing the installation package in the system on chip to form the containerized data acquisition system based on the DPU in the system on chip.
In some embodiments of the present application, compiling and packaging the object code based on the source code management platform to generate a corresponding installation package or image includes:
If the target code is in an offline installation mode, compiling and packaging the target code in an OBS mode to generate a corresponding offline installation package;
if the target code is in an online installation mode, compiling the target code in a container mirror image construction mode, and pushing the compiled container mirror image to a mirror image warehouse for pulling during online installation.
The application provides a DPU-based containerized data acquisition method, which is characterized in that each service data is acquired in a container of a system-on-chip of the DPU, and data processing is carried out on each service data to obtain standard format data corresponding to each service data, wherein each service data comprises the following components: software data of a host side and/or software and hardware data of the DPU; and storing each standard format data in a container of the system-on-chip so as to enable an external system to access the standard format data from the container, and designing a data acquisition method in the system-on-chip which can independently run in the DPU by using a containerization technology without inserting piles and embedding points in an application program, so that the universality and reusability of the data acquisition method can be effectively improved, and the comprehensiveness and reliability of service data acquisition in a host side and the DPU can be effectively improved.
Additional advantages, objects, and features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present application are not limited to the above-described specific ones, and that the above and other objects that can be achieved with the present application will be more clearly understood from the following detailed description.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and together with the description serve to explain the application. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the application. Corresponding parts in the drawings may be exaggerated, i.e. made larger relative to other parts in an exemplary device actually manufactured according to the present application, for convenience in showing and describing some parts of the present application. In the drawings:
fig. 1 is a schematic flow chart of a first method for collecting data based on a DPU in a container according to an embodiment of the present application.
Fig. 2 is a schematic flow chart of a second method for collecting data based on a container of a DPU according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a first architecture of a DPU-based containerized data acquisition system in accordance with one embodiment of the present application.
Fig. 4 is a schematic diagram of a second structure of a DPU-based containerized data acquisition system in an embodiment of the present application.
Fig. 5 is a schematic diagram of the architecture of a DPU-based containerized data acquisition system in an application example of the present application.
Fig. 6 is a schematic flow chart of a first method for deploying a DPU-based containerized data acquisition system in accordance with one embodiment of the present application.
Fig. 7 is a second flowchart of a deployment method of the DPU-based containerized data acquisition system according to an embodiment of the present application.
Fig. 8 is a schematic structural diagram of a deployment device of a DPU-based containerized data acquisition system in an embodiment of the present application.
Fig. 9 is a schematic flow chart of an implementation of a deployment method of a DPU-based containerized data acquisition system in an application example of the present application.
Detailed Description
The present application will be described in further detail with reference to the following embodiments and the accompanying drawings, in order to make the objects, technical solutions and advantages of the present application more apparent. The exemplary embodiments of the present application and the descriptions thereof are used herein to explain the present application, but are not intended to limit the application.
It should be noted here that, in order to avoid obscuring the present application due to unnecessary details, only structures and/or processing steps closely related to the solution according to the present application are shown in the drawings, while other details not greatly related to the present application are omitted.
It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
It is also noted herein that the term "coupled" may refer to not only a direct connection, but also an indirect connection in which an intermediate is present, unless otherwise specified.
Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.
The DPU is the third chip beyond the CPU and the GPU (graphics processor), which brings the popularization and digitization of computers, and the GPU brings the development of high-definition graphics images, games and artificial intelligence, and AI technology. The DPU is in the form of a hardware-accelerated FPGA or ASIC chip product. The DPU accesses the host operating system in the form of a board card (similar to a GPU card insertion) through PCIE interface technology. The DPU is a hardware product with high integration level, has complex functions and various use scenes, is matched with own software ecology for supporting the functions of complex scenes, for example, an openEuler can be adopted as a development platform on the basis, and a HADOS heterogeneous computing development platform is constructed for the DPU on the basis so as to fully release the capability of the DPU.
The existing mature Linux operating system such as RedHat, ubuntu builds an rpm and deb package management method, solves the problem of version dependence of the software package, and provides an excellent scheme for software distribution. However, the binary software distribution mode based on the operating system version is limited by the principle, and cannot be multiplexed before different operating system platforms, even cannot be shared between small versions of the same operating system, and deployment between software depends on complexity.
