CN112394945A - System verification method for complex edge calculation - Google Patents
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Abstract
A system verification method facing complex edge calculation comprises the following steps: after the development of the service is completed, a developer needs to construct a service model and declare the characteristics supported by the service; the method comprises the following steps that equipment information is collected in an initialization stage when equipment runs and reported to a cloud end, and the cloud end collects and constructs an equipment model; the method comprises the steps that a service list is extracted from a system customization list by a customization platform rear end, and combined with a service model and an equipment model of equipment to be deployed for joint pre-verification, wherein the verification comprises CPU architecture verification, resource verification, service dependency relationship verification and hardware drive verification; and if the success is verified, pushing the service to the edge system for deployment and operation, and if the failure is verified, feeding back the reason of the failure of the user and giving corresponding guidance. The invention realizes pre-check before the operation of the edge customization system by constructing the service model and the equipment model, detects the fault in advance and gives corresponding guidance. The robustness of the customization system is greatly improved, and the Debug time of customization personnel is reduced.
Description
Technical Field
The invention provides a customization system joint verification method based on a service model and an equipment model. The invention can quickly realize the pre-check of the customized system in the complex edge scenes of various services and various heterogeneous devices, and discover various faults possibly occurring in the operation stage of the system in advance.
Background
The appearance of cloud computing provides an efficient management and computing platform for big data, but the current network bandwidth increase speed is far beyond the increase speed of data, and a long transmission link brings higher delay and potential safety hazard. In order to make up for the above deficiency of cloud computing, edge computing arises. In the edge computing model, the computing resources are closer to the data source, and the network edge device already has enough computing power to implement local processing of the source data and send the result to the cloud computing center. Therefore, the bandwidth pressure in the network transmission process can be reduced, the data analysis and processing are accelerated, and meanwhile, the risk of privacy disclosure of the terminal sensitive data information can be reduced.
The edge is used as an extension of the cloud, and can provide various capabilities such as equipment access, data processing, AI intelligence and the like. System customization has become one of the mainstream trends in edge computing. Mainstream cloud computing vendors have successively introduced respective Edge customization platforms to provide functions such as hot deployment and hot update of Edge services, for example, AWS IoT greenras, Azure IoT Edge, and ali LinkEdge.
The mainstream edge platform needs a user to install an edge runtime on the edge device to receive a cloud instruction. A user maintains a system customization list at the cloud end in a visual or programming mode, information such as services to be deployed and service configuration is declared, and operation and maintenance instructions are sent to edge operation to complete system updating after the system calculates increment.
However, due to the complexity of the edge scenario, the customization system may fail during the run-time phase: if the deployment service only supports the x86 architecture, but the device is an arm architecture, the service cannot be started normally due to the mismatching of the CPU architecture, and an exec format error is reported; the service needs 1 core CPU, but the system only has 0.5 core CPU idle, and the system resource is not enough to cause abnormal service operation or resource seizing with other services; the visualization service relies on the database service to provide data, but the data visualization is abnormal because the database service is not deployed; the Bluetooth device needs to use a Bluetooth driver of the device for accessing the service, but the device does not install the corresponding driver, so that the service starting is abnormal.
The existing customized platform only provides a basic service life cycle monitoring function and does not have a set of good fault discovery mechanism. Since the fault does not necessarily directly cause the service crash, the user may find the service problem through the service exception after the service runs for a long time, resulting in a long fault discovery period. In addition, a user needs to log in the device by himself when positioning a problem, and the problem positioning time is long due to the fact that the user is complicated by means of logging checking and the like. More seriously, the wrong customization may affect other services that the system is running, causing immeasurable losses.
Therefore, it is desirable to pre-verify the customized system during the system customization stage, detect possible faults of the system during the operation stage in advance, and feedback the user to make corresponding improvements.
Disclosure of Invention
The present invention overcomes the above-mentioned shortcomings of the prior art and provides a method for pre-verifying a customized system.