With the development of virtualization, containerization and cloud native technologies such as LXC, container technology Docker (an application program packaging operation technology), kubernetes and the like, the installation, deployment and distribution of software ecological software are changed qualitatively. The DPU is a brand new hardware platform, and although the DPU also runs a traditional operating system, many functions are limited by a system and a driver, and software is still managed and deployed in an installation package such as rpm or deb. Besides cloud native related functions based on a Kubernetes related Pod and hellm mode deployment, there are also container-based deployments in part.
On the DPU, there is currently no complete and unified software development platform and software deployment ecology. Although the package management mode and the software ecology of the existing mature operating system can be multiplexed, the traditional software deployment and distribution mode is not completely applicable to the software ecology of the DPU, and the DPU platform has unique characteristics by relying on the traditional operating system:
1) The hardware function is exposed to the application program and service access of the user in a mode of depending on the SDK/API;
2) The DPU function depends on the kernel of an operating system and the support of related drivers;
3) The DPU software is loose in installation and running modes, and has no mode for adapting to all scenes, namely an operating system UEFI safe starting control module, an operating system version customization module, a kernel module depending on specific hardware and an operating system, and an application layer SDK and an API module depending on the kernel module.
Besides the above, the cloud native application is packaged and managed in a helm manner, and the DPU software version is updated and updated by updating the OS version, updating the FPGA firmware and updating the version.
The reasons for the fact that the software version management, the software deployment and the upgrading of the DPU and the access of the host side unloading function provide a plurality of barriers are undoubtedly caused by the different forms of the software functions on the DPU and the large difference of the provided software and hardware functions, and the DPU does not have a unified service software deployment mode at present.
That is, since the forms of software functions on the DPU are different and the provided software functions are different greatly, the conventional data acquisition method applicable to the host side cannot be directly multiplexed on the DPU.
On the basis, the traditional data acquisition functional module needs to be inserted and buried in the original application program, is strong in invasiveness and is not easy to modify. Particularly, when the system is fused and interacted with other driving and application layer modules on the DPU, the development of data acquisition functions of different software and hardware modules is difficult and the deployment is dependent due to different running mechanisms, development languages and exposed interfaces of different applications and kernel programs.
Therefore, it is desirable to design a data acquisition method and system that can be applied to a DPU and has good versatility. Based on the above, in order to provide a data collection method with strong universality and independence suitable for a DPU, so that the problems that the universality and the reusability of a traditional data collection system are poor, service data in a host side and the DPU cannot be comprehensively collected and the like are solved, the embodiment of the application respectively provides a containerized data collection method based on the DPU, a containerized data collection system based on the DPU and used for executing the containerized data collection method based on the DPU, and a deployment method of the containerized data collection system based on the DPU, so that the universality and the reusability of the data collection method can be effectively improved, and the comprehensiveness and the reliability of service data collection in the host side and the DPU can be effectively improved.
The following examples are provided to illustrate the application in more detail.
Based on this, the embodiment of the present application provides a method for collecting data containerized by a DPU, which can be implemented by a containerized data collection system based on a DPU, referring to fig. 1, the method for collecting data containerized by a DPU specifically includes the following contents:
step 100: collecting each service data in a container of a system on chip of the DPU, and carrying out data processing on each service data to obtain standard format data corresponding to each service data, wherein each service data comprises: software data on the host side and/or software and hardware data of the DPU.
In one or more embodiments of the application, the importance of the DPU-based containerized data collection method is containerized deployment, whether for data collection, processing, or external access, these services are implemented in the containers of the DPU's system-on-chip.
It will be appreciated that the number of containers is not limited, and that all functions of the DPU-based containerized data acquisition system may be implemented in one container, or that each function or sub-function of the DPU-based containerized data acquisition system may be implemented in a different container, respectively, which may further avoid the occurrence of interference between the processing of the different functions. The setting can be specifically performed according to the actual application situation.
It should be noted that the standard format data mentioned in the present application is not limited to a single unified format, and may be provided in a plurality of standard formats according to different types of data for which access is provided. Meanwhile, the initially collected service data and the standard format data may or may not be in one-to-one relationship. In the data processing process, the initially collected service data can be filtered and the like, at the moment, a part of the service data is deleted, and then the format conversion processing is carried out on the reserved service data, so that one-to-one standard format data of the reserved service data are obtained.
In step 100, the period of collecting each service data in the container of the system-on-chip of the DPU may be user-defined, and different collection periods may be specified according to the source or type of the service data. For example, the software data on the host side may be collected in real time, while the hardware data on the DPU side may be collected in units of hours, days, or the like.
Step 200: storing each of the standard format data in a container of the system-on-chip to enable an external system to access the standard format data from the container.
As can be seen from the above description, the method for collecting containerized data based on a DPU provided by the embodiments of the present application designs a method for collecting data in a system on a chip that can independently run in the DPU with containerization technology, without performing pile insertion and point embedding in an application program, so as to effectively improve the universality and reusability of the data collecting method, and effectively improve the comprehensiveness and reliability of service data collection in a host side and the DPU.
In order to further improve the comprehensiveness and reliability of the DPU-based containerized data acquisition, in the method for acquiring containerized data based on a DPU provided in the embodiment of the present application, referring to fig. 2, step 100 in the method for acquiring containerized data based on a DPU specifically includes the following:
step 110: collecting software data of a host side and a DPU, the software data comprising: operating system data;
and, step 120: and acquiring software and hardware data of the DPU in a self-defined manner, wherein the software and hardware data comprise: openvswitch soft switches offload data, network state data, and store offload state data.
It is to be appreciated that the operating system data can refer to: operating system related version, CPU, memory, network, disk, IO, TCP connection number, etc.
The OpenvSwitch soft switch offload data may specifically refer to: statistics of open virtual switching standard (open vswitch) ports, processing packets of virtual interfaces, and data flows on the DPU.
The network status data may specifically refer to: statistics of the network protocol processor (network processor).
The storing unloading state data may specifically refer to: the DPU stores configuration information, read-write rate, disk capacity information and the like related to unloading.
Among the above data, the hardware data belonging to the DPU specifically includes NP hardware forwarding statistics data offloaded from the OpenVswitch soft switch to the NP, and the software data belonging to the DPU specifically includes: openvswitch virtual port statistics data, issued flow rule data, SOC end operating system basic information and other data.
In order to further improve the effectiveness and the applicability of data processing in the process of collecting the containerized data based on the DPU, in the method for collecting the containerized data based on the DPU provided by the embodiment of the application, referring to fig. 2, step 100 of the method for collecting the containerized data based on the DPU further specifically comprises the following contents
Step 130: and aggregating each currently acquired service data in a container of the system-on-chip of the DPU.
Step 140: filtering the aggregated service data based on a preset filtering mode, wherein the filtering mode comprises the following steps: deduplication and/or filtering by preset labels.
In step 140, the aggregated service data may be deduplicated according to a certain rule. In addition, the service data can be filtered according to the labels, for example, the service data conforming to the preset labels is deleted or reserved. In one example, the rule may be to filter the log with the report target as the DPU-soc end, the tag may be set to "[ offlow ]", and filter the log record of the hardware offload flag.
Step 150: and carrying out format conversion on the filtered service data according to a preset standard format to obtain corresponding standard format data.
In step 150, format conversion of the filtered service data may specifically be conversion of a data format or a data system.
In order to further improve the effectiveness and applicability of externally provided data in the process of collecting the containerized data based on the DPU, in the method for collecting the containerized data based on the DPU provided by the embodiment of the application, referring to fig. 2, step 200 of the method for collecting the containerized data based on the DPU specifically includes the following contents:
Step 210: according to the data types of the standard format data, storing the standard format data in a container of the system-on-chip respectively so that an external system can access the standard format data with different data types respectively; wherein the data types include: log, telemetry data, and link tracking data; the external system includes: an external log system for accessing the log, a telemetry monitoring and alert system for accessing the telemetry data, and a link tracking and performance monitoring system for accessing the link tracking data.
The present application also provides a system for collecting data based on the container of the DPU in all or part of the method for collecting data based on the container of the DPU, referring to fig. 3, the system for collecting data based on the container of the DPU specifically includes the following contents:
the data collection and processing module 10 is configured to collect each service data in a container of a system on chip of the DPU, and perform data processing on each service data to obtain standard format data corresponding to each service data, where each service data includes: software data on the host side and/or software and hardware data of the DPU.