In order to realize the purpose, the technical scheme adopted by the invention is as follows: a customized system joint verification method based on a service model and an equipment model comprises the following steps:
(1) constructing a service model; after packing services into a Docker mirror image in a service development stage, a developer needs to construct a service model and declare the characteristics of the services, and the method specifically comprises the following steps:
1.1) name, namely service name, the data type is a character string, the name is the only identification of the service and consists of lower case letters, numbers and middle drawn lines, and the initial letter must be a lower case letter;
1.2) architecture, namely a CPU architecture supported by service, wherein the data type is a character string array, and the array element is a CPU architecture identifier; if the service supports a plurality of CPU architectures, Docker mirror images corresponding to the architectures need to be constructed, and a manifest is compiled to assemble different mirror images into a mirror image supporting the multi-CPU architecture;
1.3) resource, namely service resource occupation, which represents the resource minimally needed by the service, and the data type is JSON and comprises four items of cpu, memory, storage and gpu, wherein: CPU represents CPU resources occupied by the service, and the data type is a floating point number; the memory represents a memory occupied by the service, and the data type is an integer; storage represents a disk space occupied by the service, and the data type is an integer; GPU represents the number of GPUs occupied by the service, and the data type is an integer;
1.4) dependency, namely other services on which the service depends, wherein the data type is a character string array, and the array element is the name of the other services;
1.5) device, namely equipment hardware driver dependent on service, wherein the data type is a character string array, and the array element is equipment hardware driver identification;
(2) constructing an equipment model; the method comprises the following steps that the construction of an equipment model is divided into two stages, the first stage generates an access certificate of the equipment when a user registers the equipment, the second stage acquires equipment information when the equipment is initialized during running and reports the equipment information to a cloud end, and the cloud end collects the equipment information to generate a complete equipment model; the device model describes the features of the device including the following aspects:
2.1) deviceKey, namely equipment identification, wherein the data type is a character string and is the only identification of the equipment;
2.2) the product Key is a product identification, the data type is a character string, and the product to which the equipment belongs is represented;
2.3) deviceSecret, namely, the device access key, wherein the data type is a character string;
2.4) architecture, namely the CPU architecture of the device, the data type is a character string array, the array generally has only one element, the edge cluster formed by multiple devices may have computing heterogeneous devices, and the array should include all the CPU architectures of the cluster;
2.5) resource, namely equipment idle resource, if the equipment is a cluster, the equipment is the sum of resources of all the equipment in the cluster, the data type is JSON, the data type comprises four items of cpu, memory, storage and gpu, and the detail refers to a service model;
2.6) device, namely equipment hardware drive, wherein the data type is a character string array, and the details refer to a service model;
(3) a pre-calibration customization system; the method comprises the steps that the back end of a customization platform obtains a system customization list of a user, extracts a service list from the list, reads a corresponding service model from the system, performs joint pre-verification by combining with an equipment model of equipment to be deployed, verifies that the service is successfully pushed to the deployment and operation on an edge system, and feeds back the reason of user failure if the service fails, so as to give corresponding guidance; the verification specifically comprises the following four aspects:
3.1) checking the CPU architecture, extracting a supported CPU architecture list from the service model of each service, comparing the supported CPU architecture list with the CPU architecture list in the equipment model, and verifying whether an intersection exists; if not, the verification fails, and the user is reminded to replace the service with the proper framework;
3.2) checking resources, namely calculating the sum of all resources occupied by the selected service through the service model, comparing the sum with all idle resources in the equipment model, and judging whether the resources are enough; the calculation formula is as follows:
the four inequalities respectively calculate whether the CPU, the memory, the disk and the GPU resources of the equipment meet the requirements; where S is the set of services selected by the user, i represents a certain service, ci，mi，di，giRespectively representing the occupation of CPU, memory, disk and GPU resources of a certain service; cD，MD，KD，GDRespectively representing the sum of idle resources of a CPU, a memory, a disk and a GPU of the edge device; if the inequality is not established, the customized system resource does not meet the requirement, and the user is reminded to delete the service or replace the edge node with stronger performance;
3.3) checking service dependence, extracting a dependent service list from the service model of each service, checking whether the service in the list is in the service list, if not, the service does not meet the requirement, and reminding a user to deploy the responding dependent service;
and 3.4) verifying the equipment drivers, extracting a dependent equipment hardware driver list from the service model of each service, verifying whether the dependent equipment hardware driver list is contained in the hardware driver list of the equipment model, and reminding a user of adding the support of the corresponding driver to the equipment if the dependent equipment hardware driver list is not contained in the hardware driver list of the equipment model.
Compared with the prior art, the invention has the beneficial effects that: by constructing a service model and an equipment model, pre-verification is realized before the operation of the edge customization system, faults are detected in advance, and corresponding guidance is given. The robustness of the customization system is greatly improved, and the Debug time of customization personnel is reduced.
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FIG. 1 is a comparison of the method of the present invention with previous methods in troubleshooting.