A data output module 20 for storing each of the standard format data in a container of the system-on-chip to enable an external system to access the standard format data from the container.
The embodiment of the DPU-based containerized data acquisition system provided by the application can be particularly used for executing the processing flow of the embodiment of the DPU-based containerized data acquisition method in the embodiment, and reference can be made to the detailed description of the embodiment of the DPU-based containerized data acquisition method.
As can be seen from the above description, the containerized data acquisition system based on the DPU provided by the embodiments of the present application designs a data acquisition method in a system on a chip capable of running in the DPU independently by using a containerization technology, without performing pile insertion and point embedding in an application program, so that the universality and reusability of the data acquisition method can be effectively improved, and the comprehensiveness and reliability of service data acquisition in the host side and the DPU can be effectively improved.
In order to further improve the comprehensiveness and reliability of the DPU-based containerized data acquisition system, referring to fig. 4, in the DPU-based containerized data acquisition system provided by the embodiment of the present application, the data acquisition and processing module 10 in the DPU-based containerized data acquisition system specifically includes the following contents:
A general-purpose operating system collector 11 for collecting software data of the host side and the DPU, the software data comprising: operating system data;
the DPU custom collector 12 is configured to collect software and hardware data of the DPU in a custom manner, where the software and hardware data includes: openvswitch soft switch offload data, network state data, and store offload state data;
wherein the DPU custom collector 12 comprises:
an OVS data collector 121 for collecting OpenvSwitch soft switch offload data of the DPU, an NP data collector 122 for collecting network state data of the DPU, and an NVME storage half offload data collector 123 for collecting storage offload state data of the DPU.
In order to further improve the effectiveness and the applicability of data processing in the process of collecting the containerized data based on the DPU, in the containerized data collecting system based on the DPU provided by the embodiment of the application, referring to fig. 4, the data collecting and processing module 10 of the containerized data collecting system based on the DPU further specifically includes the following contents:
a data processing unit 13, configured to aggregate each currently acquired service data in a container of the system-on-chip of the DPU; filtering the aggregated service data based on a preset filtering mode, wherein the filtering mode comprises the following steps: removing duplication and/or filtering according to a preset label; and carrying out format conversion on the filtered service data according to a preset standard format to obtain corresponding standard format data.
In order to further improve the effectiveness and applicability of externally provided data in the process of collecting the containerized data based on the DPU, in the containerized data collecting system based on the DPU provided by the embodiment of the application, referring to fig. 4, the data output module 20 of the containerized data collecting system based on the DPU specifically includes the following contents:
a data provider 21, configured to store each of the standard format data in a container of the system-on-chip according to a data type to which each of the standard format data belongs, so that the external system 3 accesses each of the standard format data of a different data type; wherein the data types include: log, telemetry data, and link tracking data;
wherein the data provider 21 comprises: a log provider 211 for storing a log, a telemetry data provider 212 for storing the telemetry data, and a link data trace provider 213 for storing the link trace data;
the external system 3 includes: an external log system 31 for the log provider to access the log, a telemetry monitor alarm system 32 for the telemetry data provider to access the telemetry data, and a link tracking and performance monitoring system 33 for the link tracking provider to access the link tracking data.
In order to further illustrate the DPU-based containerized data acquisition method implemented by the DPU-based containerized data acquisition system, the application also provides a specific application example of the DPU-based containerized data acquisition system.
Referring to fig. 5, the DPU-based containerized data acquisition system is divided into 4 parts, namely a DPU acquisition component, a data processing component, a data output component and an external system data receiving component in sequence from left to right. The DPU-based containerized data acquisition system is based on an open source standard, comprises a standard system data acquisition unit and a self-defined DPU service function data acquisition unit, and mainly comprises an OVS data acquisition unit, an NP data acquisition unit, an nvme-spdk storage semi-unloading data acquisition unit and the like.
In this application embodiment, the general operating system collector 11 may be implemented by a node-exporter operating system collector, which is a general collector. The node-exporter operating system collector is a general-purpose data collector for hardware and operating system Metrics data collection.
The OVS data collector 121 may be abbreviated as an OVS collector, which is an abbreviation of open vswitch, specifically refers to an open source virtual switching technology.
The NP data collector 122 may be abbreviated as NP collector, NP being an abbreviation of network processor, specifically referring to a network protocol processor.
The NVME storage half offload data collector 123 may be abbreviated as NVME-spdk collector, NVME (Non-Volatile Memory Express) is a high performance, low latency storage access protocol, SPDK (Storage Performance Development Kit) is an open source software development kit for building high performance storage applications. It provides a series of libraries and modules for developing storage applications, drivers and tools to achieve optimal storage performance and efficiency.
The external log system 31 may be implemented using ELK, which is an abbreviation of Elasticsearch, logstash and Kibana 3 words, or EFK, which is an abbreviation of Elasticsearch, fluentd and Kibana 3 words, which is a distributed search and analysis engine, a logstar source data ingest tool, and Kibana, which is a data visualization and mining tool, for reviewing logs and events. Fluentd is an open-source general log collection and distribution system.
The telemetry monitoring and alert system 32 may be implemented using Prometheus or Grafana, which is an open source service monitoring system and time series database, and Grafana is a telemetry visualization tool.
The link tracking and performance monitoring system 33 may be implemented using SkyWalking, an application performance monitoring tool for a distributed system, or APM; APM (application performance monitoring) is an application performance monitoring tool.
Based on the above, the method for collecting the containerized data based on the DPU, which is implemented by the containerized data collecting system based on the DPU provided by the application example of the present application, specifically includes the following contents:
step 1, data acquisition: the DPU acquisition plug-in acquires data to be acquired from a service component or an operating system of the DPU
DPU service components can be divided into node-exporter operating system data collection (responsible for basic collection of operating system related version, CPU (central processing unit), memory, network, disk, IO, TCP connection number and the like), OVS collector (responsible for statistics information of Openvswitch virtual port, processing packet of virtual interface and data flow on DPU), NP collector (responsible for collection of network processor statistics data), nvme-spdk collector (responsible for collection, storage and unloading related configuration information, read-write rate, disk capacity information and the like) according to service data types. The others are DPU custom collectors.
And 2, data processing: the data processing component pulls the data collected by various collectors from the collectors in the step 1, telemeters data indexes, operation logs, link tracking information and other business data, and further processes the data.
The data processing comprises 4 small steps of data aggregation, filtering, conversion and output, wherein the data collected by different collectors is aggregated, filtered and converted according to a certain rule, then output in a uniform format is provided to the outside, and the data is butted to an external system in a data provider mode. The filtering rules include de-duplication, filtering according to tag label, etc., and the conversion of data can be the conversion of data format and data system.
Step 3, data output: the output components are divided into a log provider (log provider), a telemetry data provider (telemetry provider), and a link data trace provider (trace provider) according to the data format and data type of the output.
Step 4, interfacing an external system: the data provider in step 3 interfaces with an external log system, a telemetry monitoring alarm system and a link tracking and performance monitoring system, respectively.
On the basis of the above-mentioned DPU-based containerized data acquisition system, the present application further provides a deployment method for the DPU-based containerized data acquisition system, referring to fig. 6, where the deployment method for the DPU-based containerized data acquisition system specifically includes the following that may be executed before step 100:
step 010: and setting a code warehouse corresponding to the DPU-based containerized data acquisition system in a source code management platform.
Step 020: and if a trigger instruction aiming at the code warehouse is received, compiling and packaging the target code based on the source code management platform to generate a corresponding installation package or mirror image.
It may be appreciated that the trigger instruction may include: code pushing, labeling (TAG), etc.
Step 030: and sending the installation package to a system on chip of the DPU with the container running environment installed in advance, and installing and containerizing the installation package in the system on chip to form the containerized data acquisition system based on the DPU in the system on chip.
In order to further improve the efficiency and the applicability of the deployment method of the DPU-based containerized data acquisition system, in the embodiment of the deployment method of the DPU-based containerized data acquisition system provided by the application, referring to fig. 7, step 020 in the deployment method of the DPU-based containerized data acquisition system specifically includes the following contents:
step 021: and if the target code is in an offline installation mode, compiling and packaging the target code in an OBS mode to generate a corresponding offline installation package.