Detailed Description
The invention provides a customization system joint calibration method based on a service model and an equipment model, which can quickly realize the pre-calibration of a customization system in complex edge scenes of various services and various heterogeneous equipment and detect possible faults of the system in an operation stage in advance. The specific implementation mode is as follows:
(1) constructing a service model; after a developer packs services into a Docker mirror image in a service development stage, a service model needs to be built, and service features are declared, which are shown as follows:
the service model specifically includes the following aspects:
1.1) name, namely service name, the data type is a character string, the name is the only identification of the service and consists of lower case letters, numbers and middle drawn lines, and the initial letter must be a lower case letter;
1.2) architecture, namely a CPU architecture supported by service, wherein the data type is a character string array, and the array element is a CPU architecture identifier; if the service supports a plurality of CPU architectures, Docker mirror images corresponding to the architectures need to be constructed, and a manifest is compiled to assemble different mirror images into a mirror image supporting the multi-CPU architecture;
1.3) resource, namely service resource occupation, which represents the resource minimally needed by the service, and the data type is JSON and comprises four items of cpu, memory, storage and gpu, wherein: CPU represents CPU resources occupied by the service, and the data type is a floating point number; the memory represents a memory occupied by the service, and the data type is an integer; storage represents a disk space occupied by the service, and the data type is an integer; GPU represents the number of GPUs occupied by the service, and the data type is an integer;
1.4) dependency, namely other services on which the service depends, wherein the data type is a character string array, and the array element is the name of the other services;
1.5) device, namely equipment hardware driver dependent on service, wherein the data type is a character string array, and the array element is equipment hardware driver identification;
(2) constructing an equipment model; the method comprises the following steps that the construction of an equipment model is divided into two stages, the first stage generates an access certificate of the equipment when a user registers the equipment, the second stage acquires equipment information when the equipment is initialized during running and reports the equipment information to a cloud end, and the cloud end collects the equipment information to generate a complete equipment model; the device model describes the characteristics of the device, examples of which are as follows:
the equipment model specifically includes the following aspects:
2.1) deviceKey, namely equipment identification, wherein the data type is a character string and is the only identification of the equipment;
2.2) the product Key is a product identification, the data type is a character string, and the product to which the equipment belongs is represented;
2.3) deviceSecret, namely, the device access key, wherein the data type is a character string;
2.4) architecture, namely the CPU architecture of the device, the data type is a character string array, the array generally has only one element, the edge cluster formed by multiple devices may have computing heterogeneous devices, and the array should include all the CPU architectures of the cluster;
2.5) resource, namely equipment idle resource, if the equipment is a cluster, the equipment is the sum of resources of all the equipment in the cluster, the data type is JSON, the data type comprises four items of cpu, memory, storage and gpu, and the detail refers to a service model;
2.6) device, namely equipment hardware drive, wherein the data type is a character string array, and the details refer to a service model;
(3) a pre-calibration customization system; the method comprises the steps that the back end of a customization platform obtains a system customization list of a user, extracts a service list from the list, reads a corresponding service model from the system, performs joint pre-verification by combining with an equipment model of equipment to be deployed, verifies that the service is successfully pushed to the deployment and operation on an edge system, and feeds back the reason of user failure if the service fails, so as to give corresponding guidance; the verification specifically comprises the following four aspects:
3.1) checking the CPU architecture, extracting a supported CPU architecture list from the service model of each service, comparing the supported CPU architecture list with the CPU architecture list in the equipment model, and verifying whether an intersection exists; if not, the verification fails, and the user is reminded to replace the service with the proper framework;
3.2) checking resources, namely calculating the sum of all resources occupied by the selected service through the service model, comparing the sum with all idle resources in the equipment model, and judging whether the resources are enough; the calculation formula is as follows:
the four inequalities respectively calculate whether the CPU, the memory, the disk and the GPU resources of the equipment meet the requirements; where S is the set of services selected by the user, i represents a certain service, ci，mi，di，giRespectively representing the occupation of CPU, memory, disk and GPU resources of a certain service; cD，MD，KD，GDRespectively representing the sum of idle resources of a CPU, a memory, a disk and a GPU of the edge device; if the inequality is not established, the customized system resource does not meet the requirement, and the user is reminded to delete the service or replace the edge node with stronger performance;
3.3) checking service dependence, extracting a dependent service list from the service model of each service, checking whether the service in the list is in the service list, if not, the service does not meet the requirement, and reminding a user to deploy the responding dependent service;
and 3.4) verifying the equipment drivers, extracting a dependent equipment hardware driver list from the service model of each service, verifying whether the dependent equipment hardware driver list is contained in the hardware driver list of the equipment model, and reminding a user of adding the support of the corresponding driver to the equipment if the dependent equipment hardware driver list is not contained in the hardware driver list of the equipment model.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.