And, step 022: if the target code is in an online installation mode, compiling the target code in a container mirror image construction mode, and pushing the compiled container mirror image to a mirror image warehouse for pulling during online installation.
The deployment method provides an effective means for providing software installation and management for the DPU to realize unloading and energization, and particularly, the application provides a containerized data acquisition system and deployment method for the DPU platform based on a DPU software and hardware development platform and container technology, and provides data acquisition and aggregation service for telemetry, log and link tracking functions of other software and hardware on the DPU platform. The data acquisition service is used as a basic software service of the DPU platform and provides data collection, processing and reporting functions for most of software on the DPU. The application mainly solves the problem of providing a whole set of software flow from source code development, compiling, packing, installation and deployment to operation and monitoring for the DPU software platform. The development and deployment modes of cross-platform and cross-system are provided for the software development, packaging and running of the DPU. Based on the containerization technology, a data acquisition, log collection and link tracking system is provided for the DPU, and the problems of complex development and access, difficult transplantation, much deployment dependence, low reusability and the like of the traditional data acquisition system are solved.
In terms of software, the present application further provides a deployment device for executing the DPU-based containerized data acquisition system in all or part of the deployment method of the DPU-based containerized data acquisition system, referring to fig. 8, the deployment device for the DPU-based containerized data acquisition system specifically includes the following contents:
And the source code management module 01 is used for setting a code warehouse corresponding to the DPU-based containerized data acquisition system in a source code management platform.
And the installation package generating module 02 is used for compiling and packaging the target code based on the source code management platform to generate a corresponding installation package or mirror image if a trigger instruction for the code warehouse is received.
And the containerized deployment module 03 is used for sending the installation package to a system on chip of the DPU with the container running environment in advance, and installing and containerized deploying the installation package in the system on chip so as to form the containerized data acquisition system based on the DPU in the system on chip.
The embodiment of the deployment device of the DPU-based containerized data acquisition system provided by the application can be particularly used for executing the processing flow of the embodiment of the deployment method of the DPU-based containerized data acquisition system in the embodiment, and the functions of the embodiment of the deployment method of the DPU-based containerized data acquisition system are not described herein, and reference can be made to the detailed description of the embodiment of the deployment method of the DPU-based containerized data acquisition system.
The deployment device of the DPU-based containerized data acquisition system can perform the deployment of the DPU-based containerized data acquisition system in a server or in a client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The application is not limited in this regard. If all operations are done in the client device, the client device may further comprise a processor for specific handling of the deployment of the DPU-based containerized data acquisition system.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Any suitable network protocol may be used between the server and the client device, including those not yet developed on the filing date of the present application. The network protocols may include, for example, TCP/IP protocol, UDP/IP protocol, HTTP protocol, HTTPS protocol, etc. Of course, the network protocol may also include, for example, RPC protocol (Remote Procedure Call Protocol ), REST protocol (Representational State Transfer, representational state transfer protocol), etc. used above the above-described protocol.
The embodiment of the application also provides an electronic device, which may include a processor, a memory, a receiver and a transmitter, where the processor is configured to execute the deployment method of the DPU-based containerized data acquisition system mentioned in the foregoing embodiment, and the processor and the memory may be connected by a bus or other manners, for example, by a bus connection. The receiver may be connected to the processor, memory, by wire or wirelessly.
The processor may be a central processing unit (Central Processing Unit, CPU). The processor may also be any other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof.
The memory, as a non-transitory computer readable storage medium, may be used to store a non-transitory software program, a non-transitory computer executable program, and a module, such as program instructions/modules corresponding to a deployment method of the DPU-based containerized data acquisition system in the embodiment of the present application. The processor executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory, i.e., the deployment method of the DPU-based containerized data acquisition system in the method embodiment described above is implemented.
The memory may include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created by the processor, etc. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory that, when executed by the processor, perform the method of deploying the DPU-based containerized data acquisition system of the embodiments.
In some embodiments of the present application, a user equipment may include a processor, a memory, and a transceiver unit, which may include a receiver and a transmitter, the processor, the memory, the receiver, and the transmitter may be connected by a bus system, the memory being configured to store computer instructions, the processor being configured to execute the computer instructions stored in the memory to control the transceiver unit to transmit and receive signals.
As an implementation manner, the functions of the receiver and the transmitter in the present application may be considered to be implemented by a transceiver circuit or a dedicated chip for transceiver, and the processor may be considered to be implemented by a dedicated processing chip, a processing circuit or a general-purpose chip.
As another implementation manner, a manner of using a general-purpose computer may be considered to implement the server provided by the embodiment of the present application. I.e. program code for implementing the functions of the processor, the receiver and the transmitter are stored in the memory, and the general purpose processor implements the functions of the processor, the receiver and the transmitter by executing the code in the memory.
The embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the aforementioned method of deploying a DPU-based containerized data acquisition system. The computer readable storage medium may be a tangible storage medium such as Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disk, a removable memory disk, a CD-ROM, or any other form of storage medium known in the art.
In order to further explain the deployment method of the DPU-based containerized data acquisition system mentioned in the foregoing embodiment, the present application further provides a specific application example of the deployment method of the DPU-based containerized data acquisition system, referring to fig. 9, where the deployment method of the DPU-based containerized data acquisition system specifically includes the following contents:
the DPU acquisition system code test warehouse is hosted on a private GitLab source code management platform, is matched with a Jenkins CI/CD tool to trigger the compiling, packaging and image pushing pipeline of codes, is compiled and packaged aiming at different platforms (ubuntu, centos, openEuler), and is pushed to the Harbor image warehouse. The entire packaging system is divided into two categories: an OBS packaging mode aiming at offline deployment and a Docker container packaging mode aiming at online deployment. OBS (open build service) is a packaging method of an rpm installation kit. And (3) adopting OBS packaging for the offline installation package, and respectively generating rpm installation packages of corresponding systems for the DPU-host and the DPU-soc operating system. Dockerfile compilation and packaging are employed for online container installation packages, using Harbor storage container mirroring. Wherein, harbor is a private mirror management platform.
Whether the package is installed offline or online, the installation and deployment of the whole telemetry functional component is completed at the DPU-soc end through a database-component.yml file of a telemet. The whole deployment of the DPU acquisition system can be completed by only installing a Docker container running environment at the DPU-soc end and matching with a Docker-composition.yml container deployment description file through a Docker-composition command, and no other system dependence exists. For devices or environments which cannot be networked, the offline package is supported to be deployed after the image file is imported.
The TeleMetry dock-composition. Yml file in FIG. 9 is a deployed format description file, the contents of which are shown in Table 1.
TABLE 1
The following is explained with respect to table 1:
the deployment service names are node-exporter and hados-exporter;
the mirror addresses used by the service are image: harbor. Yudur. Tech/test/node-exporter: 1.5.0 and image: harbor. Yudur. Tech/test/hados-exporter: 1.0.0, respectively;
the adopted network mode is a host mode;
the name of the collection container service is hados-exporter;
the collector components are collector. Np, collector. Ovs and collector. Ovsddk, and the volume section designates that the file path of the host is mounted in the container for the container to use;
The 9101 port of http is exposed externally for external access.
It can be understood that the deployment mode of the DPU containerized data acquisition system based on the Docker and the Docker-compound provided by the application can be replaced by a static container deployment mode based on kubelet by pod or a binary deployment based on a host, etc., and the containerized data acquisition system based on the DPU and the deployment method provided by the application example of the application provide a general method for software development and deployment on the DPU, thereby simplifying the development flow and the deployment link of application software on the DPU.
Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein can be implemented as hardware, software, or a combination of both. The particular implementation is hardware or software dependent on the specific application of the solution and the design constraints. 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. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.
In the present application, features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, and various modifications and variations can be made to the embodiments of the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method for collecting containerized data based on a DPU, comprising:
collecting each service data in a container of a system on chip of the DPU, and carrying out data processing on each service data to obtain standard format data corresponding to each service data, wherein each service data comprises: software data of a host side and/or software and hardware data of the DPU;
storing each of the standard format data in a container of the system-on-chip to enable an external system to access the standard format data from the container.
2. The DPU-based containerized data collection method of claim 1, wherein the collecting the respective business data in the container of the system-on-chip of the DPU comprises:
collecting software data of a host side and a DPU, the software data comprising: operating system data;
and, self-defining and collecting software and hardware data of the DPU, wherein the software and hardware data comprise: openvswitch soft switches offload data, network state data, and store offload state data.