Claims (1)
1. A customized system joint verification method facing complex edge calculation comprises the following steps:
(1) constructing a service model; after packing services into a Docker mirror image in a service development stage, a developer needs to construct a service model and declare the characteristics of the services, and the method specifically comprises the following steps:
1.1) name, namely service name, the data type is a character string, the name is the only identification of the service and consists of lower case letters, numbers and middle drawn lines, and the initial letter must be a lower case letter;
1.2) architecture, namely a CPU architecture supported by service, wherein the data type is a character string array, and the array element is a CPU architecture identifier; if the service supports a plurality of CPU architectures, Docker mirror images corresponding to the architectures need to be constructed, and a manifest is compiled to assemble different mirror images into a mirror image supporting the multi-CPU architecture;
1.3) resource, namely service resource occupation, which represents the resource minimally needed by the service, and the data type is JSON and comprises four items of cpu, memory, storage and gpu, wherein: CPU represents CPU resources occupied by the service, and the data type is a floating point number; the memory represents a memory occupied by the service, and the data type is an integer; storage represents a disk space occupied by the service, and the data type is an integer; GPU represents the number of GPUs occupied by the service, and the data type is an integer;
1.4) dependency, namely other services on which the service depends, wherein the data type is a character string array, and the array element is the name of the other services;
1.5) device, namely equipment hardware driver dependent on service, wherein the data type is a character string array, and the array element is equipment hardware driver identification;
(2) constructing an equipment model; the method comprises the following steps that the construction of an equipment model is divided into two stages, the first stage generates an access certificate of the equipment when a user registers the equipment, the second stage acquires equipment information when the equipment is initialized during running and reports the equipment information to a cloud end, and the cloud end collects the equipment information to generate a complete equipment model; the device model describes the features of the device including the following aspects:
2.1) deviceKey, namely equipment identification, wherein the data type is a character string and is the only identification of the equipment;
2.2) the product Key is a product identification, the data type is a character string, and the product to which the equipment belongs is represented;
2.3) deviceSecret, namely, the device access key, wherein the data type is a character string;
2.4) architecture, namely the CPU architecture of the device, the data type is a character string array, the array generally has only one element, the edge cluster formed by multiple devices may have computing heterogeneous devices, and the array should include all the CPU architectures of the cluster;
2.5) resource, namely equipment idle resource, if the equipment is a cluster, the equipment is the sum of resources of all the equipment in the cluster, the data type is JSON, the data type comprises four items of cpu, memory, storage and gpu, and the detail refers to a service model;
2.6) device, namely equipment hardware drive, wherein the data type is a character string array, and the details refer to a service model;
(3) a pre-calibration customization system; the method comprises the steps that the back end of a customization platform obtains a system customization list of a user, extracts a service list from the list, reads a corresponding service model from the system, performs joint pre-verification by combining with an equipment model of equipment to be deployed, verifies that the service is successfully pushed to the deployment and operation on an edge system, and feeds back the reason of user failure if the service fails, so as to give corresponding guidance; the verification specifically comprises the following four aspects:
3.1) checking the CPU architecture, extracting a supported CPU architecture list from the service model of each service, comparing the supported CPU architecture list with the CPU architecture list in the equipment model, and verifying whether an intersection exists; if not, the verification fails, and the user is reminded to replace the service with the proper framework;
3.2) checking resources, namely calculating the sum of all resources occupied by the selected service through the service model, comparing the sum with all idle resources in the equipment model, and judging whether the resources are enough; the calculation formula is as follows:
the four inequalities respectively calculate whether the CPU, the memory, the disk and the GPU resources of the equipment meet the requirements; where S is the set of services selected by the user, i represents a certain service, ci，mi，di，giRespectively representing the occupation of CPU, memory, disk and GPU resources of a certain service; cD，MD，KD，GDRespectively representing the sum of idle resources of a CPU, a memory, a disk and a GPU of the edge device; if the inequality is not established, the customized system resource does not meet the requirement, and the user is reminded to delete the service or replace the edge node with stronger performance;
3.3) checking service dependence, extracting a dependent service list from the service model of each service, checking whether the service in the list is in the service list, if not, the service does not meet the requirement, and reminding a user to deploy the responding dependent service;
and 3.4) verifying the equipment drivers, extracting a dependent equipment hardware driver list from the service model of each service, verifying whether the dependent equipment hardware driver list is contained in the hardware driver list of the equipment model, and reminding a user of adding the support of the corresponding driver to the equipment if the dependent equipment hardware driver list is not contained in the hardware driver list of the equipment model.
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| CN113342382A (en) * | 2021-06-30 | 2021-09-03 | 北京京东乾石科技有限公司 | Data verification method and system and edge terminal equipment |
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