3. The DPU-based containerized data collection method of claim 1, wherein the data processing of each of the service data to obtain standard format data corresponding to each of the service data comprises:
The method comprises the steps that aggregation processing is carried out on each currently acquired service data in a container of a system-on-chip of a DPU;
filtering the aggregated service data based on a preset filtering mode, wherein the filtering mode comprises the following steps: removing duplication and/or filtering according to a preset label;
and carrying out format conversion on the filtered service data according to a preset standard format to obtain corresponding standard format data.
4. A DPU-based containerized data collection method according to any one of claims 1 to 3, wherein said storing each of said standard format data in a container of said system-on-chip to enable an external system to access said standard format data from the container comprises:
according to the data types of the standard format data, storing the standard format data in a container of the system-on-chip respectively so that an external system can access the standard format data with different data types respectively;
wherein the data types include: log, telemetry data, and link tracking data;
the external system includes: an external log system for accessing the log, a telemetry monitoring and alert system for accessing the telemetry data, and a link tracking and performance monitoring system for accessing the link tracking data.
5. A DPU-based containerized data acquisition system, comprising:
the data acquisition and processing module is used for acquiring each service data in a container of the system on chip of the DPU and processing the data of each service data to obtain standard format data corresponding to each service data, wherein each service data comprises: software data of a host side and/or software and hardware data of the DPU;
and the data output module is used for storing each standard format data in a container of the system-on-chip so as to enable an external system to access the standard format data from the container.
6. The DPU-based containerized data acquisition system of claim 5, wherein the data acquisition and processing module comprises:
a general-purpose operating system collector for collecting software data of a host side and a DPU, the software data comprising: operating system data;
the DPU custom collector is used for custom collecting software and hardware data of the DPU, wherein the software and hardware data comprise: openvswitch soft switch offload data, network state data, and store offload state data;
wherein, DPU custom collector includes:
An OVS data collector for collecting OpenvSwtch soft switch unloading data of the DPU, an NP data collector for collecting network state data of the DPU and an NVME storage half-unloading data collector for collecting storage unloading state data of the DPU.
7. The DPU-based containerized data acquisition system of claim 5, wherein the data acquisition and processing module comprises:
the data processing unit is used for carrying out aggregation processing on each currently acquired service data in a container of the system-on-chip of the DPU; filtering the aggregated service data based on a preset filtering mode, wherein the filtering mode comprises the following steps: removing duplication and/or filtering according to a preset label; and carrying out format conversion on the filtered service data according to a preset standard format to obtain corresponding standard format data.
8. The DPU-based containerized data acquisition system of claim 5, wherein the data output module comprises:
the data provider is used for respectively storing the standard format data in the containers of the system-on-chip according to the data types of the standard format data so as to enable an external system to respectively access the standard format data with different data types; wherein the data types include: log, telemetry data, and link tracking data;
Wherein the data provider comprises: a log provider for storing a log, a telemetry data provider for storing the telemetry data, and a link data trace provider for storing the link trace data;
the external system includes: an external log system for the log provider to access the log, a telemetry monitor alarm system for the telemetry data provider to access the telemetry data, and a link tracking and performance monitoring system for the link data tracking provider to access the link tracking data.
9. A method of deploying a DPU-based containerized data acquisition system as recited in any one of claims 5 to 8, comprising:
setting a code warehouse corresponding to the DPU-based containerized data acquisition system in a source code management platform;
if a trigger instruction aiming at the code warehouse is received, compiling and packaging target codes based on the source code management platform to generate corresponding installation packages or images;
and sending the installation package to a system on chip of the DPU with the container running environment installed in advance, and installing and containerizing the installation package in the system on chip to form the containerized data acquisition system based on the DPU in the system on chip.
10. The method for deploying a DPU-based containerized data acquisition system of claim 9, wherein compiling and packaging object code based on the source code management platform to generate a corresponding installation package or image comprises:
if the target code is in an offline installation mode, compiling and packaging the target code in an OBS mode to generate a corresponding offline installation package;
if the target code is in an online installation mode, compiling the target code in a container mirror image construction mode, and pushing the compiled container mirror image to a mirror image warehouse for pulling during online installation.
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